=Paper=
{{Paper
|id=Vol-1755/104-107
|storemode=property
|title=Implementation of Audio Compression Using Wavelet
|pdfUrl=https://ceur-ws.org/Vol-1755/104-107.pdf
|volume=Vol-1755
|authors=Hauwa Talatu Abdulkarim,Tijjani S Abdulrahman,Abubakar Sadiq Mohammed
|dblpUrl=https://dblp.org/rec/conf/cori/AbdulkarimAM16
}}
==Implementation of Audio Compression Using Wavelet==
https://ceur-ws.org/Vol-1755/104-107.pdf
Implementation of Audio Compression using Wavelet
Hauwa T. Abdulkarim Tijjani S. Abdulrahman
Department of Department of Abubakar S. Mohammed
Electrical/Electronic Electrical/Electronic Dept. of Electrical/Electronic
Technology, College of Technology, College of Engineering, Federal University
Education, Education, of Technology, Minna, Nigeria
Minna, Nigeria Minna, Nigeria abussadiq@yahoo.com
talatuabdulk@gmail.com teejays1569@gmail.com
ABSTRACT is one of the scarcest resources [Sannella 1994; Forman 2003] in
The need to transmit audio signal has increased tremendously such networks and data compression is one of the implementing
over the past decade. In view of this, audio compression is a sure techniques to save energy in these networks [Tavel 2007].
technology of the multimedia age which facilitates ease of The increase in data transfer has led to the need to develop
transmission. The change in the telecommunication infrastructure, appropriate signal processing techniques to handle audio and
in recent years, from circuit switched to packet switched systems video compression [Brown et al. 2003]. Many types of digital data
has also reflected on the way that speech and audio signals are can be compressed in a way that reduces the size occupied on a
carried in present systems. In many applications, such as the computer memory or the bandwidth needed to stream it with no of
design of multimedia workstations and high quality audio the full information in the original signal. Audio compression can
transmission and storage, the goal of audio compression is to be achieved by either lossless compression (in which all the
encode audio data to take up less storage space and less information from the original signal is recoverable) or by lossy
bandwidth for ease of transmission. This paper presents the data compression (in which the original signal is permanently
implementation of audio compression using wavelet. The changed by removing redundant information [Yu 2006].
implementation procedure, the Matlab code and the results Although, lossless compression would keep all the information of
obtained are duly presented and discussed. The final results the original signal unaltered, it has the limitation of compression
indicate that a good reconstruction was performed and the ratio of about 3:1 while with lossy compression algorithms, the
performance of the wavelet was excellent with the performance compression ratio can be as high as 12:1 or higher [Spector 1989].
variables all in the region well above 60%.
Audio compression is very much employed in this computer age
CCS Concepts where information can be sent over the internet and other
• Hardware ➝ Communication hardware, interfaces and ways[Zhao and Shen 2010]. Obviously the presence or absence of
storage ➝ Signal processing systems ➝ Digital Signal some details in a sound signal makes no difference to the user and
Processing removing the details during compression is of advantage to
storage and bandwidth required and consequently maximizing the
Keywords compression efficiency.
Audio; Wavelet; Compression; Transmission; MATLAB code;
Performance 2. METHODOLOGY
1. INTRODUCTION 2.1 Implementation
Audio compression- a popular 21st century technique enables the The implementation of the audio compression experiment was
substantial data rates associated with uncompressed digital audio done using Matlab. An audio file „short_beethoven.wav‟ and
signal to be efficiently stored and transmitted [Bowman et al., „plot_time_scale.m‟ were both downloaded into the Matlab
1993]. In this modern day, sounds of telephone, television, radios directory. The audio signal was loaded using a Matlab command
etc. undergo some form of compression or the other to improve „wavread‟. This original signal was plotted in order to be able to
the quality of sound and ultimately reduce storage space and differentiate it with the compressed signal. Figure 1 shows the
bandwidth. original signal.
The advancement in radio communication has geared up the
development of wireless multimedia sensor networks (WMSNs)
which can process multimedia data such as video and audio
streams, still images collected from the application area[Ding and
Marchionini 1997; Fröhlich and Plate 2000; Tavel 2007]. Energy
CoRI’16, Sept 7–9, 2016, Ibadan, Nigeria.
Figure 1: Plot of Original Signal
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Discrete Wave Transform (DWT) analysis was then performed
using the command [ca1,cd1]=dwt(s,'db3') which gives a one-
level step decomposition sequentially. The three level
decomposition for both the approximate and detail coefficient
obtained are presented in The Matlab command „soundsc‟ was
used to listen to the decomposed signal and the effect of
decomposition was observed.
After decomposition was complete the next was reconstruction of
all the details and approximations values from their coefficients
and levels of decomposition were done and the signal was
checked for errors to be sure a perfect reconstruction was done
before compression. Invert directly decomposition of the original
signal was then done and this was followed by reconstruction of
the original signal. The signal was compressed after inverse
discrete wave transform (IDWT).. Error (k) was determined
between the compressed and the original signal. The error in this
case was a value of
( )
The error, k is a value which defines the deviation of the denoised
signal from the original. This value is small enough to assume the Figure 3: Detail Coefficient for 3 Levels
deviation is negligible and this therefore implies that a near
perfect reconstruction was made.
3. RESULTS AND DISCUSSION
Figure 2 shows the plot of the original signal and the
approximation coefficient for three decomposition levels. „db3‟
was used for the 3-level decomposition, this is shown in Figure 3.
Figure 4: Plot of Histogram of original signal and
Approximation values
3.1 Compression and De-noising
‘ddencmp‟ Matlab command was used to automatically generate
Figure 2: Approximation Coefficient the thresholding needed. The function also denoises and
compresses. Figure 6 presents the denoised and the original
signals for assessment and comparison. To the eye, the two
Figures 4 and 5 show the histogram of the original signal, signals seem very identical although there are differences that
Approximation and Detail values for varying levels of may not be detected with human eye. The signal was denoised
decomposition. Histogram is a very handy tool to present results using global thresholding option applying the Matlab command
of experiments, in this case it presents at a glance the detail and „wdencmp‟.
approximation values at different levels. This represents the
energy and frequencies stored for decomposition.
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signal and the original signal. This implies that no data was loss as
a result of the compression.
Plot_time_scale.m was used to plot the discrete transform in
colour.
Figure 5:Plot of Histogram of original signal and Detail values
Figure 7: Image time-scale diagram representation of signal
detail decomposition value levels
.
4. CONCLUSION
Audio compression was implemented using wavelet. The
performance of the wavelet was excellent with „perfo‟=68.06%,
„perf12‟=99.9929%, and „perfl‟=99.9915%. This shows The
reconstruction was good as well since the error is negligible.
Audio compression is used for transmission and storage. The
compression is achieved by representing each sample of digitized
data by lesser number of bits and making it occupy lesser space
and consequently easy to transmit or store.
5. ACKNOWLEDGMENTS
The authors wish to thank Tertiary Education Trust Fund
(TETFund), Abuja, Nigeria and College of Education, Minna,
Nigeria for the sponsorship.
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