UNIVERSITI TEKNOLOGI MALAYSIA
Transcription
UNIVERSITI TEKNOLOGI MALAYSIA
PSZ 19:16 (Pind. 1/13) UNIVERSITI TEKNOLOGI MALAYSIA DECLARATION OF THESIS / UNDERGRADUATE PROJECT PAPER Author’s full name : MUHAMMAD HAFIZ FIRDAUS BIN AZMI Date of birth : 26th MAY 1991 Title : SOUND ANALYSIS OF SWIFTLET ECHOLOCATION Academic Session: 2014/2015 I declare that this thesis is classified as: CONFIDENTIAL (Containing confidential information under the Official Secret Act 1972)* RESTRICTED (Containing restricted information as specified by organization where research was done)* OPEN ACCESS the I agree that my thesis to be published and accessed online (full text) I acknowledged that Universiti Teknologi Malaysia reserves the right as follows: 1. 2. The thesis is the property of Universiti Teknologi Malaysia. The Library of Universiti Teknologi Malaysia has the right to make copies for academic purposes. Certified by: SIGNATURE 910526-14-6155 NOTES : SIGNATURE OF SUPERVISOR DR. NOR HISHAM BIN HAJI KHAMIS NEW IC NO. / PASSPORT NO. NAME OF SUPERVISOR Date: 14TH JANUARY 2015 Date: 14TH JANUARY 2015 * If the thesis is CONFIDENTAL or RESTRICTED, please attach the letter from the organization concerned stating the reason/s and duration for confidentiality or restriction. “I hereby declare that I have read this thesis and in my opinion this thesis is sufficient in terms of scope and quality for the award of the degree of Bachelor of Engineering (Electrical-Telecommunication)” Signature : Name of Supervisor : DR. NOR HISHAM BIN HAJI KHAMIS Date 14TH JANUARY 2015 : SOUND ANALYSIS OF SWIFTLET ECHOLOCATION MUHAMMAD HAFIZ FIRDAUS BIN AZMI A report submitted in partial fulfillment of the requirements for the award of the degree of Bachelor of Engineering (Electrical-Telecommunication) Faculty of Electrical Engineering Universiti Teknologi Malaysia JANUARY 2015 ii I declare that this thesis entitled “Sound Analysis of Swiftlet Echolocation” is the result of my own research except as cited in the references. The thesis has not been accepted for any degree and is not concurrently submitted in candidature of any other degree. Signature : _______ Name of Candidate : MUHAMMAD HAFIZ FIRDAUS BIN AZMI Date : 14TH JANUARY 2015 iii Dedicated to my beloved parents and family for their encouragement, support and love. iv ACKNOWLEDGEMENT First and foremost, I am so grateful to Allah the Almighty for gracing me with strength, wisdom, and confident to complete this project. I owe my deepest gratitude to my parents. Without their encouragement and support, I would not have a chance to complete this project. I would like to express my heartily gratitude to my supervisor, Dr. Nor Hisham Bin Haji Khamis for the guidance and enthusiasm given throughout the progress of this project. His vast experience and deep understanding in this area proved to be immense help to me and also his profound point of view enlightened me in many ways. A special acknowledgement goes to the owner of swiftlet birdhouse, Mr Wan Zaidi (Skudai), Cikgu Tumiran (Pontian) and also Mr Zulkifli Ismail (Kota Tinggi) for allowed me to collect the swiftlet sample sound for the analysis. Without the sample the analysis of swiftlet sound cannot be done. Their comments and feasible advices help me to improve my work. Appreciation is also extended to my friends for their continuous help and moral support despite the hectic semester that we had to go through. v ABSTRACT This research is about the analysis of swiftlet sound, which is to get the information in swiftlet recording audio such as frequency and quality of the sound. Normally in swiftlet industry, sound system is the important device to attract swiftlet into the house. It will play swiftlet audio for a long period to lure them inside the house and nesting. The problems are there are only a few of birdhouses are successful; most does not attract the swiftlets and swiftlets are not nesting in the birdhouse. Major problem is the swiftlets sounds - whether it is the correct sound or not; or whether the recorder are playing the sound with the correct power and volume. Therefore, this study analyze the swiftlet sound feature to get the information inside swiftlet sound such as frequency and quality before it can be directly used. To conduct this research, the sound analysis for swiftlet sound and echolocation is performed by using signal processing techniques which are Fast Fourier Transform and Short Time Fourier Transform. It is also done to identify the frequency in the swiftlet sound sample and getting the frequency distribution for all swiftlet sample sounds. The results. The proposed high frequency distribution for swiftlet sample are ranging between 5 – 6 kHz for internal sound and 5.5 kHz – 6.5 kHz for the external sound. In addition, the amplitude of the internal sound should have a variation of high and low amplitude. Meanwhile, for the external sound, the high amplitude are along the time. vi ABSTRAK Kajian ini adalah mengenai analisis bunyi burung walit, iaitu untuk mendapatkan maklumat dalam rakaman audio burung walit, seperti frekuensi dan kualiti bunyi. Biasanya, dalam industri burung walit, sistem bunyi adalah alat yang penting untuk menarik burung walit masuk ke dalam rumah. Ia akan memainkan audio burung walit untuk tempoh yang lama untuk menarik burung walit tersebut ke dalam rumah dan membuat sarang. Masalah-masalah yang terdapat adalah, hanya beberapa buah rumah burung yang berjaya; tidak berjaya menarik burung walit untuk bersarang di dalam rumah burung. Manakala, masalah utama adalah bunyi burung walit- sama ada ia adalah bunyi yang betul atau tidak; dan perakam suara memainkan bunyi dengan kuasa serta kelantangan bunyi yang betul. Oleh itu, kajian ini menganalisis ciri-ciri bunyi burung walit untuk mendapatkan maklumat tentang bunyi burung walit seperti, frekuensi dan kualiti sebelum ia boleh digunakan secara terus. Bagi menjalankan kajian ini, analisis bunyi untuk bunyi burung walit dan echolocation dilakukan dengan menggunakan teknik pemprosesan isyarat iaitu Fourier Transform Fast dan Short Time Fourier Transform. Ia juga dilakukan untuk mengenal pasti frekuensi dalam sampel bunyi burung walit dan mendapat taburan frekuensi bagi semua sampel bunyi burung walit. Hasilnya, taburan frekuensi tinggi untuk sampel burung walit adalah di antara 5 - 6 kHz untuk bunyi dalaman dan 5.5 kHz - 6.5 kHz untuk bunyi luaran. Di samping itu, amplitud bunyi dalaman harus mempunyai perubahan amplitud tinggi dan rendah. Manakala untuk bunyi luaran, amplitud tinggi adalah sepanjang masa. vii TABLE OF CONTENTS CHAPTER 1 2 TITLE PAGE DECLARATION ii DEDICATION iii ACKNOWLEDGEMENT iv ABSTRACT v ABSTRAK vi TABLE OF CONTENTS vii LIST OF TABLES x LIST OF FIGURES xi LIST OF ABBREVIATIONS xv INTRODUCTION 1 1.1 Introduction 1 1.2 Problem Statement 2 1.3 Objective of Project 2 1.4 Scope of Project 3 1.5 Project Flow 3 1.6 Outline of thesis 4 LITERATURE REVIEW 5 2.1 Introduction 5 2.2 Swiftlet 5 2.2.1 Types of swiftlet 7 viii 2.2.2 The nutrition of swiftlet nest 8 2.2.3 Swiftlet House 9 2.2.3.1 House Characteristics 2.3 2.4 3 4 10 2.2.4 Echolocation in Swiftlet 13 Sound analysis of swiftlet sound 15 2.3.1 Fast Fourier Transform 16 2.3.2 Folding FFT spectrum 17 2.3.3 Short Time Fourier Transform 18 Visualization of sound 20 2.4.1 Oscillogram 20 2.4.2 Sonogram 21 2.4.3 Power Spectrogram 22 METHODOLOGY 23 3.1 Introduction 23 3.2 Analysis of swiftlet sound 24 3.3 Swiftlet sound sample 24 3.3.1 Types of swiftlet sound 25 3.3.2 List of swiftlet sound 25 3.4 Sound analysis development 28 3.5 Software development and visualization 30 3.6 Simulation tools 32 THE DEVELOPMENT AND SIMULATION OF SWIFTLET SOUND 33 4.1 Introduction 33 4.2 Development of swiftlet sounds: 34 Determine the frequency range and the high frequency out from the swiftlet sounds 4.2.1 Procedures 34 ix 4.3 Simulation of swiftlet sounds 35 4.3.1 Simulation of time domain graph 35 4.3.2 Simulation of Fast Fourier 36 Transform 4.3.3 Simulation of Short Time Fourier 39 Transform 5 RESULT AND DISCUSSION 42 5.1 Introduction 42 5.2 Result from analysis of swiftlet sounds 43 5.3 Results from Oscillogram 43 5.3.1 Internal swiftlet sound 44 5.3.2 External swiftlet sound 46 Results from Power Spectrogram 49 5.4.1 Internal Swiftlet Sound 49 5.4.2 External Swiftlet Sound 53 Results from Sonogram 55 5.4 5.5 6 7 REFERENCES CONCLUSION 57 6.1 Conclusion 57 6.2 Recommendation and Future Work 58 6.3 Summary 58 PROJECT MANAGEMENT 59 7.1 Introduction 59 7.2 Project Schedule 59 61 x LIST OF TABLES TABLE NO TITLE PAGE 2.1 The differences between Swiftlet and Swallow 6 2.1 The types of swiftlets can be found in Malaysia 7 with specification 2.3 The various conditions related to illuminance 11 3.1 List swiftlet sound samples for (a) external and (b) internal 27 5.1 The distribution high peak frequencies and frequency range of internal swiftlet sounds for Pontian Birdhouse 49 5.2 The distribution