![]() ![]() We often need to process these audio signals for various applications. MATLAB is one of the best signal analysis and signal processing tools.Īudio compression is a very good example of speech and signal processing. We use the Internet for various purposes including entertainment. Audio is common in all entertainment applications. If an audio file size is large, it takes more space to store.Īudio/video compression frees up space substantially, which can then be utilised for other purposes. This article describes some important audio compression techniques.Īn audio signal sample is taken and analysed using MATLAB for frequency and amplitude. Haar and Daubenches algorithms are applied on the speech signal and the audio is compressed. Audio sizes before and after compression are compared. The following parameters are compared by the program: Peak signal-to-noise ratio (PSNR), normalised root-mean-square error (NRMSE) and compression ratios. Haar wavelet algorithm performs the following functions:ġ. Selects audio and finds actual signal sizeĤ. Inspects the spectrum and finds tones maskersĭaubenches wavelet transform performs the following functions:ġ.ĭecomposes the signal spectrum into waveletĦ. Selects audio and finds the actual signal sizeĦ. Does compression using inverse discrete cosine transform (IDCT)įor complete algorithms, refer code implementations. ![]() MATLAB code file AudioCompression.m implements Haar wavelet and AudioCompression2.m file implements Daubenches wavelet. In this example, Windows XP Startup.wav is the sample audio file used for compression.Ĭomparison of performance metrics such as PSNR, MSE and compression ratio shows that Daubenches algorithm is best suited for lossless compression of speech signals. Advantages of audio compression are less storage space and associated cost, and faster data transfer. R D Pinzon Morales et al.There are several techniques for data compression. Mmunications and Informatics, Proceedings of the 11th internationalĬonference on Signal Processing (SITE'12),115-120. In Proceedings of the 11th international conference on Teleco Wavelet function for pupil fluctuation analysis to evaluate levels of The library implementing the GA is from Peter Møller-Nielsen This method has been presented among other references in. The GA optimize U and P based on a given cost function, for example, class separability, mutual information, etc. The LS have two FIR filter, U and P, that correspond to the wavelet and scalling functions in the traditional WT. Matlab Files to optimize a wavelet function using genetic algorithms (GA) and lifting schemes (LS), which are an implementation of the WT using filter banks. Optimization of Wavelet Functions with Genetic Algorithms in Matlab ![]()
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