Speech coding: techniques and standards

Recent advances in speech coding algorithms and techniques based on the use of linear prediction now permit high quality voice reproduction at remarkably low bit rates. This paper reviews some of the main ideas underlying the algorithms of major interest today. The concept of removing redundancy by linear prediction is reviewed, first in the context of predictive quantization or DPCM. Then linear predictive coding, adaptive predictive coding, and vector quantization are discussed. The concepts of excitation coding via analysis-by-synthesis linear predictive coding is explained and some important enhancements such as vector sum excitations, and adaptive postfiltering are described. Low-delay coding by backward computation of LPC parameters is explained. The concept of phonetic segmentation of speech for closed-loop coding systems is also presented. Linear prediction is the key technique that underlies almost all of the important algorithms for speech coding of interest today. Finally, we discuss some recent work on nonlinear prediction of speech and its potential for the future of speech coding.

2.4-kb/s speech coding based on pitch-prediction multipulse speech coding is described. An adaptive segmentation procedure to effectively control the renewal rate for synthesis model parameters, a linear time-varying pitch-synthesis filter, a model for multipulse excitation during one pitch period, and a vector quantization for linear predictive coding parameters are introduced. Subjective evaluation demonstrates that pitch-prediction multiphase speech coding can provide much more natural-sounding synthetic speech than single-pulse-excitation speech coding (as with a vocoder) or pitch-interpolation multipulse speech coding at 2.4-kb/s. >

Image compression is the technique of reducing the number of bits required to represent a digital image, which can be accomplished by reducing the redundant and visually irrelevant information present in the images. In this study, a Wavelet Difference Reduction (WDR) coding based image compression technique is proposed which uses Principal Component Analysis (PCA). These techniques are combined in order to achieve high compression ratio with an acceptable degradation in the compressed images. The input image is first compressed using PCA. Few of the Principal Components are used to reconstruct the image. The reconstructed image obtained by PCA is further used as an input data for WDR compression. Since WDR is a wavelet based coding technique so it exploits local characteristics of the image and leads to high Compression Ratio (CR). Here, PCA and WDR are combined because PCA gives high image quality but low CR on the other hand WDR works opposite of it as it gives high CR. The proposed technique is applied on several test images and results are compared with WDR and JPEG2000 techniques. Results clearly show that the proposed technique is better than the existing techniques in terms of both objective and subjective fidelity criteria.

Various experiments that have been carried out for the realization of a combined speech and channel coder to be submitted as a candidate for the standardization of the pan-European half-rate channel are described. The speech coder is based on the code-excited linear prediction (CELP) technique and involves binary regular pulse excitation (RPCELP). Fast codebook search procedures are described, and a fast algorithm for closed-loop long-term prediction is introduced. Fractional pitch analysis can be performed with a small computational load. The channel coder uses a convolutional code with an error detection code for the most sensitive bits. The global solution was designed to reduce the effects of transmission errors. The proposed half-rate codec is real-time implemented in fixed point with a very low complexity. Results indicate that speech quality remains close to that of the full-rate codec for all transmission conditions. >

Two major types of correlations are present in a speech signal. These are known as near-sample redundancies and distant-sample redundancies. Near-sample redundancies are those which are present among speech samples that are close together. Distant-sample redundancies are due to the inherent periodicity of voiced speech. Predictive speech coders make use of these correlations in the speech signal to enhance coding efficiency. In predictive speech coders, the cascade of two nonrecursive prediction error filters process the original speech signal. The formant filter removes near-sample redundancies. The pitch filter acts on distant-sample waveform similarities. The result is a residual signal with little sample to sample correlations. The parameters that are quantized and coded for transmission include the filter coefficients and the residual signal. From the coded parameters, the receiver decodes the speech by passing the quantized residual through a pitch synthesis filter and a formant synthesis filter. The filtering steps at the receiver can be viewed in the frequency domain as first inserting the fine pitch structure and then, shaping the spectral envelope to insert the formant structure. The formant and pitch filters are adaptive in that the analysis to determine the coefficients is carried out frame by frame. Also, the bits representing the quantized parameters are transmitted on a frame by frame basis. The bit rate of the coder is the total number of bits transmitted in one frame divided by the time duration of the analysis frame.

Electronic speech coding had its origin in 1928 when Homer Dudley proposed to send speech signals over a new transatlantic telegraph cable with the (then) enormous bandwidth of 100 Hz. Dudley argued that speech was generated by slowly moving articulators and should therefore require only some 100-Hz total bandwidth for transmission. Since extraction of these parameters proved difficult, Dudley suggested, as an alternative, the transmission of the likewise slowly changing spectral information. The result was the channel vocoder, which (together with formant and voice-excited vocoders) dominated speech coding and that of musical sounds of single-pitch instruments until the mid 1960s. Unhappy with the vocoder's “electronic accent.” Bishnu Atal and the author, in 1967, advanced the idea of linear predictive coding (LPC), motivated by the desire to replace rigid vocoderlike coding by a more flexible method in which a “prediction residual” could represent deviations of real speech signals from an assumed all-pole model. In 1972, subjective error criteria were introduced, based on auditory models of masking and the possibility of “hiding” quantizing noise under the speech signal. The resulting code-excited linear predictive (CELP) coders based on statistical models have been operated at 1/4 bit per sample for the residual at virtually no audible degradation. With powerful digital signal processing chips, CELP has now become the method of choice for synthetic-voice applications. For musical sounds, masking models and linear predictive coding, which is equivalent to prewhitening of the spectrum, lead to a substantial reduction in the required number of bits per sample.

