Coding Of Speech Signals Research Articles

A method for extracting the semantic features of speech signal recognition based on empirical wavelet transform

The subject of this study is methods for improving the efficiency of semantic coding of speech signals. The purpose of this study is to develop a method for improving the efficiency of semantic coding of speech signals. Coding efficiency refers to the reduction of the information transmission rate with a given probability of error-free recognition of semantic features of speech signals, which will significantly reduce the required source bandwidth, thereby increasing the communication channel bandwidth. To achieve this goal, it is necessary to solve the following scientific tasks: (1) to investigate a known method for improving the efficiency of semantic coding of speech signals based on mel-frequency cepstral coefficients; (2) to substantiate the effectiveness of using the adaptive empirical wavelet transform in the tasks of multiple-scale analysis and semantic coding of speech signals; (3) to develop a method of semantic coding of speech signals based on adaptive empirical wavelet transform with further application of Hilbert spectral analysis and optimal thresholding; and (4) to perform an objective quantitative assessment of the increase in the efficiency of the developed method of semantic coding of speech signals in contrast to the existing method. The following scientific results were obtained during the study: a method of semantic coding of speech signals based on empirical wavelet transform is developed for the first time, which differs from existing methods by constructing a set of adaptive bandpass Meyer wavelet filters with further application of Hilbert spectral analysis to find the instantaneous amplitudes and frequencies of the functions of internal empirical modes, which will allow the identification of semantic features of speech signals and increase the efficiency of their coding; for the first time, it is proposed to use the method of adaptive empirical wavelet transform in the tasks of multiple-scale analysis and semantic coding of speech signals, which will increase the efficiency of spectral analysis by decomposing the high-frequency speech oscillation into its low-frequency components, namely internal empirical modes; the method of semantic coding of speech signals based on mel-frequency cepstral coefficients was further developed, but using the basic principles of adaptive spectral analysis with the help of empirical wavelet transform, which increases the efficiency of this method. Conclusions: We developed a method for semantic coding of speech signals based on empirical wavelet transform, which reduces the encoding rate from 320 to 192 bps and the required bandwidth from 40 to 24 Hz with a probability of error-free recognition of approximately 0.96 (96%) and a signal-to-noise ratio of 48 dB, according to which its efficiency is increased by 1.6 times as compared to the existing method. We developed an algorithm for semantic coding of speech signals based on empirical wavelet transform and its software implementation in the MATLAB R2022b programing language.

Warped Linear Predictive Coding of Speech Signal of Processing

Warped Linear Predictive Coding of Speech Signal of Processing

METHODS OF FACTORIAL CODING OF SPEECH SIGNALS

Context. The paper outlines the methods of factorial coding of speech signals using a factorial code to provide integrated information security and to maintain a receiver and transmitter clock phase. By integrated information security, for the methods proposed in this article, we mean data protection from effects of noise in communication channel and attempts of data unauthorized access in open multiple access telecommunication networks. Objective. The goal of the research is to provide integrated protection of real-time speech signals based on factorial coding. For this, the methods for factorial coding of speech signals and building speech codecs have been developed. These methods are based on the properties of factorial codes to keep synchronism with the working signal, to detect a significant part of errors caused by the action of noise, natural or created intentionally, to provide the ability to correct all detected errors with a finite accuracy, as well as to provide cryptographic protection against voice message unauthorized listening by hiding the law of converting speech signal samples into a permutation. Method. The main idea of the proposed methods is to choose permutations for information transferring with a specific set of properties and features that provide the ability to correct errors detected by code and to recover speech signal samples with a finite degree of accuracy (with a nonzero aperture). Results. The procedures for information coding/decoding have been determined. The results of the experimental evaluation of the model of such systems when working on a communication channel with both independent and multiple bit errors are presented. The magnitude of decoding noise due to the finite accuracy of speech signal samples recovery is determined as a function of bit error probability in a communication channel. Conclusions. The proposed methods of factorial coding of a speech signal provide integrated information security and recovery with finite accuracy of speech signal samples deformed by noise in communication channel. The requirements to the quality of communication channel (to the value of bit error probability) for comfortable speech perception are determined.

