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 104 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. 105 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. 6. REFERENCES Figure 6: Plot of original and denoised signals [1] Bowman, M., Debray, S. K., and Peterson, L. L. 1993. The Matlab code used for compressing the signal is Reasoning about naming systems. ACM Trans. Program. [thr,sorh,keepapp]=ddencmp('cmp','wv',s); Lang. Syst. 15, 5 (Nov. 1993), 795-825. DOI= [sd,csd,lsd,perfo,perfl]=wdencmp('gbl',s,'db3',3,thr,sorh,keepapp) http://doi.acm.org/10.1145/161468.16147. ; [2] Brown, L. D., Hua, H., and Gao, C. 2003. A widget „Perfo‟ and „perfl‟ are the variables which defines the framework for augmented interaction in SCAPE. In performance of the wavelet used for compression.‟perfo‟ indicates Proceedings of the 16th Annual ACM Symposium on User the number of zeroed coefficients. For the present experiment a Interface Software and Technology (Vancouver, Canada, 68.0609% was obtained. This indicates that a good compression November 02 - 05, 2003). UIST '03. ACM, New York, NY, can be achieved at least beyond 60%. 99.9915% was obtained for 1-10. DOI= http://doi.acm.org/10.1145/964696.964697 „perfl‟ which indicates almost equal energy in the compressed 106 [3] Ding, W. and Marchionini, G. 1997. A Study on Video [7] Spector, A. Z. 1989. Achieving application requirements. In Browsing Strategies. Technical Report. University of Distributed Systems, S. Mullender, Ed. ACM Press Frontier Maryland at College Park. Series. ACM, New York, NY, 19-33. DOI= [4] Forman, G. 2003. An extensive empirical study of feature http://doi.acm.org/10.1145/90417.90738 selection metrics for text classification. J. Mach. Learn. Res. [8] Tavel, P. 2007. Modeling and Simulation Design. AK Peters 3 (Mar. 2003), 1289-1305 Ltd., Natick, MA. [5] Fröhlich, B. and Plate, J. 2000. The cubic mouse: a new [9] Yu, Y. T. and Lau, M. F. 2006. A comparison of MC/DC, device for three-dimensional input. In Proceedings of the MUMCUT and several other coverage criteria for logical SIGCHI Conference on Human Factors in Computing decisions. J. Syst. Softw. 79, 5 (May. 2006), 577-590. DOI= Systems (The Hague, The Netherlands, April 01 - 06, 2000). http://dx.doi.org/10.1016/j.jss.2005.05.030. CHI '00. ACM, New York, NY, 526-531. DOI= [10] Zhao O. D. and Sheng-qian, M. A. 2010 “Speech http://doi.acm.org/10.1145/332040.332491. Compression with Best Wavelet Packet Transform and [6] Sannella, M. J. 1994. Constraint Satisfaction and Debugging SPIHT Algorithm” Second International Conference on for Interactive User Interfaces. Doctoral Thesis. UMI Order Computer Modeling and Simulation in 2010 Number: UMI Order No. GAX95-09398., University of Washington. 107