high peak frequencies and frequency range of internal swiftlet sounds for Skudai Birdhouse 51 5.3 The distribution high peak frequencies and frequency range of internal swiftlet sounds for Kota Tinggi Birdhouse 52 5.4 The distribution high peak frequencies and frequency range of external swiftlet sounds for Pontian Birdhouse 53 5.5 The distribution high peak frequencies and frequency range of external swiftlet sounds for Skudai Birdhouse 54 5.6 The distribution high peak frequencies and frequency range of external swiftlet sounds for Kota Tinggi Birdhouse Work schedule for FYP 2 54 7.1 60 xi LIST OF FIGURES FIGURE NO. TITLE PAGES 1.1 Project flow 4 2.1 The swiftlet nest produced by 6 (a) Aerodramus Maximus (b) Aerodramus Fuciphagus 2.2 The swiftlet house 9 2.3 The air ventilation hole mounted with KNI pipe 10 2.4 ThermoHygrometer gauge is used to measure 10 moisture/humidity level in swiftlet room. 2.5 The tweeters are mounted at the corners 12 2.6 Piezo tweeter suitable to use for external building 12 2.7 Puller locate at the rooftop of swiftlets house 13 2.8 South American Oilbirds 14 2.9 Bats use echolocation to locate their prey 15 2.10 Fast Fourier Transform (FFT) 16 2.11 FFT Double Sided Spectrum 17 2.12 FFT Single Sided Spectrum 18 2.13 The equation for Short Time Fourier Transform 18 2.14 STFT will extract into frame from the information 19 of sinusoidal signal in (a) by using window technique to identify into frequency and time in (b). 2.15 The visualization or graphic representation of sound 20 xii 2.16 A sound is represent in Oscillogram 21 2.17 Example of Sonogram from Seaside Sparrow sound 22 2.18 Power Spectrogram of the Blackbird 22 (Turdus merula) sound 3.1 The flying path of swiftlet (brown arrow) and 24 types of sound system used to play swiftlet sound audio 3.2 Flow chart of sound analysis development 29 3.3 The sound sample are cut (black color) 29 each for beginning, ending and two at the middle of sound 3.4 The flow chart of the software development 31 and visualization 3.5 The interface of MATLAB with feature of FFT 32 4.1 Analysis of swiftlet sounds 34 4.2 The Oscillogeam of internal JJ Anak Walet 36 4.3 The double – sided FFT of JJ Anak Walet at zero 37 center frequency 4.4 The single – sided FFT of JJ Anak Walet 39 4.5 The sonogram graph is based on STFT technique 41 5.1 The Oscillogram (time domain) for internal swiftlet 44 sound for Pontian’s Birdhouse (a) JJ Anak Walet (b) THL (c) Baby Kuang King (d) Quran Internal (e) Super Baby King and (f) Super Colony 5.2 The Oscillogram of internal swiftlet sound sample 45 for Skudai’s Birdhouse named as Juwita Malam 5.3 The oscillogram of internal swiftlet sound for 46 xiii Kota Tinggi’s birdhouse named as Golden 33 Swiftlet In 5.4 The Oscillogram (time domain) for external 47 swiftlet sound for Pontian’s Birdhouse (a) Dr F Black Girl (b) Bulan Mengambang (c) Double Trouble 2 (d) Dr Faiz Walet (e) Gangnam Walet and (f) Party Walet 5.5 The Oscillogram of external swiftlet sound sample 48 for Skudai’s Birdhouse named as Saliva Out 26 5.6 The oscillogram of external swiftlet sound for 48 Kota Tinggi’s birdhouse named as Golden 33 Swiftlet Out 5.7 The sonogram of JJ Anak Walet 55 5.8 The sonogram of Bulan Mengambang that has two 56 frequency along the time xiv LIST OF ABBREVIATIONS FFT - Fast Fourier Transform STFT - Short Time Fourier Transform Hz - Hertz 1 CHAPTER 1 INTRODUCTION 1.1 Introduction The success of swiftlet birdhouse depends on five important factors; temperature, humidity, odor, light and sound system. Sound system which consists of amplifier, tweeters and swiftlet recording audio played an important part to attract and lure the swiftlet into the house. Meanwhile, humidity, odor, light and temperature are factors for make swiftlet feel cave – like enviroment and comfortable for it to stay. An amplifier will play the swiftlet recording audio for 24 hours and delivered it to the tweeters. This study is will benefit the bioacoustics field. This study on swiftlet can be considered as new and can be as a guide for the entrepreneur who want to start this business. The study of animal with echolocation abilities has been done especially for bats and dolphin. However not many researchers have been done on the sound analysis of swiftlets. Therefore, this study is a fundamental guidelines for future study. 2 1.2 Problem Statement Not many birdhouse are successful. Most does not attract swiftlets, swiftlet are not nesting in the birdhouse - Kg. Seri Bunian, Pontian. It has been labeled as failure house since no indication of swiftlet entered the house since it was built. Another birdhouse design in Skudai also faced the same situation. But birdhouse in Skudai managed to attract few swiftlet to the house and making nest. Major problem is the swiftlets sounds - whether it is the correct sound or not; or whether the recorder are playing the sound with the correct power and volume. The other problem is, the population of swiftlet in this area is less compared to the rural area. Since sound system is the main parameter to attract swiftlet. Therefore, the analysis of swiftlet sound can be the first step to identify which factors lead to the failed birdhouse. The result is the information about the sound is known - it is played in the right frequency range for swiftlet; the energy used is the highest for swiftlet sound; and the frequency distribution along the time. 1.3 Objective of Project The main core of this project are: To perform the sound analysis for swiftlet sound and echolocation by using signal processing techniques - Fast Fourier Transform and Short Time Fourier Transform. To identify the frequency in the swiftlet sound sample and getting the frequency distribution for all swiftlet sample sounds. 3 1.4 Scope of Project In order to achieve the objectives, the scope of project needs to take points and outlined. The scope of this project will focus on the analysis of swiftlet sound using two different type of signal processing techniques which are Fast Fourier Transform (FFT) and Short Time Fourier Transform (STFT). The results will be showed in three different types of visualizations; oscillogram; sonogram; and power spectrogram. The swiftlet sound samples were taken at the swiftlet birdhouse which faced the failure and semifailed situation. 1.5 Project Flow In order to complete this project and research, the perfect planning must be developed. Thus, the overall project flow is shown in Figure 1.1. Start Literature Review from Paper and Journal Visit to swiftlet birdhouse area location in Johor – Kota Tinggi, Skudai and Pontian Design the program of FFT and STFT Pre-processing using MATLAB 4 Analysis Identify and discuss the sound characteristics End Figure 1.1: Project Flow 1.6 Outline of Thesis This thesis consists of 7 chapters. The first chapter is discuss about the introduction which includes background of study, objective and scope of this project along with summary of works. For Chapter 2 will discuss more about the swiftlet and birdhouse management. Besides that, the literature review about sound analysis technique using signal processing such as Fast Fourier Transform, Short Time Fourier Transform and graphic for sound representations. In Chapter 3, the discussion on methodology about sound analysis development and software development and visualization. All discussion of this project will be presented in Chapter 5. Last but not least, the conclusion of this project and future work are discuss in Chapter 6 while the project management of this project discuss in Chapter 7. 5 CHAPTER 2 THEORY AND LITERATURE REVIEW 2. 