Detection of Emotion by analysis of speech is important for identification of emotional state of person. This can be done using `Linear Predictive Techniques(LPC)', which has different parameters like pitch, vocal tract spectrum, formant frequencies, Duration, MFCC etc. which are used for extraction of features from speech. TEO-CB-Auto-Env is the method which is non-linear method of features extraction. Analysis is done using TU-Berlin (Technical University of Berlin) German database. Here emotion recognition is done for different emotions like neutral, happy, disgust, sad, boredom and anger. Emotion recognition is used in lie detector, database access systems, and in military for recognition of soldiers' emotion identification during the war.

Linear and non-linear prediction techniques of speech signal and the different performance of LPC algorithms are discussed. A new coding plan has been suggested to raise the quantification SNR by about 2 dB by coding the difference of the log-area ratio (LAR) of adjacent frames instead of the LAR. Then, the stable time delay ANN is used to carry out NLP (non-linear prediction) tasks. Experiment results show that only one NLP system is enough to deprive all short-term and long-term correlation from the speech samples and the energy from the output of the NLP system is much smaller than that obtained with the LP system.

Recent standardization efforts have provided motivation for research in the area of wideband speech coding (50 Hz to 7 kHz). Both the European Telecommunications Standards Institute (ETSI) in conjunction with the Third Generation Partnership Project (3GPP), and the ITU-T are currently in the process of evaluating candidate algorithms at bit rates from around 12 kbps to 32 kbps. This paper describes a code-excited linear prediction (CELP) technique used in the Motorola candidate algorithm that is scalable to a wide range of bit rates, thereby allowing the use of a ubiquitous speech model. Preliminary subjective test results indicate that the technique provides very high quality speech that meets or exceeds all requirements for both the ETSI/3GPP and ITU-T wideband speech standardization efforts.

The paper presents a speech generative model that provides an efficient way of generating speech waveform from its amplitude spectral envelopes. The model is based on hybrid speech representation that includes deterministic (harmonic) and stochastic (noise) components. The main idea behind the approach originates from the fact that speech signal has a determined spectral structure that is statistically bound with deterministic/stochastic energy distribution in the spectrum. The performance of the model is evaluated using an experimental low-bitrate wide-band speech coder. The quality of reconstructed speech is evaluated using objective and subjective methods. Two objective quality characteristics were calculated: Modified Bark Spectral Distortion (MBSD) and Perceptual Evaluation of Speech Quality (PESQ). Narrow-band and wide-band versions of the proposed solution were compared with MELP (Mixed Excitation Linear Prediction) speech coder and AMR (Adaptive Multi-Rate) speech coder, respectively. The speech base of two female and two male speakers were used for testing. The performed tests show that overall performance of the proposed approach is speaker-dependent and it is better for male voices. Supposedly, this difference indicates the influence of pitch highness on separation accuracy. In that way, using the proposed approach in experimental speech compression system provides decent MBSD values and comparable PESQ values with AMR speech coder at 6,6 kbit/s. Additional subjective listening testsdemonstrate that the implemented coding system retains phonetic content and speaker’s identity. It proves consistency of the proposed approach.

A lot of attentions has been paid by the researchers for lossy image compression. Due to the high uses of multimedia, a high image compression ratio while keeping good image quality, is still a research issue. In this paper, we proposed an image compression using adaptive scanning, in which DWT(discrete wavelet transform) is performed to source image, DCT(discrete cosine transform) is performed to obtained DWT elements. Next, non zero quantized DCT (discrete cosine transform) coefficients are represented in less number of bits. First, the source image is divided into \(16\times 16\) grids and then DWT transformed, later the obtained elements are divided into \(8\times 8\) grids and then DCT is performed, quantized, differential encoded and an indexed vector is formed from the quantized matrix using adaptive scanning. Furthermore the indexed vector is divided into two indexed vectors namely \(nonzero\) vector and zerocount vector. The nonzero coefficients are stored in \(nonzero\) vector and the number of zeros preceding a nonzero coefficient is stored in zerocount vector. A new block is introduced to store the number of bits required to store highest value of nonzero coefficient in \(nonzero\) vector so that all the nonzero elements can be represented in that many bits rather than a fixed number of bits. The experimental results show that the proposed method has achieved more compression ratio with slight reduction in image quality as compared to existing recent methods.

