UNIVERSITI TEKNOLOGI MALAYSIA

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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
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I acknowledged that Universiti Teknologi Malaysia reserves the right as follows:
1.
2.
The thesis is the property of Universiti Teknologi Malaysia.
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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
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