Efficient Remote Access System Based on Coded Speech Signals

This paper investigates an approach for speaker identification in a remote access system based on coded speech signals. The aim of using the coding process is to decrease the amount of transmitted data via the channel. In this proposed system, the speech signal is coded by two different coding techniques. It can be coded either by linear predictive coding or compressive sensing. The coded speech signal is transmitted into the receiver via the wireless communication channel. At the receiver, the received signal is decoded, and then speaker identification system is applied on the decoded signal. During the transmission process, the channel errors affect on the transmitted signal, so they should be taken into account. The speaker identification process is used to achieve the security needed for the remote access system. In speaker identification system, the feature vectors are captured from different discrete transforms such as discrete wavelet transform, discrete cosine transform, and discrete sine transform, besides the time domain. The recognition rate for all transforms is computed to evaluate the effect of coded signals on the performance of the speaker identification system. The results proved that the discrete cosine transform and discrete wavelet transform are the best. In addition to the proposed system gives close recognition results to those obtained from real speech signals revealing a simple degradation effect due to the speech coding.

Design and Comparison of Vector Quantization Codebooks for Narrowband Speech Coding

Vector quantization codebook algorithms are used for coding of narrow band speech signals. Multi-stage vector quantization and split vector quantization methods are two important techniques used for coding of narrowband speech signals and these methods are very popular due to the high bit rate minimization during coding of the signals. This paper presents performance measurements of multistage vector quantization and split vector quantization methods. We used line spectral frequencies for coding of the speech signals in codebook tables so as to ensure filter stability after quantization. The codebooks were generated by using the Linde-Buzo-Gray (LBG) algorithm. The tests were performed by selecting large amount of input data in training and test stages and to evaluate noise robustness of the methods, both noisy and clean speech signals were used. As a result, different codebooks were designed and tested in many stages and different bit rates to measure quantization performance. It is measured in terms of spectral distortion evaluation. We obtained the best result in 24bit multistage vector quantization codebook with a spectral distortion less than 1 dB for clean speech training data input. When we compared multistage and split vector quantization codebook spectral distortion results, multistage codebooks gave better performance in each option.

An Algorithm for Simple Differential Speech Coding Based on Backward Adaptation Technique

This paper presents a simple differential speech signal coding algorithm, based on backward adaptation. The considered algorithm is executed in frame by frame manner, by implementing predictive and adaptive quantization techniques. Both prediction and adaptation are performed backward, based on the previously quantized input signal frame. This enables us to obtain high quality output signal, without increasing the bit rate. This research puts emphasis on the quantizer design, with the optimal support limit determination, and theoretical performance evaluation. Objective quality of the output signal is evaluated through signal to quantization noise ratio (SQNR). We perform theoretical and experimental analysis of the algorithm performance and provide comparative results of implementing speech signal coding techniques with similar complexity. Experimental results show that our simple differential speech coding algorithm satisfies the G.712 Recommendation for high-quality speech coding at the bit rate of 6 bits per sample. This indicates that the algorithm can be successfully implemented in high quality speech signal coding.

A subspace based progressive coding method for speech compression

In this study, two novel methods, which are based on Karhunen Loeve Transform (KLT) and Independent Component Analysis (ICA), are proposed for coding of speech signals. Instead of immediately dealing with eigenvalue magnitudes, the KLT- and ICA-based methods use eigenvectors of covariance matrices (or independent components for ICA) by geometrically grouping these vectors into fewer numbers of vectors. In this way, a data representation compaction is achieved. Further compression is achieved through discarding autocovariance eigenvectors corresponding to the small eigenvalues and applying vector quantization on the remaining eigenvectors. Additionally, this study proposes an iterative error refinement process, which uses the rest of the available bandwidth in order to transmit an efficient representation of the description error for better SNR. The overall process constitutes a new approach to efficient speech coding, with ICA being used in subspace speech coding for the first time. Constant bit rate (CBR) and variable bit rate (VBR) coding algorithms are employed with the proposed methods. TIMIT speech database is used in the experimental studies. Speech signals are synthesized at 2.4 kbps, 8 kbps, 12.2 kbps, 16 kbps, 16.4kbps and 19.85 kbps rates by using various frame lengths. The qualities of synthesized speech signals are compared to those of available speech codecs, i.e., LPC (2.4 kbps), G.728 (LD-CELP, 16 kbps), G.729A (CS-CELP, 8 kbps), EVS (16.4 kbps), AMR-NB (12.2 kbps) and AMR-WB (19.85 kbps).

