Mixed spectral representation—Formants and linear predictive coding

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 modified regular-pulse excitation (RPE) linear predictive coding (LPC) codec is used to encode audio signals of 15 kHz bandwidth. With appropriate perceptual masking and carefully selected excitation structure, a fixed-pulse excitation (FPE) LPC codec is able to provide high-fidelity audio output. The quantization of both the LPC parameters and the excitation pulse sequence is examined. It is found that the LPC parameters can be quantized efficiently by adaptive differential PCM, while the excitation pulse sequences need more bits to quantize the amplitudes compared to narrowband speech encoding. Informal listening tests show that high-fidelity audio can be achieved at bit rates less than 100 kb/s.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

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.

The authors present a new method of modeling the excitation of the linear predictive coding (LPC) synthesis filter at low and medium bit rates. For the speech segments with regular patterns, the excitation is composed of two sequences of pulses. The first sequence is generated in a way similar to the classical physical model that consists of a glottal filter with thinned coefficients driven by a set of pitch pulses. Both the glottal function and pitch pulses are determined using the analysis-by-synthesis technique with the mean square criterion. The auxiliary sequence consists of a few pulses to supplement the first sequence for further reducing the mean square error. For unvoiced speech segments, multipulse excitation is simply used to drive the synthesis filter. Based on real speech analysis, the model has a gain on signal-to-noise ratio (SNR) of 2-3 dB for voiced segments over the multipulse LPC using 0.8-2.5 pulses/ms.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

An elegant relationship (here called symmetry) between the linear predictive coding (LPC) analysis and the composite sinusoidal modeling (CSM) analysis is described. The objective is to provide a systematic view of the theory of voice spectral modeling such as LPC, PARCOR, CSM, and line spectral pairs (LSP). All of these methods of analysis are reduced to the problems of deriving the model parameters for lower‐order autocorrelation functions equal to the number of degrees of freedom of the model. From the point of view of the theory of orthogonal polynomials, LPC and CSM are the theory of orthogonal polynomials of a unit circle (in the case of LPC) and on the real axis (in the case of CSM). Beautiful symmetries hold in regard to the formulation, definition of various parameters, and solution algorithm. Also, a new interpretation is given here for the LSP from the point of view of orthogonal polynomials. © 2002 Scripta Technica, Electron Comm Jpn Pt 3, 85(6): 42–54, 2002

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.

In Malaysia, most people speak several varieties of English known as Malaysian English (MalE) and there is no uniform version because of the existence of multi-ethnic population. It is a common scenario that Malaysians speak a particular local Malay, Chinese or Indian English accent. As most commercial speech recognizers have been developed using a standard English language, it is a challenging task for achieving highly efficient performance when other accented speech are presented to this system. Accent identification (AccID) can be one of the subsystem in speaker independent automatic speech recognition (SI-ASR) system so that deterioration issue in its performance can be tackled. In this paper, the most important speech features of three ethnic groups of MalE speakers are extracted using Linear Predictive Coding (LPC), formant and log energy feature vectors. In the subsequent stage, the accent identity of a speaker is predicted using K-Nearest Neighbors (KNN) classifier based on the extracted information. Prior, the preprocessing parameters and LPC order are investigated to properly extract the speech features. This study is conducted on a small set speech corpus developed as pilot study to determine the feasibility of automatic AccID of MalE speakers which has never been reported before. The experimental results indicate a highly promising recognition accuracy of 94.2% upon feature fusion sets of LPC, formants and log energy.

A technique of temporal speech compression, called temporal decomposition, is considered. A mathematical presentation of temporal decomposition is discussed from the point of view of speech production. Temporal decomposition is studied in terms of an iterative solution and its convergence. For vocoder applications, a practical algorithm, short-term temporal decomposition, is proposed. The statistical performance of this algorithm was tested on a multispeaker database. The efficiencies of vector quantization and vector predictability were comparatively studied on data derived from the algorithm and on data from linear predictive coding (LPC) analysis. The results show that data from the algorithm have more efficient information transmission via vector quantization than data from LPC, and that vector prediction coding is feasible for the data from temporal decomposition.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

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...

In this paper, we derived a relationship between Linear Predictive Coding (LPC) coefficients and Immittance Spectral Pairs (ISP) pseudo-cepstrum. In Adaptive Multi-Rate Wideband(AMR-WB) speech codec, ISP parameters were used for quantisation and interpolation. This paper first described the representation of ISP pseudo-cepstrum. ISP pseudo-cepstrum was defined as the inverse z-transform of half natural logarithm of immittance function. References [2] and [3] proposed two kinds of new perceptual weighting filters based on ISP pseudo-cepstrum. Spectral tilt is more pronounced in wideband signals due to the wide dynamic range between low and high frequencies. The proposed filters are helpful for solving this problem. Then we gave the conversion method between ISP pseudo-cepstrum and polynomials cepstrum coefficients. Finally we derived the recursive relations between ISP pseudo-cepstrum and LPC coefficients. There are two methods of converting LPC coefficients to ISP pseudo-cepstrum. The one is directly from LPC coefficients to ISP pseudo-cepstrum. The other is from ISP parameters.

We are investigating countermeasure techniques against electric noise on telecommunication and related equipment. There are many types of electric noise, and we focus on the characterization of transient noise such as the switching noise of switching power supplies. We are developing a method of transient signal analysis based on linear predictive coding (LPC) for the purpose. Unfortunately, as standard LPC contains Yule-Walker (YW) approximation, which replaces a given time series with a periodic time series for simplification, it is not suitable for transient signal analysis. Therefore, our method of transient signal analysis does not contain YW approximation. Instead, we use the local linearization technique, which was developed in the field of quantum mechanics. Our method makes it possible to obtain instantaneous frequencies and instantaneous decay rates simultaneously with high precision from a small number of samples. We discuss our LPC method avoid using YW approximation and using the local linearization technique and its application to repetitive ringing signals, which mimics the switching noise of a switching power supply, to show the efficiency of our method.

This paper investigates the Malay speaker identification using Neural Networks. Speech database was developed with five speakers as trainers and five speakers as imposters. The speech training set included 30 vowel sounds of five trainer speakers. The test set included 30 vowel sounds from the five trainers and 30 vowel sounds from five imposters. The speech sounds were sampled at 20 kHz with 16 bit resolution. A single frame of cepstral coefficients was extracted from the speech sounds using Linear Predictive Coding. Multi-Layer Perceptron with one hidden-layer was used to perform the speaker identification. The output of the MLP consisted of one neuron. Experiments were conducted to determine the optimal signal length of vowels, hidden neuron number and threshold values. A maximum recognition rate of 93.33% was achieved.