1 Introduction This chapter is divided into three parts which includes the introduction into swiftlets, graphic representation of sound, sound analysis using Fast Fourier Transform and Short Time Fourier Transform, and lastly discuss about MATLAB program which to run the simulation of the work 2.2 Swiftlet Swiftlets are very unique because they can make the nests by using its own saliva. This make them special from the other species since swiflet nest has a lot of nutritions which is good for the health. Swiftlets are a type of birds that looks similar with swallows (Herundinidae), sparrows (Paserride) and house swifts ( Apus Affinis) but there are a few differences and does not related between these species. Swiftlets are from the Collocaliini tribe within Appodidae family which the scientific name comes from a Greek word, apous, means “without feet” [1]. Swiftlets or Cave Swiftlets are contained within the four genera - Aerodramus, Hydrochous, Schoutedenapus and Collocalia [2]. Mostly swiftlet can be found in the South East regions i.e Malaysia, Indonesia, North Australia and Thailand. Swiftlets are the type of bird that have weak feets and not able to roost well. Apart from 6 that, it can fly for 12 hours long from the early morning until nightfall [3]. The vision of swiftlet is very sharp, it can catch aerial insects in position of 1,500 meters from sea level. Table 2.1: The differences between Swiftlet and Swallow Type of bird Swiftlet Swallow Characteristic Has curve wing and style Has long and sharp of flying is gliding wing .Type of flying is up and down style Uniqueness Making nest by using Type of visual foragers and saliva and echolocation has two area sharp focus on each retina called “fovae” (a) (b) Figure 2.1: The swiftlet nest produced by (a) Aerodramus Maximus (b) Aerodramus Fuciphagus 7 2.2.1 Types of Swiftlets There are currently 24 species of swiftlet are recorded in the world. But only three out of four classification of swiftlet can be found in Malaysia ; Aerodramus, Collocalia and Hyrdochous. According to Dr. Christopher Lim (2007), “ the five common species of swiftlets can be found in Malaysia and Borneo are Hydrochous Gigas, Collocalia Esculent, Asian Palm Swift (Cypsiurus Balasiensis), Aerodramus Fuciphagus and Aerodramus Maximus” (p. 4). While from Drs. Arief Budiman and Mohd Sallehuddin (2005) stated that “there are six classification of swiftlets can be found in Malaysia, Aerodramus Fuciphagus, Hydrochous Gigas, Aerodramus Maxima, Aerodramus Brevirostris, Aerodramus Vanikorensis and Collocalia Esculent” (p. 24). Table 2.2 The types of swiftlets can be found in Malaysia with specification Specification Name Aerodramus Fuciphagus (Edible Nest Bird) Aerodramus Hydrochous Gigas Maximus (Black Nest (Giant Swiftlet) Swiftlet) Type of bird The feather color is dark black and the bottom color is in grey or brown color The feather color is dark brown with the tail color is grey. It’s look similar to Aerodramus Fuciphagus Most big compare to other swiftlet. The black color of bird with bottom color is in dark brown. White Black Not determined Nest color 8 Specification Name Collocalia Esculenta (Glossy Swiftlet) Type of bird The feather color is dark blue with shiny. The bottom color is dark grey and at the stomach area is seems like white color Nest color Not determined 2.2.2 The Nutrition of Swiftlet nests From the scientific research, the swiftlet nest has lot of nutritions such as soluble glycol-protein and amino acids helps to strengthen the immune system and promote good skin layers [4]. The phosphorus and calcium inside the swiftlet nest help to build bone formation and iron helps to formation of red bloods [5]. Other than that it also helps to relieve breathing systems, correcting the nervous system, improved renal, white and smooth face (for men and women, get rid of wrinkles facial wrinkles (for women and men), refreshing eye and asthma (asthma). Meanwhile, it is not dangerous for those who have high blood pressure and obesity because it contents the cholesterol and fats with amount very low [5]. 9 2.2.3 Swiftlet House Swiftlet house plays the role major in luring to the house to make nest and increase the population of swiftlet. The design of swiftlet house must be same environment like in cave to make the swiftlet feel comfortable. Apart from that, the management of swiftlet house must be consistent based on several factors. Figure 2.2: The swiftlet house 2.2.3.1 House characteristics There are five factors to attract swiftlet to the house. All this factors need to be considered because the population of swiftlet might be decrease if the house management is not well- managed. Below are the characteristics of swiftlet house. Temperature Temperature is one important factor because it affected the swiftlet population and the quality of swiftlet nest itself. The ideal temperature of swiflet house is range in 25°- 28° Celsius [6]. If the temperature of swiflet house more than 30° Celsius, it will 10 resulted the saliva of swiftlet dries up fast. Therefore, it will affect the quality of the swiflet nest. To maintain the temperature in such range, some of steps can be considered such as make air ventilation hole, paints wall and thickening the walls. Figure 2.3: The air ventilation hole mounted with KNI pipe Humidity The humidity is another factor need to take considered. The ideal humidity for swiftlet house is about 80% - 90% [7]. The lower humidity level such as 30% - 50% will result the swiftlet nest is easily cracked and it will turn into another odd shape. This effects its eggs less fertile and the population will getting worse and might the swiftlet leave the bird house. While, the higher humidity level around 95% - 105% will turn the swiftlet nest become soft and changing from their original color. Figure 2.4: ThermoHygrometer gauge is used to measure moisture/humidity level in swiftlet room. 11 Light The environmental of swiftlet house must be exactly same like in cave. That means it must quite dark and no light penetrated into the house. This will give swiftlets feel comfortable and felt like in cave. Besides that, swiftlet like darkness environment as safety to their babies since it can travel in darkness through echolocation. The darkness of the house must be less than 1 lux [8]. Lux is the SI unit for illuminance. From the table below, the house must dark same like the darkness during full moon in night. Table 2.3: The various conditions related to illuminance Odor In order to get rid of all the unfamiliar smell like cement, paintwork and certain timber used to construct the farm, it is better to place bird shits for added some aroma [9]. This is called as guano which it undergoes some decomposition cycle where bacteria and bird shit insects turn into guano. Sound system The correct installation and positioning of tweeters in swiftlet house is one of important element. It’s very useful to attract and lure the swiftlet into the house by 12 playing a swiftlet sound. In general, animals communicate with other species with sound generate. This is the why the useful of using recording swiflets sound can attracted swiftlet into the house. Swiftlet is very quick when responding some particular sound such as mating sound. For the internal building, the best installation for positon the tweeters are at the corner. Swiftlet is attracted to make nest at the specific spot corresponding to the tweeter since it played the swiftlet sound. So, the amount of tweeter will determine the numbers of nest. Some of the juvenile swiftlet loves to make nest at the corner spot because it felt safe and protected [10]. Figure 2.5: The tweeters are mounted at the corners The angle of positioning the tweeter need also to be considered. It is very useful the angle of tweeter must not 90° facing each other. It will results the sound wave produced by tweeters colliding each other and make swiftlet become confused since it cannot identify the source. The best to face the tweeters are faced towards the entrance on each room and inter floored room. Figure 2.6: Piezo tweeter suitable to use for external building 13 Used different types of twitter for external, internal and puller of building played important part to lure the swiftlet into the house. The external is placed at outside of house which to attract the swiftlets come to the house. Meanwhile, the puller is placed at the rooftop of the house. It will played the swiftlet voice and make them to gather. Figure 2.7: Puller locate at the rooftop of swiftlets house Lastly, an amplifier is used as the heart of sound system which to amplify and deliver the signal to the transducer; loudspeaker or tweeters. It takes energy from a power supply or other input source and provides a larger version of the signal to the output or to the another stage of amplifier [11]. An amplifier must be robust which it can operate for long time and without face any problem when playing swiftlet sound. 