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.

Pitch-scaled estimation of simultaneous voiced and turbulence-noise components in speech

Abstract: Almost all speech contains simultaneous contributions from more than one acoustic source within the speaker's vocal tract. In this paper, we propose a method-the pitch-scaled harmonic filter (PSHF)-which aims to separate the voiced and turbulence-noise components of the speech signal during phonation, based on a maximum likelihood approach. The PSHF outputs periodic and aperiodic components that are estimates of the respective contributions of the different types of acoustic source. It produces four reconstructed time series signals by decomposing the original speech signal, first, according to amplitude, and then according to power of the Fourier coefficients. Thus, one pair of periodic and aperiodic signals is optimized for subsequent time-series analysis, and another pair for spectral analysis. The performance of the PSHF algorithm is tested on synthetic signals, using three forms of disturbance (jitter, shimmer and additive noise), and the results were used to predict the performance on real speech. Processing recorded speech examples elicited latent features from the signals, demonstrating the PSHF's potential for analysis of mixed-source speech.

Lossless compression of waveform data for efficient storage and transmission

Abstract: A two-stage technique for lossless waveform data compression is described. The first stage is a modified form of linear prediction with discrete coefficients, and the second stage is bilevel sequence coding. The linear predictor generates an error or residue sequence in a way such that exact reconstruction of the original data sequence can be accomplished with a simple algorithm. The residue sequence is essentially white Gaussian with seismic or other similar waveform data. Bilevel sequence coding, in which two sample sizes are chosen and the residue sequence is encoded into subsequences that alternate from one level to the other, further compresses the residue sequence. The algorithm is lossless, allowing exact, bit-for-bit recovery of the original data sequence. The performance of the algorithm at each stage is analyzed. Applications of the two-stage technique to typical seismic data indicates that an average number of compressed bits per sample close to the lower bound is achievable in practical situations. >

Digital speech vocoder

Abstract: A speech analyzer and synthesizer system using a sinusoidal encoding and decoding techniques for voiced frames and noise excitation or multiple pulse excitation for unvoiced frames. For voiced frames, the analyser (100) transmits the pitch, values for each harmonic frequency by defining the offset from integer multiples of the fundamental frequency, total frame energy, and linear predictive coding, LPC, coefficients (FIG. 1). The synthesizer (200) is responsive to that information to determine the phase of the fundamental frequency and each harmonic based on the transmitted pitch and harmonic offset information and to determine the amplitudes of the harmonics utilizing the total frame energy and LPC coefficients (FIG. 2). Once the phase and amplitudes have been determined for the fundamental and harmonic frequencies, the sinusoidal analysis is performed for voiced frames. For each frame, the determined frequencies and amplitudes are defined at the center of the frame, and a linear interpolation is used both to determine continuous frequency and amplitude signals of the fundamental and the harmonics throughout the entire frame by the synthesizer. In addition, the analyzer initially adjusts the pitch so that the harmonics are evenly distributed around integer multiples of this pitch.

Learning spectral mapping for speech dereverberation

Abstract: Reverberation distorts human speech and usually has negative effects on speech intelligibility, especially for hearing-impaired listeners. It also causes performance degradation in automatic speech recognition and speaker identification systems. Therefore, the dereverberation problem must be dealt with in daily listening environments. We propose to use deep neural networks (DNNs) to learn a spectral mapping from the reverberant speech to the anechoic speech. The trained DNN produces the estimated spectral representation of the corresponding anechoic speech. We demonstrate that distortion caused by reverberation is substantially attenuated by the DNN whose outputs can be resynthesized to the dereverebrated speech signal. The proposed approach is simple, and our systematic evaluation shows promising dereverberation results, which are significantly better than those of related systems.

Apparatus for expanding narrowband speech to wideband speech by codebook correspondence of linear mapping functions

Abstract: Apparatus for expanding the bandwidth of speech signals such that a narrowband speech signal is input and digitized, the spectral envelope information and residual information are extracted from the digitized signal by linear predictive coding analysis, the spectral envelope information is expanded into wideband information by a spectral envelope converter, the residual information is expanded into wideband information by a residual converter, the converted spectral envelope information and residual information are combined to produce a wideband speech signal, frequency information not contained in the input signal is extracted from the obtained wideband speech signal by a filter, and the resulting signal is added to the original digitized input signal, and the obtained signal is converted into an analog signal as the output signal of the apparatus. The apparatus comprises a linear mapping function codebook used for converting spectral parameters, and a weights calculator and an adder for weighing and summing function outputs.