Frequency-space contractive speech coding

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 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.

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.

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.

This paper describes about speech recognition implemented on ATmega162 microcontroller. The word (voice command) in a speech signal is applied for controlling movement of a mobile robot. The mobile robot will move according to the voice command in the speech signal. There are five commands, which are Indonesian language, used to control the movement of mobile robot. They are “maju”, “mundur”, “kiri”, “kanan”, and “stop” used to command the mobile robot to move forward, move backward, turn left, turn right and stop respectively.The methods used in this research are Linear Predictive Coding (LPC) and Hidden Markov Model (HMM). LPC is used to extract word data from a speech signal. HMM is used to recognize the word pattern data, which are extracted from a speech signal. Sampling rate of the speech signal is 8 kHz and the speech signal is sampled for 0.5 seconds. Experiments were done in several variations of observation symbol number and number of sample. The experimental results show that the highest recognition rate that can be achieved by the system is 87%. The mobile robot can move in accordance with the voice command that is given to the robot.

Linear predictive coding (LPC) residual is an important component for analysis-by-synthesis speech coders. Using spectral envelope vector quantisation (SEVQ) for LPC residual signals, MPEG-4 harmonic vector excitation coding (HVXC) speech coder is able to change the tone and speed for decoding bitstreams. A modified version is proposed to the simplified hearing-based SEVQ (SSEVQ) approach proposed by Wang and Yang. In addition, the energy first search scheme for SEVQ (EFSEVQ) is proposed in order to reduce the computational complexity of SEVQ for MPEG-4 HVXC speech coder. The proposed EFSEVQ scheme also agrees with human hearing properties. Simulation results reveal that the proposed EFSEVQ search scheme not only reduces the computational complexity but also preserves the quality of encoded speech.

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.

Experimental results are reported in the area of speech modification for the purpose of correcting vocal tract resonance disorders. The linear predictive coding (LPC) coefficients and residual corresponding to an input speech signal are calculated, and a combination of nonlinear frequency warping and amplitude scaling is applied to produce modified LPC coefficients and then synthesize an output speech signal from these modified coefficients and the residual signal. The spectral envelope modification parameters are designed to adapt to the first two formant frequencies of the input speech. Cubic polynomial warping functions and amplitude scaling factors are found at vowel formant locations and are interpolated from these points over F1, F2 space. The results are compared with those obtained from frame-by-frame nonlinear spectral warping using a dynamic programming algorithm. >

Growth in Digital signal processing technology gives positives influences in human life. One of the branch of science that gives significant influence is digital speech processing. It can be expand into some applications that make human life easier. Digital speech processing is appropriate to speech recognition. Speech recognition is used to arrange the movement of remote control car. The remote control car will move according to our speech. This final project is closely related with speech recognition. The LPC (Linear Predictive Coding) method will extract the speech signal features and HMM (Hidden Markov Model) to modelling the speech signal are used. It is done by comparing model from extracting feature that is available in HMM modelling. Models will be used in the speech recognition process, if the models have highest level of conformity. The experiment has been done in two conditions, i.e ideal condition in room wih low noise level and unideal condition in room with noise. Result of the experiment from the whole sistem performace at ideal condition is 97,71% for people that have been inputed in the database, and 95,42% for people which have not been inputed in the database. Result of the experiment of database say it regulary is 97,14%. Result of the experiment of database at unideal condition is 54,28% for inputing word at noisy area, result of the experiment of database for inputing word at high frequency from siren voice is 98,57% and result of the experiment of inputing similar word with database is 97,6% Keywords: speech recognition, LPC, HMM

Speech signals are one of the most important means of communication among the human beings. In this paper, a comparative study of two feature extraction techniques are carried out for recognizing speaker independent spoken isolated words. First one is a hybrid approach with Linear Predictive Coding (LPC) and Artificial Neural Networks (ANN) and the second method uses a combination of Wavelet Packet Decomposition (WPD) and Artificial Neural Networks. Voice signals are sampled directly from the microphone and then they are processed using these two techniques for extracting the features. Words from Malayalam, one of the four major Dravidian languages of southern India are chosen for recognition. Training, testing and pattern recognition are performed using Artificial Neural Networks. Back propagation method is used to train the ANN. The proposed method is implemented for 50 speakers uttering 20 isolated words each. Both the methods produce good recognition accuracy. But Wavelet Packet Decomposition is found to be more suitable for recognizing speech because of its multi-resolution characteristics and efficient time frequency localizations.

Speech coders operating at low bit rates necessitate efficient encoding of the linear predictive coding (LPC) coefficients. In this work, we developed an optimized switched split vector quantization (SSVQ) scheme applied for encoding the LPC coefficients represented by the line spectral frequencies (LSF) parameters. It will be shown that the SSVQ provides better performance in terms of bit-rate, spectral distortion and computational complexity than the traditional split vector quantizer. We further applied our SSVQ encoding system, called LSF-SSVQ encoder, to quantize the LSF parameters of the 2.4 Kbits/s normalised speech coder MELP operating over an ideal noiseless channel.

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.

This paper addresses the problem of reconstructing wideband speech signals from observed narrowband speech signals. The goal of this work is to improve the perceived quality of speech signals which have been transmitted through narrowband channels or degraded during acquisition. We describe a system, based on linear predictive coding, for estimating wideband speech from narrowband. This system employs both previously identified and novel techniques. Experimental results are provided in order to illustrate the system's ability to improve speech quality. Both objective and subjective criteria are used to evaluate the quality of the processed speech signals.

This paper describes the progress in frequency-domain linear prediction coding (LPC)-based audio coding schemes. Although LPC was originally used only for time-domain speech coders, it has been applied to frequency-domain coders since the late 1980s. With the progress in associated technologies, the frequency-domain LPC-based audio coding scheme has become more promising, and it has been used in speech/audio coding standards, such as MPEG-D unified speech and audio coding and 3GPP enhanced voice services since 2010. Three of the latest investigations on the representations of LPC envelopes in frequency-domain coders are shown. These are the harmonic model, frequency-resolution warping and the Powered All-Pole Spectral Envelope, all of which are aiming at further enhancement of the coding efficiency.

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. >