2.2.5 Echolocation in Swiftlet Apart from making nest by using saliva, other uniqueness of swiftlet is able to echolocate [12]. Swiftlet has an echolocation ability which it used to navigate in the darkness of the caves they inhabit and social purposes. Merriam-Webster.com stated that an echolocation is like a sonar like system which it locating distant or invisible objects (such as prey) by emitting sound waves that are reflected back to the emitter by the objects. Echolocation of swiftlet produce a click like sound which returned echoes to provide information about bird’s speed and position relative to an object [13]. The “click” term is loosely used to describe acoustic signals which are short and do not exhibit any 14 structured changes in frequency over time. Swiftlet is sensitive towards sound especially mating sound. The frequency of echolocation of swiftlet fall in range 1-16 kHz [14]. Where the most energy is between 2 – 5 kHz. This frequency fall in the human hearing range which is 20 Hz – 20 kHz. This is why human can hear the swiftlet sound. Oilbirds and Swiftlets There are two types of bird that has the ability of echolocation which are Swiftlets and Oilbirds. South American oilbirds (Steatornis caripensis) performed function same as swiftlet which use echolocation to navigate in total darkness with low lighting conditions via low frequency echolocation. Oilbirds are nocturnal fruit-eaters, preferentially eating fruits of palms (Palmaceae), laurels (Lauraceae), and incense (Burseraceae). The echolocation frequency range of Oilbirds fall uneven distributed at the range 1-15 kHz with most energy is between 1.5 – 2.5 kHz [15]. Figure 2.8: South American Oilbirds Differences echolocation between bats and swiftlets Other than Oilbirds and swiftlets, the flying type animal which has an echolocation is bats. Bat has poor eyesight and use echolocation to navigate around objects and locate their prey using echolocation [16]. Compare to swiftlet, it does not use echolocation to catch their prey but using large eyes and use vision to locate their insect 15 ray [17]. The reason is the frequency use by swiftlets are much lower than bats (typically 20 kHz – 80 kHz and higher). Therefore, swiftlet does not suitable to detect such small object (range from 0.63 cm to 1 cm) and only use echolocation for navigation and social purpose. Figure 2.9: Bats use echolocation to locate their prey (Photograph by Merlin D. Tuttle) 2.3 Sound analysis of swiftlet sound The analysis of swiftlet sound is really important because it will determine the behavior, characteristics and quality of the sound. Most of the entrepreneur will use swiftlet sound as main attraction to lure swiftlets into the house. It is rarely to see the entrepreneur to analyze first the sound before it may be used. This will result to the failure luring swiftlet to house. One of the factors which contributed to failure is the sound maybe contain more noisy sound than it original sound. Besides that, the pitch and frequency of the sound is not compactable with swiftlet hearing lead to them reluctant to enter the house. The step in analysis swiftlet sound process is by taking several samples of sounds. There are many different types of swiflet sounds used by entrepreneur such as mating, baby and group of swiftlet when gathering. The main objective when conducted an analysis of swiftlet sound is to know at what most frequency contained in sound and look how much frequency available in one sample sound by using two technique of signal processing FFT and STFT. 16 2.3.1 Fourier Transform Fast Fourier Transform (FFT) is an algorithm which it translates signal in sinusoid in time domain and transform to the signal in frequency domain [18]. FFT is very suitable algorithm when applying the analysis of swiftlets sound since the signal is in time domain. It is difficult to know the frequency in time domain, so FFT will play their role to translate it into frequency. FFT is not another type of algorithm in Fourier Transform family, but FFT is a faster version than Discrete Fourier Transform (DFT). FFT is very useful in field that requires discrete time to discrete frequency transformation such as Microprocessor and Digital Signal Processing (DSP). (a) (b) Figure 2.10: Fast Fourier Transform (FFT) - The equation (a) is rewrite to (b) In Figure 2.10, it is easier to rewrite for the same values of . . It can be calculated many times as the computation proceeds. The advantages of the FFT can be achieved in a variety of ways. One of the way is it can be generalized for any number of coefficients, N by a factoring which does not shuffle the data. It takes a Fourier transform of a discrete set of data in N Log2 (N) operations. In Matlab program also include feature like FFT and it has become such a commonplace algorithm which built into Matlab by type fft(data) function. 17 2.3.2 Folding FFT Spectrum from Double Sided into Single Sided From Nyquist theorem, the frequency domain corresponds to the Fourier Transform will range from 0 Hertz to frequency sampling (fs) hertz with increament of fs/N. Therefore, FFT will produce double-sided spectrum as shown in figure 2.11. The double spectrums are separated into two for positive frequency and negative frequency with center at zero frequency. Basically the negative frequency is the reflection of positive frequency. The negative frequency is there because of mathematical reasons. Figure 2.11: FFT Double Sided Spectrum In practical, many instruments only use spectrum for positive frequency. It is better to fold from two - sided into one –sided frequency where to compensate the effect of ignoring the negative frequencies, by multiplying all positive frequency components by 2 as shown in figure 2.12. 18 Figure 2.12: FFT Single Sided Spectrum 2.3.3 Short Time Fourier Transform Short Time Fourier Transform is another signal processing method that used to analyze the signal in non – stationary, where its characteristic is vary with time. STFT fu (t , u ) f (t ) W (t t ) e j 2 ut dt t Figure 2.13: The equation for Short Time Fourier Transform Each frame extracted can be analyzed and view stationary by using window technique so the Fourier Transform can be used. When the window is moving along the time axis, then the relation between frequency and time can be identified. 19 (a) (b) Figure 2.14: STFT will extract into frame from the information of sinusoidal signal in (a) by using window technique (black) to identify into frequency and time in (b). Choosing the right window, W (t) also must be considered. Commonly type of windows are rectangular, Hann and Gaussian. The window should be narrow enough to make sure the portion of the signal falling within the window is stationary. It’s provide the good time resolution but poor frequency resolution. While wide window offer the good frequency resolution but poor time resolution. Short Time Fourier Transform Window Size W (t ) 1 If W(t) infinitely long: ,, STFT turns into FT, providing excellent frequency localization, but no time information. W(t) infinitely short: W (t ) (t ) , it gives the time signal back, with a phase factor, providing excellent time localization but no frequency information. 20 2.4 Visualization of Sound Visualization of sound helps to give the clear overview of the signal. It will show how the signal looks like after the transformation with certain function. There are several terms used in visualization of sound. Three main terms used in the analysis of swiftlet sound where to visualize the sound. There are Oscillogram, Sonogram and Power Spectogram [19]. The signals obtained from these graph will help to move further step which discussing the result. Visualization n Oscillogram Sonogram Power Spectrogram Figure 2.15: The visualization or graphic representation of sound 2.4.1 Oscillogram Oscillogram shown in Figure 2.16 is a graph which illustrates intensity fluctuations over the time. In other word, it likes amplitude waveform. The x-axis represent the passing of time corresponding to the sound while sound volume is reflected based on the height of the spikes above and below from this axis. The y-axis indicates the amplitude of the signal. Basically, Oscillogram is helpful to study the insect sound. But for sound with more complex frequency, the use of Oscillogram for analysis is not preferred. 21 Figure 2.16: A sound is represent in Oscillogram 2.4.2 Sonogram Sonogram is graph includes the information on the pitch or show the frequency of the sound over time. It allows looking inside a bird vocalization and providing important clues on how to differentiate one call or song from another [20]. The x-axis represents the time meanwhile y-axis show the frequency. The low frequency will appear at the baseline and the high frequency sound higher up. Sonogram will provide the information about the quality and richness of the sound. The more harmonic in certain time, it is more quality of the sound. Apart from that, sonogram also can show if noisy sound contain in the audio. The first one is understand the tone of the bird by look on how many sound are stacked at many moment of time either is like amount of harmonics or kind of noise. Second, need to interpret the rhythm and pattern of the song. 22 Figure 2.17: Example of Sonogram from Seaside Sparrow sound 2.4.3 Power Spectrogram Power spectrogram is important visualization because it gives the information of signal when it performs from time domain to frequency domain. Power spectrogram displays amplitude against frequency. The distribution of a song or signal depicting the sound energy present will show at each frequency. Power spectrogram is very desirable when to determine the frequency for certain sound like in Figure 2.18. Many researcher use Spectrogram when studying the hearing frequency of animals. Figure 2.18: Power Spectrogram of the Blackbird (Turdus merula) sound 23 CHAPTER 3 METHODOLOGY 3. 1 Introduction In this chapter will discuss about the methodology of how the analysis of swiftlet sound is carried out. The analysis is required to obtain the desired frequency distribution of the all swiftlet sound. 