Speech Signal Coding Using Forward Adaptive Quantization and Simple Transform Coding

The paper proposes a novel speech signal coding scheme that implements a simple transform coding and forward adaptive quantization. The proposed scheme is adapted to the input signal variance, providing highly efficient bandwidth usage, whereas implemented transform coding provides sub-sequences with more predictable signal characteristics, so that more suitable signal processing can be performed. The aforementioned transform coding precedes adaptive quantization, providing additional compression. The objective quality measure used for system performance estimation is SQNR (signal-to-quantization-noise ratio), which represents a standard measure for lossy coding types. The influence of transform coding is discussed by comparing the obtained results with the corresponding one achieved by applying only the same adaptive quantization. Furthermore, the comparison with system performance of PCM (pulse-code modulation) coding system confirms that the proposed coding scheme has a lot of potential for further implementation, since that the proposed system ensures SQNR gain up to 4.0983 [dB] for various values of system parameters. DOI: http://dx.doi.org/10.5755/j01.eie.22.3.15318

Two-Channel Quadrature Mirror Filter Bank: An Overview

During the last two decades, there has been substantial progress in multirate digital filters and filter banks. This includes the design of quadrature mirror filters (QMF). A two-channel QMF bank is extensively used in many signal processing fields such as subband coding of speech signal, image processing, antenna systems, design of wavelet bases, and biomedical engineering and in digital audio industry. Therefore, new efficient design techniques are being proposed by several authors in this area. This paper presents an overview of analysis and design techniques of the two-channel QMF bank. Application in the area of subband coding and future research trends are also discussed.

Cochlear Implant Speech Processing Using Wavelet Transform

We present a method for coding speech signals for the simulation of a cochlear implant. The method is based on a wavelet packet decomposition strategy. We used wavelet packet db4 for 7 levels, generated a series of channels with bandwidths exactly the same as nucleus device, and applied an input stimulus to each channel. The processed signal was then reconstructed and compared to the original signal, which preserved the contents to a high percentage. Finally, performance of the wavelet packet decomposition in terms of computational complexity was compared to other commonly used strategies in cochlear implants. The results showed the power of this method in processing of the input signal for implant users with less complexity than other methods, while maintaining the contents of the input signal to a very good extent.

Sparse Linear Prediction and Its Applications to Speech Processing

The aim of this paper is to provide an overview of Sparse Linear Prediction, a set of speech processing tools created by introducing sparsity constraints into the linear prediction framework. These tools have shown to be effective in several issues related to modeling and coding of speech signals. For speech analysis, we provide predictors that are accurate in modeling the speech production process and overcome problems related to traditional linear prediction. In particular, the predictors obtained offer a more effective decoupling of the vocal tract transfer function and its underlying excitation, making it a very efficient method for the analysis of voiced speech. For speech coding, we provide predictors that shape the residual according to the characteristics of the sparse encoding techniques resulting in more straightforward coding strategies. Furthermore, encouraged by the promising application of compressed sensing in signal compression, we investigate its formulation and application to sparse linear predictive coding. The proposed estimators are all solutions to convex optimization problems, which can be solved efficiently and reliably using, e.g., interior-point methods. Extensive experimental results are provided to support the effectiveness of the proposed methods, showing the improvements over traditional linear prediction in both speech analysis and coding.

MODEL SINUSOIDA SECARA SEGMENTAL UNTUK PENGKODEAN SINYAL SUARA

Segmental Sinusoidal Model for Speech Signal Coding. Periodic signal can be decomposed by sinusoidal component with Fourier series. With this characteristic, it can be modeled referring by sinusoidal form. By the sinusoidal model, signal can be quantized in order to encode the speech signal at the lower rate. The recent sinusoidal method is implemented in speech coding. By using this method, a block of the speech signal with 20 ms to 30 ms width is coded based on Fourier series coefficients. The new method proposed is quantization and reconstruction of speech signal by the segmental sinusoidal model. A segment is defined as a block of the speech signal from certain peak to consecutive peak. The length of the segment is variable, instead of the fixed block like the recent sinusoidal method. Coder consists of the encoder and the decoder. Encoder works to code speech signal at variable rate. Then coded signal will be transmitted to receiver. On the receiver, coded signal will be reconstructed, so that the reconstruction signal has the near quality compared with the original signal. The experimental results show that the average of segmental SNR is more than 20 dB.

