High quality audio coding using analysis-by-synthesis technique

Linear predictive coding (LPC) parameters are widely used in various speech coding applications for representing the short-time spectral envelope information of speech [1]. For low bit rate speech coding applications, it is important to quantize these parameters using as few bits as possible. Considerable workhas been done in the past to develop both scalar and vector quantization procedures to quantize the LPC parameters [2, 3,4]. Scalar quantizers quantize each of the LPC parameters independently, while vector quantizers consider the entire set of LPC parameters as an entity and allow for direct minimization of quantization distortion. Because of this, the vector quantizers result in smaller distortion than the scalar quantizers at any given bit rate. The vector quantizers, however, have one major problem; their computational complexity is high. In our earlier paper [3], we have reported on a vector quantizer where the LPC parameter vector is split in the line spectral frequency (LSF) domain to overcome this complexity problem. We have shown that this quantizer can quantize the LPC parameters at 24 bits/frame with an average spectral distortion of 1 dB, less than 2% frames having spectral distortionl in the range 2-4 dB and no frame having spectral distortion greater than 4 dB.

The authors explore the benefits of time-varying bit allocation to excitation and LPC (linear predictive coding) parameters for the case of codebook-excited LPC. The overall bit rate in the experiment was 4.8, 6.4, or 8.0 kb/s. In each case, permissible bit rates for the LPC component were 0, 24, 36, or 48 bits per frame, one of which was selected for each speech frame using a brute-force search maximum performance. Average SNR gains over conventional time-invariant methods were modest, on the order of 1 to 2 dB, but gains for certain speech segments were as high as 3 to 5 dB. Perceptually, gains due to variable bit allocation were most noticeable in the 6.4 kb/s system, especially with female speakers. However, even in this case, the benefits of flexible bit allocation were somewhat offset by distortions due to other inadequacies in the coding algorithm. >

An algorithm for LPC (linear predictive coding) parameter optimization in multipulse (MP)-LPC based speech coders is presented. It is shown that, by taking into account the nature of the MP-excitation signal into LPC parameter computation, it is possible to improve the effectiveness of the LPC model. This results in a better quality of the reconstructed signal in terms both of objective and subjective criteria. The implementation details of the algorithm are discussed and experimental results are presented. In particular a comparison with standard MP-LPC techniques is given. >

A class of reverberant speech enhancement techniques involve processing of the linear prediction residual signal following Linear Predictive Coding (LPC). These approaches are based on the assumption that reverberation is mainly confined to the prediction residual and affects the LPC coefficients to a lesser extent. This chapter begins with a study on the effects of reverberation on the LPC parameters where mathematical tools from statistical room acoustics are used in the analysis. Consequently, a general framework for dereverberation using LPC is formulated and several existing methods utilizing this approach are reviewed. Finally, a specific method for processing a reverberant prediction residual is presented in detail. This method uses a combination of spatial averaging and larynx cycle-based temporal averaging. Experiments with a microphone array in a small office demonstrate the dereverberation and noise suppression of the spatiotemporal averaging method, showing up to a 5 dB improvement in segmental SRR and 0.33 in the normalized Bark spectral distortion score.

A cascade formant model is well suited to describe certain speech segments, such as vowels and vowel-like sounds. The formant model is also useful because the relationship between formants and the vocal tract configurations are well understood; however, this model is not adequate for other speech sounds, such as stops, fricatives, nasals, etc. On the other hand, LPC (linear predictive coding) analysis, of sufficiently high order, can adequately describe the spectrum of any speech sound, but the relationship between the LPC parameters and the spectrum or vocal tract configuration is not obvious. This paper describes a speech analysis/synthesis scheme that uses both formants and LPC parameters for different sections of a speech signal. Thus, in some regions, the benefit of the formant model can be utilized, while in other regions, the LPC representation can be used to obtain a good description of the speech spectrum. The analysis algorithm resolves the problem of discontinuities that arise from using the two different spectral representations. When provided with the same multipulse signal, the method of speech analysis described in this paper produces resynthesized speech of the quality of multipulse LPC.

This paper presents a class of quadratically weighted distortion measures which provide optimal performance for the high rate vector quantization (VQ) of linear predictive coding (LPC) parameters. It is shown that the quantization distortion of a high rate VQ converges to a quadratically weighted measure, where the quadratic weighting matrix is a matrix, which is a generalization of the scalar sensitivity concept to the vector case. The sensitivity matrix is the second order term of the Taylor series expansion of the original distortion measure. Closed form expressions and computationally efficient methods for computing the sensitivity matrices of the different LPC parameterizations are given, which involve no numerical integration and can be implemented in real-time on modern DSP chips. In the general case, the sum of sensitivity weighted scalar errors is not equivalent to the original distortion measure. However the sensitivity matrix of the line spectral pair (LSP) frequencies is exactly diagonal, demonstrating that for LSPs only a sum of sensitivity weighted scalar errors will result in optimal performance.

The authors describe an implementation of fast algorithms for codebook searching used in code-excited linear predictive (CELP) speech coding. They present details of the algorithms including (i) fast conversion of line spectrum pair parameters to linear predictive coding parameters, and (ii) fast searches of the parameters of adaptive and stochastic codebooks. The description can be readily used for CELP implementations, such as on (i) high-quality low-bit-rate speech transmission in point-to-point or store-and-forward mode, and (ii) efficient speech storage in speech recording and multimedia databases. The algorithms have been implemented on an IBM-PC AT equipped with a TMS320C30 module and a SUN Sparcstation 10 for the development of packet radio speech transmission and voice mail applications, respectively.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

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.

