Audio compression addresses the problem of reducing the amount of data required to represent digital audio. It is used for reducing the redundancy by avoiding the unnecessary duplicate data. In the present work a low complexity and efficient coding scheme based on discrete cosine transform (DCT) is proposed. The proposed system consists of audio normalization, followed by DCT transform, scalar quantization, improved run length encoding and a new high order shift coding. To reduce the effect of quantization noise, which is notable at the low energetic audio segments, a post processing filtering stage is introduced as the final stage of decoding process. The system performance is tested using different audio test samples; the test samples have different size and different in audio signal characteristics. The compression performance is evaluated using peak signal to noise (PSNR) ratio and compression ratio (CR). The test results indicated that the compression performance of the system is promising. The compression ratio is increased with the increase of block size. Also the post processing stage improved the fidelity level of reconstructed audio signal.

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Audio Compression Based on Discrete Cosine Transform, Run Length and High Order Shift

Digital audio is required to transmit large sizes of audio information through the most common communication systems; in turn this leads to more challenges in both storage and archieving In this paper, an efficient audio compressive scheme is proposed, it depends on combined transform coding scheme; it is consist of i) bi-orthogonal (tab 9/7) wavelet transform to decompose the audio signal into low & multi high sub-bands, ii) then the produced sub-bands passed through DCT to de-correlate the signal, iii) the product of the combined transform stage is passed through progressive hierarchical quantization, then traditional run-length encoding (RLE), iv) and finally LZW coding to generate the output mate bitstream The measures Peak signal-to-noise ratio (PSNR) and compression ratio (CR) were used to conduct a comparative analysis for the performance of the whole system Many audio test samples were utilized to test the performance behavior; the used samples have various sizes and vary in features The simulation results appear the efficiency of these combined transforms when using LZW within the domain of data compression The compression results are encouraging and show a remarkable reduction in audio file size with good fidelity

/pdf/audio-compression-using-transforms-and-high-order-entropy-2psc7vtqp8.pdf

Audio compression using transforms and high order entropy encoding

In this paper, a novel algorithm based on Discrete Wavelet Transform has been proposed for good compression ratios. In proposed algorithm, the audio samples of the signal are first decomposed into N equal frames and decomposed signal is processed to remove nonzero coefficients which significantly reduce the size of audio signal. The signal is then thresholded and psychoacoustically modeled to obtain the tonal and non-tonal components. Further, quantization is performed on the masked signal. In the end, the signal is reconstructed and various parameters are studied. In this paper, best compression ratio is achieved with db2 wavelet at level 4 and signals are analyzed on the basis of mathematical parameters viz. SNR, distortion. This has been implemented on MATLAB 7.1 R2009 on an Intel i-5 processor based machine.

A novel compression algorithm using DWT

This paper proposes a new approach to Audio Compression that incorporates lossless text compression algorithm. The purpose of Audio Compression is to reduce the amount of data required to represent the digital audio by removing redundant data. In the present work Discrete Cosine Transform and lossless text compression method (Lempel-Ziv-Welch method) based Audio Compression algorithm has been proposed which also includes audio normalization, scalar quantization and encoding. The LZW dictionary obtained after compression is used for transmission and storage. The performance of the proposed algorithm is analyzed using various audio specimens of distinct size with distinct audio signal parameters. The compression performance is assessed using Peak Signal to Noise Ratio (PSNR) and Compression Ratio (CR). The audio specimen outcomes indicate the propitious performance of proposed algorithm. The compression ratio can be enhanced by changing various parameters of the system.

Audio Compression Algorithm using Discrete Cosine Transform (DCT) and Lempel-Ziv-Welch (LZW) Encoding Method

The ultimate aim of this work is to enhance an efficient Tamil speech word recognition system for recognition speech words to determine the authorized and unauthorized persons. The recognition of Tamil speech words from number of spoken words in the database shows a unique challenge as these words include noise and therefore classifying Tamil words is very complex. The recognition is an essential step in Tamil speech words to acquire quantitative measurement such as the speech word recognition rate. The main intention of speech word recognition system is to augment the quantity of recognition rate and lessen the wrong recognition. As there is deficiency of Tamil speech word recognition systems for identifying the number of speech words from various peoples, some modern recognition scheme are implemented. The first projected Mel Frequency Cepstral Coefficients-Hidden Markov Model (MFCC-HMM) recognition system for recognizing Tamil speech words is encompassed of three major phases. In the first preprocessing phase, the database Tamil speech words noises are eliminated by Gaussian filtering technique. Subsequently, the noise free speech words are used in the feature extraction process. In feature extraction MFCC features are obtained from the speech words. The HMM is used to execute the training process and to recognize the speech words. The analyzing process check out whether the given testing database words are recognized or unrecognized. Even though the first model accomplishes higher rate of accuracy in speech word recognition, the sensitivity and other measures still need to be developed. So the second method is proposed in order to attain the higher sensitivity and other measures. The second proposed Mel Frequency Cepstral Coefficients-Feed Forward Back Propagation Neural Network (MFCC-FFBNN) system illustrates the phases of preprocessing, feature extraction and recognition by FFBNN for

Spoken Tamil word Recognition System

Audio compression has become one of the basic technologies of the multimedia age. The change in the telecommunication infrastructure, in recent years, from circuit switched to packet switched systems has also reflected on the way that speech and audio signals are carried in present systems. In many applications, such as the design of multimedia workstations and high quality audio transmission and storage, the goal is to achieve transparent coding of audio and speech signals at the lowest possible data rates. In other words, bandwidth cost money, therefore, the transmission and storage of information becomes costly. However, if we can use less data, both transmission and storage become cheaper. Further reduction in bit rate is an attractive proposition in applications like remote broadcast lines, studio links, satellite transmission of high quality audio and voice over internet

http://ijirset.com/upload/may/61_Audio.pdf

Ankita Varma

Papers

Audio and Speech Compression Using DCTand DWT Techniques

Self reported benefits of participating in group prayer in a hospital outpatient setting: A cross-sectional observational study.