Speech Compression Analysis Using Matlab

Choice Reviews Online

The growth of the cellular technology and wireless networks all over the world has increased the demand for digital information by manifold. This massive demand poses difficulties in handling huge amounts of data that need to be stored and transferred. To overcome this problem we can compress the information by removing the redundancies present in it. Redundancies are the major source of generating errors and noisy signals. Coding in MATLAB helps in analyzing compression of audio signals with varying bit rate and remove errors and noisy signals from the audio signals. The audio signal's bit rate can also be reduced to remove errors and noisy signals which are suitable for remote broadcast lines, studio links, satellite transmission of high quality audio and voice over internet. This paper focuses on the audio compression process and its analysis using DWT and RLE Techniques through MATLAB by which processed audio signal can be heard with clarity and in the noiseless mode at the receiver end.

2011

Abstract: This paper deals with speech compression based on Linear predictive coding, Discrete wavelet transforms and Wavelet packet transform. We used Malayalam (one of south Indian language) for this experiment. We could successfully compress and reconstructed Malayalam spoken words with perfect audibility by using both waveform coding and parametric coding.

The International Arab …, 2003

With the growth of the multimedia and cellular technology over the past decades, the demand for digital information increases dramatically. This enormous demand poses difficulties for the current technology to handle speech compression. One approach to overcome this problem is to compress the information by removing the redundancies present in it. This is a lossy compression scheme that is often used to compress information such as speech signals. This paper presents a new lossy algorithm to compress speech signals using Discrete Wavelet Transform (DWT) Techniques to solve the limited bandwidth problem facing the Palestinian cellular company, Jawwal. The performance of the DWT for speech compression is very good compared with other techniques such as µ-law speech coder. Moreover, the compression ratio using Wavelet can be varied easily, while other techniques have fixed one.

2019

Speech compression is required for storing and transmission of speech signals at low bit rates which is needed for wireless communication. In this paper, ten speech signals of female voice are recorded with sampling frequency of 8 kHz and using monochannel in Praat software. In the first part of the paper, wavelet transform is performed on these signals using MATLAB software. It is found that the percentage of zero coefficients after six levels of wavelet decomposition in the speech signal is very high and those can be discarded without much loss of information. The decomposed signal is again reconstructed in MATLAB, and compared with the original signal, the distortion is found to be very less (≈10−5). In the second part of the paper, these same signals are quantized by uniform quantization and also compressed by µ-law companding. After compression, these coefficients are quantized. The q-levels with and without companding are compared. The signals are reconstructed by µ-law expand...

TJPRC, 2013

A novel transformation technique for speech compression is proposed to improve the compression ratio without loss of intelligibility of speech. This technique is a combination of analysis-synthesis and sub-band coding technique. The proposed technique is compared with conventional techniques like Linear Predictive Coding (LPC) and Discrete Wavelet Transform (DWT). First, a speech encoder at lower bit rates (LPC) is used to analyze the speech signals. Levinson Durbin Algorithm is used to reproduce the important characteristics of the input speech in LPC analysis. Later, DWT is used to analyze the speech signal. The comparative analysis was done for the proposed, conventional and transformation techniques in terms of qualitative speech parameters like Mean Squared Error (MSE), Peak Signal to Noise Ratio (PSNR).

2020

Noise is an ever present phenomenon while dealing with recording devices, be it digital or analog, be it specks in images or background hiss in music recordings. Therefore, this paper aims at ways of reducing the effects of these forms of noise on image and sound files that can be compressed and sent through a communication channel. This noise reduction mechanism is represented in the form of algorithms and combined with data compression to minimize the effects of noise, while images and sounds are transmitted with their original content undistorted.

Generic data compression algorithms is an area of digital processing that is focusing on reducing bit rate of the speech signal for transmission or storage without significant loss of quality. The focus of this paper is to compress the digital speech using wavelet transform. The main idea behind the generic data compression algorithms is to represent uncompressed speech with minimum number of bits and optimum speech quality. Wavelet transform has been recently proposed for signal analysis. The wavelet transform is useful to remove redundancies and irrelevancies present in the speech signal for the compact representation. The classical subband coding method also known as filter bank method is used in which signal is decomposed into basis of wavelet functions using high pass and low pass filters. Speech coding is a lossy scheme and is implemented here to compress one-dimensional speech signal. Basically, this scheme consists of three operations which are the transform, threshold techniques (by level and global threshold), and run-length encoding operations. Finally the compressed signal is reconstructed. The generic data compression algorithms using filter bank method is implemented and simulated by using wavelet transform algorithm for different wavelets such as Haar, Daubechies series (Daub-4, Daub-6, Daub-8, Daub-12, Daub-16 Daub-32 Daub-64) and bi-orthogonal wavelet.

2013

Speech compression is one area of digital signal processing that focusing on reducing the bit rate of the speech signal for transmission or storage without significant loss of quality. This paper presents a new design feature for speech Compression using Wavelet Packet Transform and Linear prediction Coding. The proposed algorithm uses best tree decomposition of wavelet packets, which develop features afterward compressed signal is compressed by Linear prediction Coding. The 4 points encoded vector is a full of information just like the original best tree’s structure. The implied scoring system makes BTE suitable for compression problems and we reach at 12 compression ratio. The performance of speech signal is measure on the basis of signal to noise ratio (SNR), mean square error (MSE) and peak signal to noise ratio (PSNR).

In modern world, communication service providers are continuously met with the challenge of accommodating more users with in a limited allocated bandwidth. Due to this motivation service providers and manufactures are continuously in search of low bit rate speech coders that deliver high quality speech. The main objective of speech compression is to reduce the bits rate of speech for communication or storage without significant loss of its intelligibility. Wavelet based speech coding is a new technique used for speech compression. The main issue regarding wavelet based speech coding is to choose optimal wavelet to transform the speech signal. In this experiment we use two types of wavelets orthogonal and biorthogonal wavelets to transform the speech signal.Run length encoding algorithm is used to encode the threshold coefficient. Performance of this experiment is evaluated in terms of MSE, SNR, PSNR, RSE, and in terms of compression percentage