A Theory of Lossless Compression of High Quality Speech Signals with Comparison (original) (raw)
Related papers
2010
This paper presents a theory of lossless digital compression. Quality of voice signal is not important for voice communication. In hearing music high quality music is always recommended. For this emphasis is given on the quality of speech signal. To save more music it is needed to save them consuming smaller memory space. In proposed compression 8-bit PCM/PCM speech signal is compressed. When values of samples are varying they are kept same. When they are not varying the number of samples containing same value is saved. After compression the signal is also an 8bit PCM/PCM. MPEG-4 ALS is applied in this compressed PCM signal for better compression.
2009 International Conference on Future Computer and Communication, 2009
This research work reveals that Voice Signal Compression (VSC) is a technique that is used to convert the voice signal into encoded form when compression is required, it can be decoded at the closest approximation value of the original signal. This work presents a new algorithm to compress voice signals by using an "Adaptive Wavelet Packet Decomposition and Psychoacoustic Model". The main goals of this research work is to transparent compression (48% to 50%) of high quality voice signal at about 45 kbps with same extension (i.e. .wav to .wav), second is evaluate compressed voice signal with original voice signal with the help of distortion and frequency spectrum analysis and third is to compute the signal to noise ratio (SNR) of the source file.For this, a filter bank is used according to psychoacoustic model criteria and computational complexity of the decoder. The bit allocation method is used for this which also takes the input from Psychoacoustic model. Filter bank structure generates quality of performance in the form of subband perceptual rate which is computed in the form of perceptual entropy (PE). Output can get best value reconstruction possible considering the size of the output existing at the encoder. The result is a variable-rate compression scheme for high-quality voice signal. This work is well suited to high-quality voice signal transfer for Internet and storage applications.
A new speech compression method
2004
In this paper a new speech compression method is presented. The traditional speech compression method is based on linear prediction. The compression method, proposed in this paper, is based on the use of an orthogonal transform, the discrete cosine packets transform. This method is well suited for the speech processing, taking into account the sine model of this kind of signals and because this transform converges asymptotically to the Karhunen-Loève transform. After the computation of the discrete cosine packets transform, the coefficients obtained are processed with a threshold detector, who keeps only the coefficients superior to a given threshold. This way the number of non zero coefficients is reduced doing the compression. The next block of the compression system is the quantization system. This is build following the speech psycho-acoustic model. The proposed compression method is transparent, the compression rate obtained is important and the operations number and the memory volume used are not very high.
This chapter presents an introduction to speech compression techniques, together with a detailed description of speech/audio compression standards including narrowband, wideband and fullband codecs. We will start with the fundamental concepts of speech signal digitisation, speech signal characteristics such as voiced speech and unvoiced speech and speech signal representation. We will then discuss three key speech compression techniques, namely waveform compression, parametric compression and hybrid compression methods. This is followed by a consideration of the concept of narrowband, wideband and fullband speech/audio compression. Key features of standards for narrowband, wideband and fullband codecs are then summarised.
1993
Compared to most digital data types, with the exception of digital video, the data rates associ-ated with uncompressed digital audio are substan-tial. Digital audio compression enables more effi-cient storage and transmission of audio data. The many forms of audio compression techniques offer a range of encoder and decoder complexity, compressed audio quality, and differing amounts of data com-pression. The -law transformation and ADPCM coder are simple approaches with low-complexity, low-compression, and medium audio quality algo-rithms. The MPEG/audio standard is a high-complexity, high-compression, and high audio qual-ity algorithm. These techniques apply to general au-dio signals and are not specifically tuned for speech signals.
A Design of Lossless Compression for High-Quality Audio Signals
2000
Three extension tools for extending and enhancing the com- pression performance of prediction-based lossless audio coding are proposed. The first extension aims at supporting floating- point data input in addition to integer PCM data. The sec- ond is progressive-order prediction of the starting samples at each random-access frame, where the information on previous frame is not available. The third is
Speech Data Compression using Vector Quantization
Mostly transforms are used for speech data compressions which are lossy algorithms. Such algorithms are tolerable for speech data compression since the loss in quality is not perceived by the human ear. However the vector quantization (VQ) has a potential to give more data compression maintaining the same quality. In this paper we propose speech data compression algorithm using vector quantization technique. We have used VQ algorithms LBG, KPE and FCG. The results table shows computational complexity of these three algorithms. Here we have introduced a new performance parameter Average Fractional Change in Speech Sample (AFCSS). Our FCG algorithm gives far better performance considering mean absolute error, AFCSS and complexity as compared to others.
Improving audio data quality and compression
Emerging Technologies, 2008. …, 2008
High data quality at low bit rate is an essential goal that people want to achieve. It is necessary to transfer data at low bit rate so that the bandwidth of the medium can be utilized efficiently. In most of the speech coding techniques the goal of low bit rate transfer is achieved but the data quality is affected badly. The proposed technique is an attempt to improve the data quality at low bit rate as well as fast transmission of data. The proposed technique protects the data quality by applying Linear Predictive Coding-10 and achieves the low bit rate by applying Quadrature Mirror Filter. A comprehensive analysis is on the basis of given parameters as size, compression time, Signal to Noise Ratio, power, energy, power in air, energy in air, mean, standard deviation and intensity.
SPEECH COMPRESSION ANALYSIS USING MATLAB
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 for 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 speech signals with varying bit rate and remove errors and noisy signals from the speech signals. Speech signal's bit rate can also be reduced to remove error and noisy signals which is suitable for remote broadcast lines, studio links, satellite transmission of high quality audio and voice over internet This paper focuses on speech compression process and its analysis through MATLAB by which processed speech signal can be heard with clarity and in noiseless mode at the receiver end .