The past two decades have witnessed a rapid expansion within the telecommunications industry. This growth has been primarily motivated by the proliferation of digital communication systems and services which have become easily available through wired and wireless systems. Current research trends involve the integration of speech, audio, video and data channels into true multimedia communications over fixed and mobile networks. However, while the available bandwidth in wired terrestrial networks is relatively cheap and expandable, it becomes a limited resource in satellite and cellular-radio systems. In order to accommodate an ever growing number of users while maintaining high quality and low operational costs, it is necessary to maximise spectral efficiency. This has given rise to the development of high rate compression techniques with the ability to adapt to a broad class of input signals and to varying network resources. The research carried out in this thesis has mainly focused on the design of a single algorithm for compressing speech and audio signals sampled at different rates. The algorithms are based on the analysis-by-synthesis linear prediction coding (AbS-LPC) scheme, which has been widely employed in various speech coding standards. However, this bit rate reduction technique is based on the speech production mechanism and as such provides a rigid structure which presents a major limitation for audio coding. In order to improve the audio quality at low rates and to compensate for the errors incurred by the linear prediction during segments of high transitions, the algorithms employ an efficient pulse excitation structure which represents the short innovation sequences with sparse unit magnitude pulses. The scheme proposed for the compression of telephone bandwidth speech and audio signals at 12kb/s achieves similar quality to the G.728 coder at 16kb/s and higher audio quality than the GSM-EFR standard at 12.2kb/s. Wideband speech and audio coding schemes have been designed using both the fullband approach at bit rates of 17 and 19kb/s and also the split band technique at a bit rate of 20kb/s. The perceptual quality is comparable to the G.722 coder operating at 48kb/s. The subband decomposition technique is also adapted to code speech and audio signals sampled at 32kHz. The quality of the coder at 28kb/s is similar to the quality achieved by the MP3 coder at 32kb/s. The algorithm also provides bandwidth and bit rate scalability ranging from 12 to 64kb/s, making it ideal for deployment in rate-adaptive communication systems.

The goal of this work is to provide a brief review on the image compression algorithms in the wireless capsule endoscopy, which is efficient and simple. This paper also covers the review on each stage of the image compression algorithms and its efficiency. At the latter part of this paper, an ideal algorithm for the image compression with high compression ratio and good image reconstruction quality. The main complication associated with the wireless endoscopy system is the reduction in area and consumed power while preserving the acceptable reconstruction of video with high quality. Most of the existing algorithms are based on DCT algorithms which require more buffer space; thereby it increases the large area and large power for the computation. In this paper, the different data compressor based on DCT is presented. The ideal scheme is based on the subsampling of data. Special features and characteristics of endoscopic images are considered in the stage of algorithm development. The algorithm includes the compression technique differential pulse coded modulation (DPCM) which is lossless followed by simple, efficient and variable length encoding called Golomb-Rice coding. The proposed ideal image compressor is compatible with any image sensors which are low power and commercially available that outputs the pixels of the images in a raster scan manner. It will evict the need of the memory buffer and temporary storage needed in the transform based coding methods. Additional compression acquired by the introduction of the corner clipping algorithm to remove the distorted corners present in the endoscopic images. Compared to other algorithms aims at wireless capsule endoscopy, the proposed ideal algorithm is highly efficient with a high compression ratio as well as very high reconstruction peak signal-to-noise ratio.

The goal of compression techniques is to reducing the size of data and decreasing the communication cost while transferring data. Fractal based coding technique is widely used to compress images files which provides high compression ratio and good image quality. However, like a compression technique, it is still limited because of the difference of the human perceptions between audio and image files, the long time for searching the best possible domain blocks and many comparisons in the encoding process. For those reasons, Fractal Coding had not broadly studied on audio data. Few years ago, Fractal Coding has been extended to apply on the audio data. In this paper, the application of the Fractal Coding on different types of audio files is investigated. Moreover, the effect of block length on the audio quality and compression performance are highlighted since block length is considered the main factor in the Fractal Coding algorithm. A GTZAN dataset is adopted in the evaluation and the experimental results show that there is an inverse relationship between block length and audio quality and proportional relationship between block length and compression ratio and factor. Furthermore, it can be noticed that the Fractal Coding can be compressed any speech and music audio signal directly with acceptable quality, PSNR 39 dB on average with a high compression ratio around 90 % with compression factor around 10 when the block length is 20 samples.

For speech coders which fall within the class of waveform coders, the reconstructed signal approaches the original with increasing bit rate. In such coders, the distortion criterion generally operates on the speech signal or a signal obtained by adaptive linear filtering of the speech signal. To satisfy computational and delay constraints, the distortion criterion must be reduced to a very simple approximation of the auditory system. This drawback of conventional approaches motivates a new speech coding paradigm in which the coding is performed in a domain where the single-letter squared-error criterion forms an accurate representation of perception. The new paradigm requires a model of the auditory periphery which is accurate, can be be inverted with relatively low computational effort, and which represents the signal with relatively few parameters. We develop such a model of the auditory periphery and discuss its suitability for speech coding. The results indicate that the new paradigm in general and our auditory model in particular form a promising basis for the coding of both speech and audio at low bit rates.