ISSN: 2277-3754 ISO 9001:2008 Certified International Journal of Engineering and Innovative Technology (IJEIT) Volume 4, Issue 1, July 2014 45 Abstract— 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. Index Terms—Audio compression, Discrete Cosine Transform, Scalar Quantization, Lossless Coding. I. INTRODUCTION The growth of the computer industry has invariably led to the demand for quality audio data. Compared to most digital data types, the data rates associated with uncompressed digital audio are substantial. For example, if we want send high-quality uncompressed audio data over a modem, it would take each second’s worth of audio about 30 seconds to transmit. This means that the data would be gradually received, stored away and the resulting file played at the correct rate to hear the sound. However, if real-time audio is to be sent over a modem link, data compression is needed. Compression can be considered as a key that reduces the amount of data used to convey the same information [1, 2]. 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 [3]. Transform coding uses a mathematical transformation like FFT and DCT. Jacaba discussed the application of the modified discrete cosine transform (MDCT) to audio compression, specifically the MP3 standard. MDCT plays a very important role in perceptual audio coding [4]. Wang and Vilermo compared the use of Modified Discrete Cosine Transform (MDCT) in audio coding and error concealment with the perspective Fourier frequency analysis [5]. Alvarado and Garcia proposed an efficient joint implementation of DCT, as a method to obtain a sparse audio signal representation, and the application of the compressive sampling algorithm to this sparse signal [6]. Harmanpreet Kaur and Ramanpreet Kaur proposed a speech compression method using different transform techniques. The signal is compressed by DWT technique afterward this compressed signal is again compressed by DCT and then this compressed signal is decompressed using DWT technique. Harmanpreet, K., Ramanpreet 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 [7]. Patil and et al proposed a simple discrete wavelet transform & DCT based audio compression scheme. It was implemented using MATLAB, the experimental results indicated that in general there is improvement in compression factor and signal to noise ratio with DWT based technique [8]. In this paper we introduce a simple DCT transform based coding system to decompose audio signal. DCT is adopted because of its nice de-correlation and energy compaction properties. The resulting transform coefficients are quantized using non uniform scalar quantization. Then, the quantized values are represented using run length encoding (RLE) to prune the long 0's runs, followed by an improved shift coding algorithm. II. AUDIO COMPRESSION SYSTEM The most common characteristic of audio signals is the existence of redundant information lay between the adjacent samples. Compression tries to remove this redundancy and make the data de-correlated. Typical audio compression system contains three basic modules to accomplish audio compression. First, an appropriate transform is applied. Second, the produced transform coefficients are quantized to reduce the redundant information; here, the quantized data hold errors but should be insignificant. Third, the quantized values are coded using packed codes; this encoding stage changes the format of quantized coefficients values using one of the suitable variable length coding technique. Figure (1) shows the layout of the proposed system, the modules of our proposed system are described in the following sections. A. Discrete Cosine Transform (DCT) This transform had been originated by [Ahmed et al. 74]. Since that time it was studied extensively and commonly used in many applications [9]. At present, DCT is widely used transforms in image and video compression algorithms. Its Audio Compression Based on Discrete Cosine Transform, Run Length and High Order Shift Encoding Zainab T. DRWEESH, Loay E.GEORGE