— Audio compression has become one of the basic technologies and has been the subject of much res... more — Audio compression has become one of the basic technologies and has been the subject of much research and experimentation throughout the last two decades. The need to compress audio data is motivated by both storage requirements and transmission requirements. The purpose of this research is to design and implement a low complexity and efficient audio coding scheme based on Biorthogonal tab 9/7 wavelet filter. The proposed system consists of the audio normalization, followed by wavelet (Tap 9/7), progressive hierarchal quantization, modified run length encoding, and finally high order shift coding to produce the final bit stream. To reduce the effect of quantization noise, which is notable at the low energetic segments of the audio signal, a post processing filtering stage is introduced as final stage of the decoding processes. The efficiency performance of the suggested audio encoding methods has been measured using peak signal to noise (PSNR) ratio and compression ratio (CR). The attained results indicated that compression performance of the system is promising; it achieved better results than the DCT based. The compression ratio is increased with the increase of number of passes. Also the post processing stage improved the subjective quality of the reconstructed audio signal. Also, it improved the fidelity level of reconstructed audio signal when PSNR is less than 38 Db. Keywords— Audio Compression, Biorthogonal tab 9/7 wavelet filter, Hierarchal Quantization, Lossless Coding I. INTRODUCTION The need to compress media data is motivated by both storage requirements and transmission requirements. In earlier days of the information age, disk space was limited and expensive, and bandwidth was limited, so compression was a must [1]. Data compression is popular for two reasons: (1) people like to accumulate data and hate to throw anything away, no matter how big a storage device one has, sooner or later it is going to overflow, data compression seems useful because it delays this inevitability and (2) people hate to wait a long time for data transfers. When sitting at the computer, waiting for a Web page to come in or for a file to download, we naturally feel that anything longer than a few seconds is a long time to wait [2]. The need for audio compression algorithms that can satisfy simultaneously the conflicting demands of high compression ratios and transparent quality for high fidelity audio signals led to the establishment of several coding methodologies over the last two decades. In general, audio compression schemes employ design techniques that exploit both perceptual irrelevancies and statistical redundancies. The most popular audio coders are based on using two techniques (i.e., sub-band coding and transform coding). Sub-band coding splits signal into a number of sub-bands, using band-pass filter like wavelet transform [3]. Transform coding uses a mathematical transformation like FFT, and DCT. Considerable interest has arisen in recent years regarding wavelet as a new transform technique for both image and audio processing applications. Like other transform coding techniques, wavelet coding is based on the idea that the coefficients of a transform decorrelates the samples values of an audio signal and can be coded more efficiently than the original samples values themselves [4], such that most of the signal energy is concentrated in a small fraction of samples. For surveying the problem of improving audio coding (compression), several algorithms have been developed. Khalifa et al [5] proposed audio compression using wavelet transform to achieve transparent coding of audio and speech signals at the lowest possible data rates. Their wavelet based compression system reached compression ratio 1.88 with signal to noise ratio 34.5 dB when using the Daubechies-10 wavelet. Also, Dhubkarya and Dubey [4] have presented a new high quality audio codec at low bit rate using wavelet transform and made improvement in reconstructed wave using post filtering. Harmanpreet Kaur and Ramanpreet Kaur [6] proposed a speech compression method using different transform techniques. The signal is compressed using DWT technique afterward this compressed signal is again compressed by DCT and then this compressed signal is decompressed using DWT technique. They have investigated the use of DWT & DCT as analysis tools for speech signal coding; they used Peak Signal to Noise Ratio and Normalized Root Mean Square Error (NRMSE) to evaluate the effectiveness of different filters of wavelet family. Patil and et al [7] proposed a simple audio compression scheme based on discrete wavelet transform & DCT. They implemented it using MATLAB, the experimental results indicated that in general there is improvement in compression gain and signal to noise ratio with DWT based technique.