24 3.2 Analysis of Swiftlet sounds Analysis of the swiftlet sounds will be carried out and visualize into three partsOscillogram, Sonogram and Power Spectrogram. An Oscillogram is function to view the original sound in time domain. For Sonogram, it is functional to look inside the sound and describe the quality of the sound. Lastly the Power Spectrogram is to identify the frequency used in swiftlet song. All three visualization of signal then will be discussed and compared with all swiftlet sound samples The objectives of this analysis are to identify the frequency distribution used in swiftlet sound and the quality of sound. The process of the analysis of swiftlet sounds are undergo into two steps – sound development and software development and visualization. The sound development is used to gather all the sample and trim into several file size for fulfill the requirement when begin analysis using software. The pre-processing and analysis or software development are really important for signal processing when to start and study about the signal. The code to translate signal from time domain to frequency domain is by using Fast Fourier Transform algorithm and Short Time Fourier Transform in MATLAB and waveform results are obtained. 3.3 Swiftlet Sound Sample To obtain the swiftlet sample sound, the field trip must be done at few swiftlet birdhouse. There are three places swiftlet birdhouse in Johor – Pontian, Skudai and Kota Tinggi. 25 3.3.1 Types of swiftlet sound Swiftlet sound are play differently for external and internal amplifier. For external, basically swiftlet sound used is for attract the swiftlet into the house. The signal amplitude of external swiftlet sound is normally high. Compare with internal sound, the swiftlet sound use is for make comfortable swiftlet to stay and the sound is not noisy compare to external. Below figure 3.1 show the types of sound system for swiftlet sound (external and internal) along with the flying path of swiftlet sound. Figure 3.1: The flying path of swiftlet (brown arrow) and types of sound system used to play swiftlet sound audio 3.3.2 List of Swiftlet Sound With the permission the owner of swiftlet birdhouse, the sample sound can be collected and list in table 3.1 below along with the status of birdhouse. Only one swiftlet birdhouse in Pontian fail to attract swiftlet to the house since the birdhouse was built eventhough the swiftlet sound is played. From observation, there are lot of swiftlet played around near the birdhouse area but still cannot attract swiftlet to the house. For Skudai 26 area, it only managed attract few of swiftlet to the house, this might be the population of swiftlet in small town area is not many since difficult to find food and preys. While swiftlet birdhouse in Kota Tinggi face same situation with the birdhouse in Skudai only managed to attract few but because of the sound system having problem, swiftlet not entered this house again. Location of Birdhouse: Pontian Status House: Fail since swiftlet did not enter the house eventhough the management of house is fulfill. (a) (b) Location of Birdhouse: Skudai Status House: Considered fail but only attract few swiftlet to the house. No Name of swiftlet’s sound (sample) 1 Saliva Out 26 27 (a) No Name of swiftlet’s sound (sample) 1 Juwita Malam (b) Location of Birdhouse: Kota Tinggi Status House: Considered fail but only attract few swiftlet to the house before sound system is broken. No Name of swiftlet’s sound (sample) 1 Golden33 Swiftlet Out (a) No Name of swiftlet’s sound (sample) 1 Golden33 Swiftlet In (b) Table 3.1: List swiftlet sound samples for (a) external and (b) internal 28 3.4 Sound Analysis Development The swiftlet sounds obtained are in mp3 format need to cut and trim then must convert into wav format by using Power MP3 Cutter Pro software. In order MATLAB program to read the file into memory, MATLAB only can read the audio in wav format. The advantage of wav format is contain full information in heavy memory while mp3 format will reduce the information. The important to cut the sound into several sample because MATLAB can read the sound file size not more than 20 MB, otherwise the program will not respond and heavy. Power MP3 Cutter Pro is the software which has 2 in 1 functions - cut and trim the sound and also convert it into different format. The samples of swiftlet sounds in wav format are cut into 20 MB at the beginning, ending and two random locations at the middle of sound. Basically, the swiftlet sound has time length more than one hour and file size is more than 50 MB in mp3 format. So, it is difficult to analyze for full song when using Matlab. Below is the flow chart to show the process of sound analysis development. Start Obtained sample of the swiftlet sound Cut the sound for 20MB each at beginning, random 1, random and ending of the sound Convert sample from mp3 to wav 29 Pre-processing using MATLAB Analysis Identify and discuss the sound characteristics End Figure 3.2: Flow chart of sound analysis development Figure 3.3: The sound sample are cut (black color) each for beginning, ending and two at the middle of sound From Figure 3.3, the sound sample is cut by using Power MP3 Cutter Pro software. Each size of cute sound must be approximately about 20 MB. Which mean for one sample of sound has four different locations need to analyze. The advantage by using this method is faster to analyze the sound since swiftlet sound always has the same rhythm along the time. 30 3.5 Software Development and Visualization Software development and visualization is helpful for the next level which to analyze the sound. Software development is functional to transform signal from time domain to frequency domain using FFT function in MATLAB program. Besides that, this section also will have a look into swiftlet sound to tell the quality of the sound. Oscillogram, Sonogram and Power Spectrogram are types of visualization need to carry out at the end of the process for analysis process. Figure 3.4 shows the flow chart of the sound development and visualization. Start Pre-processing using MATLAB Sampling Frequency (FS), FFT Size and N= length of wave Calculate: ∆f = sampling frequency/FFT size Nyquist = sampling frequency / 2 Calculate FFT Shift the signal to the center frequency (f=0) for double sided spectrum. Translate double – sided FFT into Single – sided FFT 31 Pre-processing using STFT technique Visualize the signal in Oscillogram, Sonogram and Power Spectrogram End Figure 3.4: The flow chart of the software development and visualization. The sample of swiftlet sound which has been cut into four different locations are placed at the bin folder of Matlab program for begin the analysis. The reason is when writing the code in Matlab it will read the information of wave files. The information from the wav file can obtained such as frequency sampling (FS) and wave elements. The frequency sampling already exist in swiftlet sound sample when recording the audio in birdhouse. The frequency sampling is one of the method of analog to digital converter (ADC). Normally, the frequency sampling of audio is stored in 44.1 kHz. From the Nyquist theorem, the maximum frequency of one sample can obtained by frequency sampling divided by 2, FS/2. It is the requirement at least the maximum frequency is equal to frequency sampling divide by 2 to avoid aliasing. Since FFT only does the summation term, the value return by FFT must be scaled by dividing them by no points, K. The signal result obtained is double sided but not at zero center. Therefore it is preferred to make the double – sided FFT at center zero frequency to obtain negative and positive frequency. Then, the double – sided signal is translate into single – sided signal frequency since in this research interest in positive frequency only. The Short Time Fourier Frequency (STFT) is perform to get the frequency against time signal. At the end 32 of this process three visualization of sound signal can be obtained such as Oscillogram, Sonogram and Power Spectrogram. 3.6 Simulation Tool In this project will use MATLAB for simulation tools. MATLAB stands for MATrix LABoratory, the software is built up around vectors and matrices. It is partically useful for linear algebra but MATLAB is also a great tool for solving algebraic, differential equations and for numerical integration. MATLAB has powerful graphic tools and can view the pictures in both 2D and 3D. MATLAB has features of Fast Fourier Transform function and Short Time Fourier Transform which can analyze the sound from time domain to frequency domain. The drawback of MATLAB is cannot run the file size more than 20 MB. Because the size sample of swiftlet’s sound more than 20 MB (more than 100 MB), it needed the cut the sound not more than 20 MB. Figure 3.5: The interface of MATLAB with feature of FFT 33 CHAPTER 4 THE DEVELOPMENT AND SIMULATION OF SWIFTLET SOUND 4. 