Topic
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.
Papers published on a yearly basis
Papers
More filters
TL;DR: An enhanced feature representation for frog call classification using the temporal, perceptual and cepstral features is presented and results show that the proposed feature representation could achieve better classification performance comparing to other methods.
27 citations
23 May 1989
TL;DR: A novel approach to narrow- and medium-band speech coding that can dynamically balance the transmission rate between the excitation and the spectral parameters is introduced, improving the subjective speech quality.
Abstract: The authors introduce a novel approach to narrow- and medium-band speech coding that can dynamically balance the transmission rate between the excitation and the spectral parameters. The coding algorithm, called multimode coding, operates several coding blocks, each of which has a different bit assignment in parallel, and selects the optimum coding block frame by frame based on an evaluation of the reproduced speech quality. This coding algorithm is applied to 4.8 and 8.0 kb/s CELP coders, and 2.0-2.4 dB of SNRseg improvement is achieved over conventional CELP coders. The spectral distortion measure is added as an evaluation function, improving the subjective speech quality. >
27 citations
Patent•
10 May 2012
TL;DR: In this article, the authors proposed a method of processing an audio signal comprises steps for determining whether the audio signal encoding type is musical signal encoding types using first type information and second type information.
Abstract: FIELD: information technology. ^ SUBSTANCE: method of processing an audio signal comprises steps for determining whether the audio signal encoding type is musical signal encoding type using first type information. If the audio signal encoding type is not a musical signal encoding type, it determined whether the audio signal encoding type is a speech signal encoding type or a mixed signal encoding type using second type information. If the audio signal encoding type is a mixed signal encoding type, spectral data and a linear prediction coefficient is extracted from the audio signal; a difference signal for linear prediction by performing inverse frequency transformation over spectral data is generated; the audio signal is reconstructed by performing linear predictive coding over the linear prediction coefficient and the difference signal and the high-frequency domain signal is reconstructed using a base extension signal corresponding to the frequency domain of the reconstructed audio signal and range extension information. ^ EFFECT: higher efficiency of coding/decoding a audio signals. ^ 15 cl, 14 dwg
27 citations
30 Nov 2003
TL;DR: A framework for speech enhancement and robust speech recognition that exploits the harmonic structure of speech and achieves substantial gains in signal-to-noise ratio (SNR) of enhanced speech as well as considerable gains in accuracy of automatic speech recognition in very noisy conditions.
Abstract: We present a framework for speech enhancement and robust speech recognition that exploits the harmonic structure of speech. We achieve substantial gains in signal-to-noise ratio (SNR) of enhanced speech as well as considerable gains in accuracy of automatic speech recognition in very noisy conditions. The method exploits the harmonic structure of speech by employing a high frequency resolution speech model in the log-spectrum domain and reconstructs the signal from the estimated posteriors of the clean signal and the phases from the original noisy signal. We achieve a gain in SNR of 8.38 dB for enhancement of speech at 0 dB. We also present recognition results on the Aurora 2 data-set. At 0 dB SNR, we achieve a reduction of relative word error rate of 43.75% over the baseline, and 15.90% over the equivalent low-resolution algorithm.
27 citations
TL;DR: An efficient way to directly compute the full-resolution frequency estimates of speech and noise using coupled dictionaries, which results in improved word error rates for the speech recognition tasks using HMM-GMM and deep-neural network (DNN) based systems.
Abstract: Exemplar-based speech enhancement systems work by decomposing the noisy speech as a weighted sum of speech and noise exemplars stored in a dictionary and use the resulting speech and noise estimates to obtain a time-varying filter in the full-resolution frequency domain to enhance the noisy speech. To obtain the decomposition, exemplars sampled in lower dimensional spaces are preferred over the full-resolution frequency domain for their reduced computational complexity and the ability to better generalize to unseen cases. But the resulting filter may be sub-optimal as the mapping of the obtained speech and noise estimates to the full-resolution frequency domain yields a low-rank approximation. This paper proposes an efficient way to directly compute the full-resolution frequency estimates of speech and noise using coupled dictionaries: an input dictionary containing atoms from the desired exemplar space to obtain the decomposition and a coupled output dictionary containing exemplars from the full-resolution frequency domain. We also introduce modulation spectrogram features for the exemplar-based tasks using this approach. The proposed system was evaluated for various choices of input exemplars and yielded improved speech enhancement performances on the AURORA-2 and AURORA-4 databases. We further show that the proposed approach also results in improved word error rates (WERs) for the speech recognition tasks using HMM-GMM and deep-neural network (DNN) based systems.
27 citations