Linear predictive coding

About: Linear predictive coding is a research topic. Over the lifetime, 6565 publications have been published within this topic receiving 142991 citations. The topic is also known as: Linear predictive coding, LPC.

Optimizing LPC filter parameters for multi-pulse excitation

Abstract: Present LPC analysis procedures assume that the input to the all-pole filter is white; the filter parameters are obtained by minimizing the mean-squared error between the filter output samples and their values obtained by linear prediction on the basis of past output samples. It is well known that these procedures often do not yield accurate filter parameters for periodic (or quasi-periodic) signals such as voiced speech. To compensate for the periodic nature of speech, an estimate of the excitation of the all-pole filter has to be made. Multi-pulse LPC obtains the best excitation for a specified bit rate by minimizing a weighted mean-squared criterion representing subjectively important differences between original and synthetic speech signals. In this paper we examine the possibility that multi-pulse excitation can approximate the all-pole filter excitation sufficiently closely and obtain the optimum filter parameters for this excitation.

Speaker modification with LPC pole analysis

Abstract: Speaker modification is the ability to change the perceived speaker identity of a recorded utterance. Basic to this is the capability to alter the vowel segments of speech. Not only do these segments comprise the majority of the voiced portion of speech but they are dominated by clearly defined acoustic parameters-formant frequencies and pitch. A method of altering the formant frequencies of vowel segments using LPC analysis/synthesis was investigated. Pole location modification based on statistical references provided individual control over formant frequencies and bandwidths but, in some transformations, lead to artifacts in the reconstructed speech.

Modeling and classification of natural sounds by product code hidden Markov models

Abstract: Linear predictive coding (LPC), vector quantization (VQ), and hidden Markov models (HMMs) are three popular techniques from speech recognition which are applied in modeling and classifying nonspeech natural sounds. A new structure called the product code HMM uses two independent HMM per class, one for spectral shape and one for gain. Classification decisions are made by scoring shape and gain index sequences from a product code VQ. In a series of classification experiments, the product code structure outperformed the conventional structure, with an accuracy of over 96% for three classes. >

Stochastic-Deterministic MMSE STFT Speech Enhancement With General A Priori Information

Abstract: A wide range of Bayesian short-time spectral amplitude (STSA) speech enhancement algorithms exist, varying in both the statistical model used for speech and the cost functions considered. Current algorithms of this class consistently assume that the distribution of clean speech short time Fourier transform (STFT) samples are either randomly distributed with zero mean or deterministic. No single distribution function has been considered that captures both deterministic and random signal components. In this paper a Bayesian STSA algorithm is proposed under a stochastic-deterministic (SD) speech model that makes provision for the inclusion of a priori information by considering a non-zero mean. Analytical expressions are derived for the speech STFT magnitude in the MMSE sense, and phase in the maximum-likelihood sense. Furthermore, a practical method of estimating the a priori SD speech model parameters is described based on explicit consideration of harmonically related sinusoidal components in each STFT frame, and variations in both the magnitude and phase of these components between successive STFT frames. Objective tests using the PESQ measure indicate that the proposed algorithm results in superior speech quality when compared to several other speech enhancement algorithms. In particular it is clear that the proposed algorithm has an improved capability to retain low amplitude voiced speech components in low SNR conditions.

Method and apparatus for speech encoding and decoding by sinusoidal analysis and waveform encoding with phase reproducibility

Abstract: A speech encoding method and apparatus in which an input speech signal is divided in terms of blocks or frames as encoding units and encoded in terms of the encoding units, whereby explosive and fricative consonants can be impeccably reproduced, while there is an attenuation of the occurrence of foreign sounds being generated at a transient portion between voiced (V) and unvoiced (UV) portions, so that the speech with high clarity devoid of “stuffed” feeling may be produced. The encoding apparatus includes a first encoding unit for finding residuals of linear predictive coding (LPC) of an input speech signal for performing harmonic coding and a second encoding unit for encoding the input speech signal by waveform coding. The first encoding unit and the second encoding unit are used for encoding a voiced (V) portion and an unvoiced (UV) portion of the input signal, respectively. Code excited linear prediction (CELP) encoding employing vector quantization by a closed loop search of an optimum vector using an analysis-by-synthesis method is used for the second encoding unit. A corresponding decoding method and apparatus is also provided.