1 Introduction In this chapter will discuss about the development and simulation of the analysis of swiftlet sound is carried out. The analysis is required to obtain the desired frequency distribution of the all swiftlet sound. 34 4.2 Development of swiftlet sounds: Determine the frequency range and the high frequency out from the swiftlet sounds Analysis of swiflet sounds are conducted to determine the frequencies range and also the high frequencies came out from the swiftlet sounds. Besides that, the study about sound quality is included. The pre-processing and analysis are really important for signal processing when to start and study about the signal. The procedures and result will be discussed in following sections. Sample of the swiftlet sound Pre-processing using MATLAB for FFT Pre-processing using MATLAB for STFT Figure 4.1: Analysis of swiftlet sounds 4.2.1 Procedures 1. Take one sample from swiftlet sounds, then by using Power MP3 Cutter Pro to cut and trim the sound into different location time. 2. The sound is cut into 4 different parts, beginning, ending and two random locations at the middle of sound. 3. By using the same program, the sound which already trim is saved into .wav format. 4. Open MATLAB program then write the code as in section 4.3 to perform FFT function. Before that, the swiftlet sound in .wav format is placed into MATLAB library (bin folder). Notice that, the spectrum show is double – sided. 5. Rerun the program in MATLAB to obtain the FFT single-sided spectrum. 35 6. Then, the swiftlet sample is run again for STFT which it is use to show sonogram. 7. The figure results from MATLAB are then recorded and discussed. 4.3 Simulation of swiftlet sounds The simulation of swiftlet sound is done by using MATLAB for signal processing of Fast Fourier Transform and Short Time Fourier Transform. FFT will produce magnitude against frequency while STFT will produce frequency against time. These two techniques are really important to analyze the swiftlet sound. FFT can obtained what frequency contain inside swiftlet sound while STFT can obtained what frequency happen along the time. 4.3.1 Simulation of time domain graph The simulation of time domain of swiftlet sample is done by writing the program below in MATLAB. 1 - [wave,fs]=wavread ('E:\Matlab Program\bin\JJ Anak Walet.wav'); 2 - t=0:1/fs:(length(wave)-1)/fs; 3 - plot(t,wave); grid on 4 - xlabel ('Second (s)'); 5 - ylabel ('Amplitude'); 6 - title ('Oscillogram (time domain)'); 7 - axis tight In the first line, the code or program is write like this to get the MATLAB read the files such as frequency sampling (fs) and number of wave elements in swiftlet sound. 36 In line number 2 is to read the time from 0 to the length (wave) – 1/ fs with the increment of 1/fs. Then, it can be obtained the oscillogram graph by plotting the graph from line number 3 until 7. At the end, the result of oscillogram is shown as in figure 4.2. The program is rerun again for others type of swiftlet sound for internal and external Figure 4.2: The Oscillogeam of internal JJ Anak Walet 4.3.2 Simulation of Fast Fourier Transform In order to obtain frequency distribution of swiftlet sound, it is necessary to translate the time domain into frequency domain. This can be done by using Fast Fourier technique. By continue the code from above, the frequency domain can be achieved as below. 8 - n=length(wave); 9 - X_1 = fft (wave); 10 - X_2 = fftshift (X_1); 11 - X_mags = abs (X_2); 12 - bin_vals = [0 : n-1]; 13 - N_2 = ceil(n/2); 14 - fax_Hz = (bin_vals-N_2)*fs/n; 15 - plot(fax_Hz, X_mags), grid on 16 - set(gca, 'FontName', 'Times New Roman', 'FontSize', 14) 37 17 - xlabel ('Frequency (Hz)'); 18 - ylabel ('Magnitude'); 19 - title ('Double - sided FFT'); 20 - axis tight To estimate the spectrum, it can be done by using FFT command as in line number 9. The line number 10 will make the frequency shift to get center at zero frequency. Then, it can built up an appropriate frequency axis as in line number 12 by make the vector first such as f= 0,1,2,3,…., N-1. By writing until line 20, the double – sided FFT can be obtained as in figure 4.3. Figure 4.3: The double – sided FFT of JJ Anak Walet at zero center frequency From figure 4.3, it can see that FFT create the reflection where double – sided with center (f = 0) appeared when use FFT. The first half of the frequency is showing the positive frequency where range (from 0 to the Nyquist frequency fs/2) is sufficient to identify the component frequencies in the data. While, the other side is showing negative frequency range (from negative fs/2 to 0). Since FFT produce double – sided, the only part need to look is the positive part. Since many instruments only use the positive part, so it need to compensate the effect of 38 the negative parts. It is better to change double – sided spectrum into single sided spectrum. The process is known as folding as in code below. 21 - X_mags = abs(X_1); 22 - bin_vals = [0 : n-1]; 23 - fax_Hz = bin_vals*fs/n; 24 - N_2 = ceil(n/2); 25 - plot(fax_Hz(1:N_2), X_mags(1:N_2)), grid on 26 - set(gca, 'FontName', 'Times New Roman', 'FontSize', 14) 27- xlabel ('Frequency (Hz)'); 28 - ylabel ('Magnitude'); 29 - title ('Single - sided FFT'); 30 - axis tight In line number 25 will make the double sided FFT is folded into single – sided frequency arrange the frequency start of the plotting. The single sided FFT can be shown in figure 4.4. 39 Figure 4.4: The single – sided FFT of JJ Anak Walet From the figure 4.4, it can be seen that the clearly high peak frequency and also the frequency range from FFT technique. From figure above, the high peak frequency of JJ Anak Walet is at 5700 Hz for beginning of the sound. The rest of the location of sound is recorded and discuss in Chapter 5. FFT also can tell the frequency range of swiftlet sound which fall at 1 kHz until 15 kHz. Therefore, by using FFT, it can prove that can it can translate from the time domain into frequency domain. The rest of the sample is run by the same code and recorded in table. 4.3.3 Simulation of Short Time Fourier Transform From Fast Fourier Transform, the analysis is continue to other signal processing technique which is Short Time Fourier Transform. STFT is useful to look the frequency happens at certain time. It also very useful when it want to look the quality of the sound based on the frequency against time. Below is the code to develop sonogram based on STFT technique. 40 1 - [wave,fs]=wavread ('E:\Matlab Program\bin\JJ Anak Walet.wav'); 2 - x = x(:,1); 3 - xmax = max(abs(x)); 4 - x = x/xmax; 5 - N = length(x); 6 - t = (0:N-1)/fs; 7 - figure(2) 8 - spectrogram(x, 1024, 3/4*1024, [], fs, 'yaxis') 9 - h = colorbar; 10 - set(h, 'FontName', 'Times New Roman', 'FontSize', 14) 11 - ylabel(h, 'Magnitude, dB'); 12 - set(gca, 'FontName', 'Times New Roman', 'FontSize', 14) 13 - xlabel('Time, s') 14 - ylabel('Frequency, Hz') 15 - title('Spectrogram of the signal') Sonogram graph can be obtained by using the line number 8. When writing spectrogram function in MATLAB, it can performed sonogram by using STFT. Inside the parenthesis of spectrogram, the x represent the input signal vector. Meanwhile number 1024 represent window and follow by ¾ times 1024 as no of samples that each segments overlap. The bracket [] show the FFT length and the maximum is 256 or the next power of 2 greater than the length of each segment of x. The result of STFT can show the sonogram as in figure 4.5. 41 Figure 4.5: The sonogram graph is based on STFT technique From figure 4.5, the sonogram graph is obtain by using STFT from MATLAB program. Sonogram has the frequency at vertical axis while time at horizontal axis. At the right side show the color bar where the red indicate higher power strength while blue is lower power strength. The red bar indicates the frequency distribution like what FFT did. The frequency as in figure 4.5 show that the most energy happen at 1 kHz until 8 kHz where the peak frequency is 5700 Hz. Thus, by using sonogram based on STFT can obtained not only frequency along the time but also frequency distribution. 42 CHAPTER 5 RESULT AND DISCUSSION 5. 1 Introduction In this chapter, it will show the result for this project. First and foremost, an analysis of swiftlet sounds are conducted to find out the frequency range of the sound. The analysis of swiftlet sound is undergo into two; internal sound system and external sound system. The discussion from the analysis has been discussed in this chapter 43 5.2 Result from analysis of swiftlet sounds From analysis, the results for all swiftlet samples are recorded in figure below. The results are shown and discussed in three graphic representations – Oscillogram, Sonogram and Power Spectrogram are using MATLAB. All swiftlet sound sample as in Chapter 3 are analyze and discussed in section below. The results are then divided for external and internal for comparison between the amplitude and frequency. 