Subband Coding of Speech Signals Using Decimation and Interpolation

In many practical applications of digital signal processing, such as telecommunication systems, one is faced with the problem of changing the sampling rate of a signal, either increasing it or decreasing it by some amount. In telecommunication systems, that transmit and receive different types of signals, there is a requirement to process the various signals at different rates commensurate with the corresponding bandwidths of the various signals. This paper addresses the problem of sampling rate conversion and multirate signal processing in the digital domain. A structure of a two-channel quadrature mirror filter with low pass filter, high pass filter, decimators and interpolators, is proposed to perform subband coding of speech signals in the digital domain. The proposed structure decomposes a signal into low frequency and high frequency components. The performance of the proposed structure is compared with the performance of the delta-modulation encoding systems. The results show that the proposed structure significantly reduces the error and achieves considerable performance improvement compared to delta-modulation encoding systems.

Temporal precision of speech coded into nerve-action potentials

The auditory pathway is an excellent system to study temporal aspects of neuronal processing. Unlike other sensory systems, temporal cues cover an extremely wide range of information: for sound localization, interaural time differences with a precision of tens of microseconds are extracted. Phase-locking of auditory nerve responses, which is related to the coding of the temporal fine structure, occurs from the lowest audible frequencies probably up to 3 kHz in humans. Amplitude modulations in speech signals are processed in the ms to tens of ms range. And finally, the energy of spoken speech itself is modulated with a frequency of about 4 Hz, corresponding to a syllable frequency in the order of few hundreds of ms. To extract temporal cues at all timescales, it is important to understand how temporal information is coded. We investigate temporal coding of speech signals using the methods of information theory and a model of the human inner ear. The model is based on a traveling-wave model, a nonlinear compression stage which mimics the function of the amplifier, a model of the sensory cells, the afferent synapse and spike generation (Sumner ) which we extended to replicate offset adaptation (Zhang). We used the action potentials of the auditory nerve to drive Hodgkin-Huxley-type point models of various neurons in the cochlear nucleus. In this investigation we only report data from onset neurons, which exhibit extraordinary fast membrane time-constants below 1 ms. Onset neurons are known for their precise temporal processing. They achieve precisely timed action potentials by coincidence detection: they fire only if at least 10% of the auditory nerve fibers which innervate them fire synchronously. With information theory, we analyzed the transmitted information rate coded in neural spike trains of modeled neurons in the cochlear nucleus for vowels. We found that onset neurons are able to code temporal information with sub-millisecond precision (<0.02 ms) across a wide range of characteristic frequencies. Temporal information is coded by precisely timed spikes per se, not only temporal fine structure. Moreover, the major portion of information (60%) is coded with a temporal precision from 0.2 to 4 ms. Enhancing the temporal resolution from 10 ms to 3 ms and from 3 ms to 0.3 ms is expected to increase the transmitted information by approximately twofold and 2.5 fold, respectively. In summary, our results provide quantitative insight into temporal processing strategies of neuronal speech processing. We conclude that coding of information in the time domain might be essential to complement the rate-place code, especially in adverse acoustical environments. Acknowledgments:Supported by within the Munich Bernstein Center for Computational Neuroscience by the German Federal Ministry of Education and Research (reference numbers 01GQ0441 and 01GQ0443).

Application of Linear Prediction Coefficients Interpolation in Speech Signal Coding

Several interpolation techniques of linear prediction coefficients (LPC) for speech signal coding were experimentally analyzed. The obtained results show that direct interpolation of LPC can be applied for coding and decoding of significantly long segments of voiced speech. The best performance in sense of least number of unstable filters, smallest spectral distortions and greatest MOS score, was achieved when for interpolation were used linear and Hermite polynomials. Higher order interpolation polynomials such us cubic or third order splines, produced significant alternations in obtained LPC that led to worst results of the quality of the decoded speech. Also higher order polynomials without additional post processing of interpolated LPC produced more unstable filters. Ill. 5, bibl. 11 (in English; summaries in English, Russian, Lithuanian).