We describe a new method for extracting the voice source and vocal tract impulse response from voiced speech for glottal excited linear predictive speech coding. The method utilises a mixed phase model of speech and dynamic gain control to deconvolve speech signals into source and filter components. Voiced speech segments are analysed for pitch synchronously and coded with a glottal codebook of 32 entries, while the processing of unvoiced speech and silence intervals is done with a stochastic codebook. The method also exploits a high correlation among glottal pulses to simplify the algorithm computational complexity and to improve codebook search time. Results of coding both male and female speech with the new method are presented.

The aim of the study was to inspect the acoustic properties and sleep characteristics of a preapneic snoring sound. The feasibility of forecasting upcoming respiratory events by snoring sound was also investigated. Participants with habitual snoring or a heavy breathing sound during sleep were recruited consecutively. Polysomnography was conducted, and snoring-related breathing sound was recorded simultaneously. Acoustic features and sleep features were extracted from 30-second samples, and a machine learning algorithm was used to establish 2 prediction models. A total of 74 eligible participants were included. Model 1, tested by 5-fold cross-validation, achieved an accuracy of 0.92 and an area under the curve of 0.94 for respiratory event prediction. Model 2, with acoustic features and sleep information tested by Leave-One-Out cross-validation, had an accuracy of 0.78 and an area under the curve of 0.80. Sleep position was found to be the most important among all sleep features contributing to the performance of the 2 models. Preapneic sound presented unique acoustic characteristics, and snoring-related breathing sound could be deployed as a real-time apneic event predictor. The models, combined with sleep information, serve as a promising tool for an early warning system to forecast apneic events. Wang B, Yi X, Gao J, etal. Real-time prediction of upcoming respiratory events via machine learning using snoring sound signal. J Clin Sleep Med. 2021;17(9):1777-1784.

By reconciling differences between the estimator and the filter of a code excited linear predictive (CELP) voice coder, higher quality is achieved in the output speech. The pulse amplitudes and pitch tap gain are solved for simultaneously to minimize the estimator bias in the CELP excitation. Increased signal to noise ratio is accomplished by modifying the pitch predictor such that the pitch synthesis filter accurately reflects the estimation procedure used to find the pitch tap gain, and by improving the excitation analysis technique such that the pitch predictor tap gain and codeword gain are solved for simultaneously, rather than sequentially. These modifications do not result in an increased transmission rate or significant increase in complexity of the CELP coding algorithm.

In the last few years, Code-Excited Linear Predictive (CELP) coding has emerged as the most prominent technique for digital speech communication at rates of 8 Kb/s and below, and it is now considered the best candidate coder for digital mobile telephony and secure speech communication. While the CELP coder is able to provide fairly good-quality speech at 8 Kb/s, its performance at 4.8 Kb/s is yet unsatisfactory for many applications. The novelty in the CELP coding concept, namely, the stochastic excitation of a linear filter, also constitutes a weakness of this method: the excitation contains a noisy component which does not contribute to the speech synthesis process and can not be completely removed by the filter. It is a common opinion among speech communication researchers that new forms of excitations need to be studied in order to improve the CELP performance at low bit rates.

A method for generating an artificial conversational speech signal as the input test signal in measuring the characteristics of devices operated by speech signals, such as speech detectors, voice switches, and echo controllers is proposed. A state transition model among talkspurt, pause, double‐talk, and mutual silence in conversational speech signals was introduced to simulate the statistical characteristics of these states in real conversational speech signals. This model assumed that the cumulative distribution of the duration time in each state is exponential. During talkspurt intervals, an artificial voice was generated using a speech synthesizer that controlled the spectrum and source characteristics independently. This artificial signal generation method can generate a signal with given characteristics by changing the values of system parameters, such as the average duration time of each state, transition probability from one state to another, and LPC (linear predictive coding) parameters that expr...

A testbench which enables a software implementation of different code-excited linear predictive (CELP) coder configurations in a simple and modular way, is presented as a research and development tool. It permits a flexible simulation process while using the actual hardware and software resources which constitute a real system. Its behaviour is consequently close to that of the final system envisaged. This testbench has been developed using the TMS320C30 digital signal processor and has been programmed in a modular way in C language. A CELP coder using two different codeword generation methods (the LBG algorithm and the Fuzzy-LVQ partitioning algorithm) have been tested using the proposed testbench.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Compared to scalar quantization (SQ), vector quantization (VQ) has memory, space-filling, and shape advantages. If the signal statistics are known, direct vector quantization (DVQ) according to these statistics provides the highest coding efficiency, but requires unmanageable storage requirements if the statistics are time varying. In code-excited linear predictive (CELP) coding, a single compromise codebook is trained in the excitation-domain and the space-filling and shape advantages of VQ are utilized in a nonoptimal, average sense. In this paper, we propose Karhunen-Loe/spl grave/ve transform (KLT)-based adaptive classified VQ (CVQ), where the space-filling advantage can be utilized since the Voronoi-region shape is not affected by the KLT. The memory and shape advantages can be also used, since each codebook is designed based on a narrow class of KLT-domain statistics. We further improve basic KLT-CVQ with companding. The companding utilizes the shape advantage of VQ more efficiently. Our experiments show that KLT-CVQ provides a higher SNR than basic CELP coding, and has a computational complexity similar to DVQ and much lower than CELP. With companding, even single-class KLT-CVQ outperforms CELP, both in terms of SNR and codebook search complexity.