5.3 Results from Oscillogram Result from oscillogram is shown below. Samples are analyze according to internal and external sound system. The samples taken from Pontain, Skudai and Kota Tinggi birdhouse are analyzed and compared. 5.3.1 Internal swiftlet sound (a) (b) 44 (c) (d) (e) (f) Figure 5.1: The Oscillogram (time domain) for internal swiftlet sound for Pontian’s Birdhouse (a) JJ Anak Walet (b) THL (c) Baby Kuang King (d) Quran Internal (e) Super Baby King and (f) Super Colony From Figure 5.1 show the result of swiftlet sound from Pontian’s Birdhouse and it played six sample (as listed in Chapter 3) has been collected. The purpose of internal sound is for make swiftlet comfortable and want to stay for make nest. That’s mean the sound must be soft and not making swiftlet feel anxious or threated. From the Oscillogram in figure 5.1, JJ Anak Walet, THL, Quran Internal and Super Coloni show there are high and low amplitude along the time which means the sound is not noisy and make swiftlet to feel comfortable. Compare with Super Baby King and Baby Kuang King show the amplitude are high along the time. These two sound is quite noisy and maybe make swiftlet to feel anxious and threated to stay. To identify the sound quality, the 45 internal sound to make comfortable for swiftlet to stay must has good high and low amplitude. Figure 5.2: The Oscillogram of internal swiftlet sound sample for Skudai’s Birdhouse named as Juwita Malam Figure 5.2 shows the oscillogram of swiftlet sound in Skudai’s Birdhouse only played one internal swiftlet sound named as Juwita Malam. From the figure above, it can be seen that there is a high and low amplitude along the time. The quality of this sound is good and can make swiftlet feel comfortable when playing this sound. Figure 5.3: The oscillogram of internal swiftlet sound for Kota Tinggi’s birdhouse named as Golden 33 Swiftlet In 46 From Figure 5.3, swiftlet sound named as Golden 33 Swiftlet In contains high amplitude along the time. Which means, this sound is maybe quite noisy for the bird to stay when Golden 33 is playing. The variation of high and low amplitude is like human’s speech which sound comfortable when heard. If the amplitude of sound is high along the time, maybe the sound is noisy to be heard by swiftlet. Since it cannot be tell about types swiftlet sound such as crying, mating, thread or etc, the oscillogram can help to determine the kind of sound of swiftlet. 5.3.2 External swiftlet sound (a) (c) (b) (d) 47 (e) (f) Figure 5.4: The Oscillogram (time domain) for external swiftlet sound for Pontian’s Birdhouse (a) Dr F Black Girl (b) Bulan Mengambang (c) Double Trouble 2 (d) Dr Faiz Walet (e) Gangnam Walet and (f) Party Walet From figure 5.4 shows the time domain graph for Pontian’s Birdhouse for external sound. It can be seen that from figure 5.4 for Bulan Mengambang, Double Trouble 2 and Party Walet has good high amplitude along the time. The external sound is used to attract swiftlet near birdhouse. That’s mean, to attract swiftlet the house must be interesting and not so soft to indicate there are many swiftlet gathered at this place. Compare with Dr F Black Girl and Dr Faiz Walet has the variation of high and low amplitude at first sound, but then it goes to have high amplitude after certain period. Meanwhile for Gangnam Walet, it has good high amplitude at first of the sound then it goes to has variation of high and low amplitude. After a certain time, it goes back same as the first time. 48 Figure 5.5: The Oscillogram of external swiftlet sound sample for Skudai’s Birdhouse named as Saliva Out 26 From figure 5.5, the external swiftlet sound sample for Skudai’s Birdhouse has the good high amplitude along the period. Means, the quality to attract swiftlet is very efficient when playing this sound. Meanwhile, for Gold 33 Swiftlet Out as shown in figure 5.6 also contains a good high amplitude along the time. But the quality of sound is not very efficient compare with Saliva Out 26 sound since it also appears low amplitude. Figure 5.6: The oscillogram of external swiftlet sound for Kota Tinggi’s birdhouse named as Golden 33 Swiftlet Out 49 5.4 Results from Power Spectrogram The result from power spectrogram is based on Fast Fourier Technique (FFT) technique which it translate from time domain (Oscillogram) to frequency domain (Power Spectrogram). The purpose of this processing is to know the high frequency available and also the frequency range of swiftlet sound. The process and graph as shown in Chapter 4 is then recorded in table 5.1, 5.2, 5.3, 5.4, 5.5 and 5.6. The distribution of high frequency is record into four section each for beginning, middle random 1, middle random 2 and ending of the sound. The frequency range of the sound also indicate at the last column of the table. It divide into two parts one for internal and one for external. 5.4.1 Internal Swiftlet Sound Table 5.1: The distribution high peak frequencies and frequency range of internal swiftlet sounds for Pontian Birdhouse JJ Anak Walet 5700 1st Middle Random (Hz) 5400 THL 5100 5100 5100 5100 5100 1 – 14 Baby Kuang King 5600 5600 5600 5600 5600 1 – 10 Title of Sound Beginning (Hz) 2nd Middle Random (Hz) 5700 Ending (Hz) 5700 5625 1 – 19.5 Average Frequency Frequency Range (Hz) (kHz) 50 Quran Internal 2400 600 2400 2400 1950 1–9 Super baby king 2400 2200 2200 2600 2350 1–9 Super Colony 5700 5400 5700 5700 5625 1 – 19.5 From table 5.1, THL and Baby Kuang King has equal high peak frequency along the time when played which 5100 Hz and 5600 Hz. These two types of sound also fall at the swiftlet frequency range each for 1 – 14 kHz for THL sound and 1 – 10 kHz for Baby Kuang King Sound. From previous research state that the swiftlet hearing frequency is at 1-16 kHz [14]. Where the most energy is between 2 – 5 kHz. For JJ Anak Walet and Super Colony has same distribution high peak frequency which the average frequency is 5625 Hz. There is probability the two types of sound are identical same types but with different names. The frequency range of these types of sound also same where fall at 1 – 19.5 kHz which out of the ideal frequency range of swiftlet. This maybe factor of noise happen inside the sound. Meanwhile for Quran Internal, has the peak frequency 2400 Hz for beginning, middle random 2 and ending. But at the middle random 1, the peak frequency is fall at 600 Hz. That’s mean for certain time, these sound appeared the frequency of 600 Hz when played. This might make swiftlet away from the house since it played the sound out of ideal most energy. Furthermore, Quran Internal and Super Baby King contain the average frequency are each at 1950 Hz and 2350 Hz and fall at the same frequency range which at 1- 9 kHz. 51 Table 5.2: The distribution high peak frequencies and frequency range of internal swiftlet sounds for Skudai Birdhouse Title of Sound Beginning (Hz) Juwita Malam 5900 1st Middle Random (Hz) 5500 2nd Middle Random (Hz) 5700 Ending (Hz) 5700 Average Frequency Frequency Range (Hz) (kHz) 5700 1-9 For table 5.2 shows the internal swiftlet sound for Skudai birdhouse. This birdhouse only played one types of sound name as Juwita Malam. From the information obtained from swiftlet birdhouse owner, his birdhouse manage to make swiftlet to stay and making nest. He only managed to get 5 nest a year and this might be the population at this area is not many as in rural areas. The distribution of high peak frequency for Juwita Malam is at 5700 Hz. This type of sound also fall at the swiftlet frequency range which at 1 – 16 kHz. Same goes to swiftlet birdhouse in Kota Tinggi which it can attract swiftlet to stay but no making some nest. This probably due to other parameters of house such as temperature, humidity and odor of the house. Swiftlet is not just only sensitive toward the sound but also towards odor. If it sniff some prey stay at the house, it just only stay and not making some nest. 52 Table 5.3: The distribution high peak frequencies and frequency range of internal swiftlet sounds for Kota Tinggi Birdhouse Title of Sound Golden 33 Swiftlet In Beginning (Hz) 1st Middle Random (Hz) 5400 5200 2nd Middle Random (Hz) 5200 Ending (Hz) 5000 Average Frequency Frequency Range (Hz) (kHz) 5200 1 – 14 From table 5.3 shows that the average frequency is at 5200 Hz. Besides that, this type of sound also fall at the identical swiftlet hearing range which fall at 1 – 14 kHz. Conclusion, the swiftlet birdhouse from Skudai and Kota Tinggi can be as references to compare the frequency since these two types of sound managed to make swiftet stay and making nest. Eventhough these two types of birdhouse face the population issue and threat from preys, but it still managed to make swiftlet stay and making nest. It can be conclude that the average peak frequency of sound for internal is range from 5 – 6 kHz to make swiftlet feel comfortable and stay inside the house. 