Compression of surface EMG signals with algebraic code excited linear prediction

Despite the interest in long timescale recordings of surface electromyographic (EMG) signals, only a few studies have focused on EMG compression. In this paper we investigate a lossy coding technique for surface EMG signals that is based on the algebraic code excited linear prediction (ACELP) paradigm, widely used for speech signal coding. The algorithm was adapted to the EMG characteristics and tested on both simulated and experimental signals. The coding parameters selected led to a compression ratio of 87.3%. For simulated signals, the mean square error in signal reconstruction and the percentage error in average rectified value after compression were 11.2% and 4.90%, respectively. For experimental signals, they were 6.74% and 3.11%. The mean power spectral frequency and third-order power spectral moment were estimated with relative errors smaller than 1.23% and 8.50% for simulated signals, and 3.74% and 5.95% for experimental signals. It was concluded that the proposed coding scheme could be effectively used for high rate and low distortion compression of surface EMG signals. Moreover, the method is characterized by moderate complexity (approximately 20 million instructions/s) and an algorithmic delay smaller than 160 samples (∼160 ms).

Application of the wavelet transform to the low‐bit‐rate speech coding system

AbstractIn the analysis‐synthesis coding of speech signals, realization of high quality in the low‐bit‐rate coding depends on the extraction of its characteristic parameters in the preprocessing. The precise extraction of the fundamental frequency, one of the parameters of the source information, guarantees quality in speech synthesis. But its extraction is difficult because of the influence of the consonant, nonperiodicity of vocal cord vibrations, the wide range of the fundamental frequency, and so on. In this paper we propose a new fundamental frequency extraction with the criterion based on its harmonics structure and low‐bit‐rate speech coding system using the wavelet transform. © 2004 Wiley Periodicals, Inc. Electr Eng Jpn, 148(3): 62–71, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.10309

Method for coding speech and music signals

The present invention provides a transform coding method efficient for music signals that is suitable for use in a hybrid codec, whereby a common Linear Predictive (LP) synthesis filter is employed for both speech and music signals. The LP synthesis filter switches between a speech excitation generator and a transform excitation generator, in accordance with the coding of a speech or music signal, respectively. For coding speech signals, the conventional CELP technique may be used, while a novel asymmetrical overlap-add transform technique is applied for coding music signals. In performing the common LP synthesis filtering, interpolation of the LP coefficients is conducted for signals in overlap-add operation regions. The invention enables smooth transitions when the decoder switches between speech and music decoding modes.

Hammerstein Model for Speech Coding

A nonlinear Hammerstein model is proposed for coding speech signals. Using Tsay's nonlinearity test, we first show that the great majority of speech frames contain nonlinearities (over 80% in our test data) when using 20-millisecond speech frames. Frame length correlates with the level of nonlinearity: the longer the frames the higher the percentage of nonlinear frames. Motivated by this result, we present a nonlinear structure using a frame-by-frame adaptive identification of the Hammerstein model parameters for speech coding. Finally, the proposed structure is compared with the LPC coding scheme for three phonemes /a/, /s/, and /k/ by calculating the Akaike information criterion of the corresponding residual signals. The tests show clearly that the residual of the nonlinear model presented in this paper contains significantly less information compared to that of the LPC scheme. The presented method is a potential tool to shape the residual signal in an encode-efficient form in speech coding.

Model-Based Speech Signal Coding Using Optimized Temporal Decomposition for Storage and Broadcasting Applications

A dynamic programming-based optimization strategy for a temporal decomposition (TD) model of speech and its application to low-rate speech coding in storage and broadcasting is presented. In previous work with the spectral stability-based event localizing (SBEL) TD algorithm, the event localization was performed based on a spectral stability criterion. Although this approach gave reasonably good results, there was no assurance on the optimality of the event locations. In the present work, we have optimized the event localizing task using a dynamic programming-based optimization strategy. Simulation results show that an improved TD model accuracy can be achieved. A methodology of incorporating the optimized TD algorithm within the standard MELP speech coder for the efficient compression of speech spectral information is also presented. The performance evaluation results revealed that the proposed speech coding scheme achieves 50%-60% compression of speech spectral information with negligible degradation in the decoded speech quality.