Spectral subtractive type speech enhancement methods

In this paper spectral subtractive method and some of its modification are compared. Performance of spectral subtraction, its limitations, artifacts introduced by it, and spectral subtraction modifications for eliminating these artifacts are discussed in the paper in details. The algorithms are compared based on SNR improvement introduced by them. Spectrograms of speech enhanced by the algorithms, which show the algorithms performance and degree of speech distortion, are also presented

Amélioration psychoacoustique du filtrage de Wiener : quelques approches récentes et une nouvelle méthode

*Bruit musical, distorsion, filtre deWiener, psychoacoustique, signal de parol

A NEW SPEECH ENHANCEMENT TECHNIQUE USING PERCEPTUAL CONSTRAINED SPECTRAL WEIGHTING FACTORS

This paper deals with musical noise result from perceptual speech enhancement type algorithms and especially wiener filtering. Although perceptual speech enhancement methods perform better than the non perceptual methods, most of them still return annoying residual musical noise. This is due to the fact that if only noise above the noise masking threshold is filtered then noise below the noise masking threshold can become audible if its maskers are filtered. It can affect the performance of perceptual speech enhancement method that process audible noise only. In order to overcome this drawback here proposed a new speech enhancement technique. It aims to improve the quality of the enhanced speech signal provided by perceptual wiener filtering by controlling the latter via a second filter regarded as a psychoacoustically motivated weighting factor. The simulation results shows that the performance is improved compared to other perceptual speech enhancement method

A composite objective measure on subjective evaluation of speech enhancement algorithms

© 2018 Elsevier Ltd Speech enhancement algorithms is to improve speech quality, naturalness and intelligibility by eliminating the background noise and improving signal to noise ratio. There are several objective measures predicting the quality of noisy speech enhanced by noise suppression algorithms, and different objective measures capture different characteristics of the degraded signal. In this paper, the multiple linear regression analysis is used to obtain a composite measure which has high correlation with subjective tests, and the performance of several speech enhancement algorithms under car noise conditions is compared. The uncertainty of the results of the proposed measures on different speech enhancement algorithms is analyzed, and the reliability of the results is discussed

A NEW SPEECH ENHANCEMENT TECHNIQUE USING PERCEPTUAL WIENER FILTER

This paper deals with musical noise result from perceptual speech enhancement type algorithms and especially wiener filtering. Although perceptual speech enhancement methods perform better than the non perceptual methods, most of them still return annoying residual musical noise. This is due to the fact that if only noise above the noise masking threshold is filtered then noise below the noise masking threshold can become audible if its maskers are filtered. It can affect the performance of perceptual speech enhancement method that process audible noise only. In order to overcome this drawback here proposed a new speech enhancement technique. It aims to improve the quality of the enhanced speech signal provided by perceptual wiener filtering by controlling the latter via a second filter regarded as a psychoacoustically motivated weighting factor. The simulation results shows that the performance is improved compared to other perceptual speech enhancement method

A Neural Model for Self Organizing Feature Detectors and Classifiers in a Network Hierarchy

Many models of early cortical processing have shown how local learning rules can produce efficient, sparse-distributed codes in which nodes have responses that are statistically independent and low probability. However, it is not known how to develop a useful hierarchical representation, containing sparse-distributed codes at each level of the hierarchy, that incorporates predictive feedback from the environment. We take a step in that direction by proposing a biologically plausible neural network model that develops receptive fields, and learns to make class predictions, with or without the help of environmental feedback. The model is a new type of predictive adaptive resonance theory network called Receptive Field ARTMAP, or RAM. RAM self organizes internal category nodes that are tuned to activity distributions in topographic input maps. Each receptive field is composed of multiple weight fields that are adapted via local, on-line learning, to form smooth receptive ftelds that reflect; the statistics of the activity distributions in the input maps. When RAM generates incorrect predictions, its vigilance is raised, amplifying subtractive inhibition and sharpening receptive fields until the error is corrected. Evaluation on several classification benchmarks shows that RAM outperforms a related (but neurally implausible) model called Gaussian ARTMAP, as well as several standard neural network and statistical classifters. A topographic version of RAM is proposed, which is capable of self organizing hierarchical representations. Topographic RAM is a model for receptive field development at any level of the cortical hierarchy, and provides explanations for a variety of perceptual learning data.Defense Advanced Research Projects Agency and Office of Naval Research (N00014-95-1-0409

On pre-image iterations for speech enhancement

In this paper, we apply kernel PCA for speech enhancement and derive pre-image iterations for speech enhancement. Both methods make use of a Gaussian kernel. The kernel variance serves as tuning parameter that has to be adapted according to the SNR and the desired degree of de-noising. We develop a method to derive a suitable value for the kernel variance from a noise estimate to adapt pre-image iterations to arbitrary SNRs. In experiments, we compare the performance of kernel PCA and pre-image iterations in terms of objective speech quality measures and automatic speech recognition. The speech data is corrupted by white and colored noise at 0, 5, 10, and 15 dB SNR. As a benchmark, we provide results of the generalized subspace method, of spectral subtraction, and of the minimum mean-square error log-spectral amplitude estimator. In terms of the scores of the PEASS (Perceptual Evaluation Methods for Audio Source Separation) toolbox, the proposed methods achieve a similar performance as the reference methods. The speech recognition experiments show that the utterances processed by pre-image iterations achieve a consistently better word recognition accuracy than the unprocessed noisy utterances and than the utterances processed by the generalized subspace method

A review of differentiable digital signal processing for music and speech synthesis

The term “differentiable digital signal processing” describes a family of techniques in which loss function gradients are backpropagated through digital signal processors, facilitating their integration into neural networks. This article surveys the literature on differentiable audio signal processing, focusing on its use in music and speech synthesis. We catalogue applications to tasks including music performance rendering, sound matching, and voice transformation, discussing the motivations for and implications of the use of this methodology. This is accompanied by an overview of digital signal processing operations that have been implemented differentiably, which is further supported by a web book containing practical advice on differentiable synthesiser programming (https://intro2ddsp.github.io/). Finally, we highlight open challenges, including optimisation pathologies, robustness to real-world conditions, and design trade-offs, and discuss directions for future research