53 5.4.2 External Swiftlet Sound Table 5.4: The distribution high peak frequencies and frequency range of external swiftlet sounds for Pontian Birdhouse Dr F Black Girl 5400 1st Middle Random (Hz) 5300 Bulan Mengambang 5000 4950 5000 5000 4988 1 – 16 Double Trouble 2 5500 6000 6200 6200 6200 1 – 16 Dr Faiz Walet 5600 5400 5050 4000 5013 1 – 15 Gangnam Walit 6000 6000 6000 5800 5950 1 – 12 Party Magic 2500 5800 2500 5000 3950 1 – 16 Title of Sound Beginning (Hz) 2nd Middle Random (Hz) 5300 Ending (Hz) 5100 5275 1.8 – 15 Average Frequency Frequency Range (Hz) (kHz) From table 5.4, it show the distribution of six external swiftlet sample sound is analyze and the frequency is recorded in table above. Double Trouble 2 and Gangnam Walit contained the high average peak frequency which at 6200 Hz and 5950 Hz. Meanwhile, Dr F Black Girl and Dr Faiz Walet has the average peak frequency above 5000 Hz which each at 5275 Hz and 5013 Hz. Meanwhile, Bulan Mengambang and Party Magic has the average peak frequency below 5000 Hz. All six sample sound are fall at the frequency hearing range when it played. 54 Table 5.5: The distribution high peak frequencies and frequency range of external swiftlet sounds for Skudai Birdhouse Title of Sound Beginning (Hz) Saliva Out 26 5600 1st Middle Random (Hz) 5600 2nd Middle Random (Hz) 6000 Ending (Hz) 5800 Average Frequency Frequency Range (Hz) (kHz) 5750 1 - 16 From table 5.5, it show the distribution of high peak frequencies of external swiftlet sound for Skudai Birdhouse. This types of sound has the average peak frequency is at 5750 Hz. It is slightly different between the internal sound (Juwita Malam) which has average frequency of 5700 Hz. But the frequency range for Saliva Out 26 sound is fall at the same frequency range of swiftlet which at 1 – 16 kHz. It is not surprising this Skudai Birdhouse manage to attract swiftlet into the house since it played the right type of sound – the peak frequency is quite high and fall at the frequency range of swiftlet hearing. Table 5.6: The distribution high peak frequencies and frequency range of external swiftlet sounds for Kota Tinggi Birdhouse Title of Sound Golden 33 Swiftlet Out Beginning (Hz) 1st Middle Random (Hz) 5800 6000 2nd Middle Random (Hz) 6000 Ending (Hz) 5800 Average Frequency Frequency Range (Hz) (kHz) 5900 1 – 16 55 For table 5.3, there are differences between internal and external high peak frequency distribution. For internal, Golden 33 Swiftlet 33 In has peak frequency at 5200 Hz meanwhile for Golden 33 Swiftlet 33 Out has the average peak frequency at 5900 Hz. Kota Tinggi Birdhouse also managed to attract swiftlet near and into the house when played this types of sound. As a conclusion, the average frequency range for external swiftlet sound is best at the frequency range from 5.5 – 6.5 kHz since the external is required high frequency to attract swiftlet into the house. Since human cannot understand bird language, the study to indicate which types of sound is the best can be done by obtained the high peak frequency from Fast Fourier Transform technique. 5.5 Results from Sonogram The result of sonogram are recorded for all three sample birdhouse from Pontian, Kota Tinggi and Skudai. The result is then divided for internal and external swiftlet sound and discussion is made at this section. Figure 5.7: The sonogram of JJ Anak Walet The result for all swiftlet sample from three birdhouse are analyze and has the sonogram graph like in figure 5.7. There all show the same pattern which happened to 56 have one frequency along the time. But, only one swiftlet sample named as Bulan Mengambang contained two frequency along the time when it played. Figure 5.8 show the sonogram of Bulan Mengambang’s sample that has two frequency. Figure 5.8: The sonogram of Bulan Mengambang that has two frequency along the time From figure 5.8 show the Bulan Mengambang that has two frequency happened at the same time and period. One of the frequency happened at the range between 3 – 6 kHz where the peak frequency is at 5000 Hz along the time played. The magnitude of first frequency is about 40 to 50 dB. The second frequency happened at the range 15 kHz until 16 kHz along the time played. The magnitude of the second frequency is 70 to 80 dB. This second also appeared at the limit hearing frequency of swiftlet supposedly the frequency range of swiftlet is at 1 – 16 kHz. This kind of sound will make swiftlet confuse and away from the house since it played for external sound. Even human also has the limit frequency range ( 2 Hz until 20 kHz) and feel pain when hear at the limit frequency. Thus, sonogram is helpful to see inside bird vocalization and identify the frequency happened at one time or period. 57 CHAPTER 6 CONCLUSION 6.1 Conclusion The analysis is based on two signal processing technique – Fast Fourier Transform and Short Time Fourier Transform can be achieved. Besides that, MATLAB will be the main interface to develop those signal processing technique. This kind of technique can extract the information inside swiftlet sound sample such as high frequency distribution, frequency range and look inside bird vocalization. Because human cannot understand the bird language and sound, therefore the analysis will help them to determine types of sound should has to attract swiftlet to the house and make them comfortable to stay. Therefore, the quality of sound for swiftlet sound should have is good variation of high and low amplitude for internal sound. Whereas for external sound is good if has the high frequency along the time. This is necessary to attract swiftlet near the house when hear the good quality of high amplitude. Furthermore, the range of frequency of swiftlet sound is suitable at the range of 1 – 15 kHz. Meanwhile the proposed high frequency distribution for swiftlet sample is range between 5 – 6 kHz for internal sound and 5.5 kHz – 6.5 kHz for external sound. 58 The swiftlet sample sound should has only one frequency happened at what time. If there are two types or more frequency happened along the time sound is played, the sound quality is very poor. 6.2 Recommendation and Future Work The analysis can be research further to identify exact frequency for swiftlet sound by doing some filtering. There is maybe some other noise has been recorded during collecting sound sample. Besides that, from the data obtained from this research, the test can be conducted at swiftlet birdhouse for one by one of swiftlet sound swiftlet to see which types of sound gives reaction to swiftlet to support the proposed conclusion. 6.3 Summary The objective of the project had been successfully achieved. The analysis of swiftlet echolocation for all swiftlet sound sample of three swiftlet birdhouse is done. In the following chapter, the project management such as project schedule is shown. 59 CHAPTER 7 PROJECT MANAGEMENT 7.1 Introduction The purpose of the project management is to accomplish the project goals with effective planning, organizing and controlling resource within a specified time of period. Research scope, time and resources are the primary constraints in this project. Therefore, project schedule had been tabulated on Gantt chart to give a clear guideline in time management of this project. 7.2 Project Schedule Without a right planning, the completion of this final year project for Sound Analysis of Swiftlet Echolocation will not be able to meet the deadline and target. Thus, the Gantt chart is used to be followed as the schedule to check the progress of the project. Table 7.1 shows the project Gantt chart for semester two. The journey throughout completing the final year project require full of commitment. The project begins with project background study, such as Graphic the characteristic of swiftlet and birdhouse management. Because the kind of analysis is 60 still new and would be a good idea for proposing the suitable project title. In performed the analysis, MATLAB has been used. Other tasks are commences as proposed on date. Table 7.1 Work schedule for FYP 2 W1 W2 W3 W4 W5 W6 W7 W8 W9 W10 W11 W12 W13 W14 W15 Task Week Literature studies on swiftlet industry, birdhouse and sound List all related parameters Make visit to swiftlet birdhouse Collecting swiftlet sound sample Analyze the swiftlet sound based on FFT and STFT technique Make the discussion based on analysis Thesis writing Report draft submission and journal Submit hardbound thesis 61 REFERENCES [1] Dr. Christopher Lim (2007). Introduction to Swiftlets. Make Millions From Swiftlet Farming: A Definitive Guide (pp. 3). Kuala Lumpur: TRUEWEALTH PUBLISHING. [2] Swiftlet. About swallow nest, swiftlet, edible bird’s nest Article. Retrieved from http://swallow-nest.com/article/2006/12/14/swiftlet-2/ [3] Mohd Sabran Md Sani (2013, May 6). Harga Cecah Rm 10,000 sekilogram. Harian Metro Online, n.d . Retrieved May 6, 2013, from http://www.hmetro.com.my/articles/HargacecahRM10_000sekilogram/Article. [4] Dr. Christopher Lim (2007). 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