Abstract
Compression of speech signal is an essential field in signal processing. Speech compression is very important in today’s world, due to the limited bandwidth transmission and storage capacity. This paper explores a Contourlet transformation based methodology for the compression of the speech signal. In this methodology, the speech signal is analysed using Contourlet transformation coefficients with statistic methods as threshold values, such as Interquartile Filter (IQR), Average Absolute Deviation (AAD), Median Absolute Deviation (MAD) and standard deviation (STD), followed by the application of (Run length encoding) They are exploited for recording speech in different times (5, 30, and 120 seconds). A comparative study of performance of different transforms is made in terms of (Signal to Noise Ratio,Peak Signal to Noise Ratio,Normalized Cross-Correlation, Normalized Cross-Correlation) and the compression ratio (CR). The best stable result of implementing our algorithm for compressing speech is at level1 with AAD or MAD, adopting Matlab 2013a language.
Highlights
The speech communication plays an important role in every day applications, after the creation of cell phones and Internet services, which generated the possibility of transmitting voice over the networks in a digital format
Problem statement We noticed from previous research that the Contourlet coefficients were not studied through the application of the four statistical methods of Interquartile Filter (IQR), standard deviation (STD), AAM and Median Absolute Deviation (MAD), in terms of the effects of zeroing or canceling those coefficients, their effectiveness on the percentage of compression, and the quality of the sound recovered from the compression process
The performance of the work is assessed through the use of the measures of Compression Ratio (CR), Signal to Noise ratio (SNR), Peak Signal to Noise Ratio (PSNR), Normalized Root Mean Square Error (NRMSE), and Normalized Cross-Correlation (NCC), which were measured for reconstructed speech obtained from Contourlet based speech compression techniques [7]
Summary
The speech communication plays an important role in every day applications, after the creation of cell phones and Internet services, which generated the possibility of transmitting voice over the networks in a digital format. The general formulas for calculating Q1 and Q3 are given in equation (1), (2), and (3): Standard deviation is the measure of dispersion of a set of data from its mean It measures the absolute variability of a distribution. Figure-3 shows the high and low frequency of the contourlet transformation at the first level for audio signal (speech), and computes one of the statistical methods (aad) on the first subband, on the basis of which the subband values are zeroed, followed by compression of those repeated zero values using the RLE algorithm. The results of implementing speech compression on the first three Contourlet transformation levels are recorded in table, with four statistic methods. To determine the best statistic method (IQR,STD,MAD,AAD) as threshold with Contourlet transformation in speech compression of table data, the following equation (Eq13) was proposed, depending on the preference of efficient criterions.
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