— Audio compression addresses the problem of reducing the amount of data required to represent di... more — 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.
— Audio compression has become one of the basic technologies and has been the subject of much res... more — Audio compression has become one of the basic technologies and has been the subject of much research and experimentation throughout the last two decades. The need to compress audio data is motivated by both storage requirements and transmission requirements. The purpose of this research is to design and implement a low complexity and efficient audio coding scheme based on Biorthogonal tab 9/7 wavelet filter. The proposed system consists of the audio normalization, followed by wavelet (Tap 9/7), progressive hierarchal quantization, modified run length encoding, and finally high order shift coding to produce the final bit stream. To reduce the effect of quantization noise, which is notable at the low energetic segments of the audio signal, a post processing filtering stage is introduced as final stage of the decoding processes. The efficiency performance of the suggested audio encoding methods has been measured using peak signal to noise (PSNR) ratio and compression ratio (CR). The attained results indicated that compression performance of the system is promising; it achieved better results than the DCT based. The compression ratio is increased with the increase of number of passes. Also the post processing stage improved the subjective quality of the reconstructed audio signal. Also, it improved the fidelity level of reconstructed audio signal when PSNR is less than 38 Db. Keywords— Audio Compression, Biorthogonal tab 9/7 wavelet filter, Hierarchal Quantization, Lossless Coding I. INTRODUCTION The need to compress media data is motivated by both storage requirements and transmission requirements. In earlier days of the information age, disk space was limited and expensive, and bandwidth was limited, so compression was a must [1]. Data compression is popular for two reasons: (1) people like to accumulate data and hate to throw anything away, no matter how big a storage device one has, sooner or later it is going to overflow, data compression seems useful because it delays this inevitability and (2) people hate to wait a long time for data transfers. When sitting at the computer, waiting for a Web page to come in or for a file to download, we naturally feel that anything longer than a few seconds is a long time to wait [2]. The need for audio compression algorithms that can satisfy simultaneously the conflicting demands of high compression ratios and transparent quality for high fidelity audio signals led to the establishment of several coding methodologies over the last two decades. In general, audio compression schemes employ design techniques that exploit both perceptual irrelevancies and statistical redundancies. The most popular audio coders are based on using two techniques (i.e., sub-band coding and transform coding). Sub-band coding splits signal into a number of sub-bands, using band-pass filter like wavelet transform [3]. Transform coding uses a mathematical transformation like FFT, and DCT. Considerable interest has arisen in recent years regarding wavelet as a new transform technique for both image and audio processing applications. Like other transform coding techniques, wavelet coding is based on the idea that the coefficients of a transform decorrelates the samples values of an audio signal and can be coded more efficiently than the original samples values themselves [4], such that most of the signal energy is concentrated in a small fraction of samples. For surveying the problem of improving audio coding (compression), several algorithms have been developed. Khalifa et al [5] proposed audio compression using wavelet transform to achieve transparent coding of audio and speech signals at the lowest possible data rates. Their wavelet based compression system reached compression ratio 1.88 with signal to noise ratio 34.5 dB when using the Daubechies-10 wavelet. Also, Dhubkarya and Dubey [4] have presented a new high quality audio codec at low bit rate using wavelet transform and made improvement in reconstructed wave using post filtering. Harmanpreet Kaur and Ramanpreet Kaur [6] proposed a speech compression method using different transform techniques. The signal is compressed using DWT technique afterward this compressed signal is again compressed by DCT and then this compressed signal is decompressed using DWT technique. They have investigated the use of DWT & DCT as analysis tools for speech signal coding; they used Peak Signal to Noise Ratio and Normalized Root Mean Square Error (NRMSE) to evaluate the effectiveness of different filters of wavelet family. Patil and et al [7] proposed a simple audio compression scheme based on discrete wavelet transform & DCT. They implemented it using MATLAB, the experimental results indicated that in general there is improvement in compression gain and signal to noise ratio with DWT based technique.
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