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

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

DNN-Assisted Speech Enhancement Approaches Incorporating Phase Information

Speech enhancement is a widely adopted technique that removes the interferences in a corrupted speech to improve the speech quality and intelligibility. Speech enhancement methods can be implemented in either time domain or time-frequency (T-F) domain. Among various proposed methods, the time-frequency domain methods, which synthesize the enhanced speech with the estimated magnitude spectrogram and the noisy phase spectrogram, gain the most popularity in the past few decades. However, this kind of techniques tend to ignore the importance of phase processing. To overcome this problem, the thesis aims to jointly enhance the magnitude and phase spectra by means of the most recent deep neural networks (DNNs). More specifically, three major contributions are presented in this thesis. First, we present new schemes based on the basic Kalman filter (KF) to remove the background noise in the noisy speech in time domain, where the KF acts as joint estimator for both the magnitude and phase spectra of speech. A DNN-augmented basic KF is first proposed, where DNN is applied for estimating key parameters in the KF, namely the linear prediction coefficients (LPCs). By training the DNN with a large database and making use of the powerful learning ability of DNN, the proposed algorithm is able to estimate LPCs from noisy speech more accurately and robustly, leading to an improved performance as compared to traditional KF based approaches in speech enhancement. We further present a high-frequency (HF) component restoration algorithm to extenuate the degradation in the HF regions of the Kalman-filtered speech, in which the DNN-based bandwidth extension is applied to estimate the magnitude of HF component from the low-frequency (LF) counterpart. By incorporating the restoration algorithm, the enhanced speech suffers less distortion in the HF component. Moreover, we propose a hybrid speech enhancement system that exploits DNN for speech reconstruction and Kalman filtering for further denoising. Two separate networks are adopted in the estimation of magnitude spectrogram and LPCs of the clean speech, respectively. The estimated clean magnitude spectrogram is combined with the phase of the noisy speech to reconstruct the estimated clean speech. A KF with the estimated parameters is then utilized to remove the residual noise in the reconstructed speech. The proposed hybrid system takes advantages of both the DNN-based reconstruction and traditional Kalman filtering, and can work reliably in either matched or unmatched acoustic environments. Next, we incorporate the DNN-based parameter estimation scheme in two advanced KFs: subband KF and colored-noise KF. The DNN-augmented subband KF method decomposes the noisy speech into several subbands, and performs Kalman filtering to each subband speech, where the parameters of the KF are estimated by the trained DNN. The final enhanced speech is then obtained by synthesizing the enhanced subband speeches. In the DNN-augmented colored-noise KF system, both clean speech and noise are modelled as autoregressive (AR) processes, whose parameters comprise the LPCs and the driving noise variances. The LPCs are obtained through training a multi-objective DNN, while the driving noise variances are obtained by solving an optimization problem aiming to minimize the difference between the modelled and observed AR spectra of the noisy speech. The colored-noise Kalman filter with DNN-estimated parameters is then applied to the noisy speech for denoising. A post-subtraction technique is adopted to further remove the residual noise in the Kalman-filtered speech. Extensive computer simulations show that the two proposed advanced KF systems achieve significant performance gains when compared to conventional Kalman filter based algorithms as well as recent DNN-based methods under both seen and unseen noise conditions. Finally, we focus on the T-F domain speech enhancement with masking technique, which aims to retain the speech dominant components and suppress the noise dominant parts of the noisy speech. We first derive a new type of mask, namely constrained ratio mask (CRM), to better control the trade-off between speech distortion and residual noise in the enhanced speech. The CRM is estimated with a trained DNN based on the input noisy feature set and is applied to the noisy magnitude spectrogram for denoising. We further extend the CRM to the complex spectrogram estimation, where the enhanced magnitude spectrogram is obtained with the CRM, while the estimated phase spectrogram is reconstructed with the noisy phase spectrogram and the phase derivatives. Performance evaluation reveals our proposed CRM outperforms several traditional masks in terms of objective metrics. Moreover, the enhanced speech resulting from the CRM based complex spectrogram estimation has a better speech quality than that obtained without using phase reconstruction

Application of sound source separation methods to advanced spatial audio systems

This thesis is related to the field of Sound Source Separation (SSS). It addresses the development and evaluation of these techniques for their application in the resynthesis of high-realism sound scenes by means of Wave Field Synthesis (WFS). Because the vast majority of audio recordings are preserved in twochannel stereo format, special up-converters are required to use advanced spatial audio reproduction formats, such as WFS. This is due to the fact that WFS needs the original source signals to be available, in order to accurately synthesize the acoustic field inside an extended listening area. Thus, an object-based mixing is required. Source separation problems in digital signal processing are those in which several signals have been mixed together and the objective is to find out what the original signals were. Therefore, SSS algorithms can be applied to existing two-channel mixtures to extract the different objects that compose the stereo scene. Unfortunately, most stereo mixtures are underdetermined, i.e., there are more sound sources than audio channels. This condition makes the SSS problem especially difficult and stronger assumptions have to be taken, often related to the sparsity of the sources under some signal transformation. This thesis is focused on the application of SSS techniques to the spatial sound reproduction field. As a result, its contributions can be categorized within these two areas. First, two underdetermined SSS methods are proposed to deal efficiently with the separation of stereo sound mixtures. These techniques are based on a multi-level thresholding segmentation approach, which enables to perform a fast and unsupervised separation of sound sources in the time-frequency domain. Although both techniques rely on the same clustering type, the features considered by each of them are related to different localization cues that enable to perform separation of either instantaneous or real mixtures.Additionally, two post-processing techniques aimed at improving the isolation of the separated sources are proposed. The performance achieved by several SSS methods in the resynthesis of WFS sound scenes is afterwards evaluated by means of listening tests, paying special attention to the change observed in the perceived spatial attributes. Although the estimated sources are distorted versions of the original ones, the masking effects involved in their spatial remixing make artifacts less perceptible, which improves the overall assessed quality. Finally, some novel developments related to the application of time-frequency processing to source localization and enhanced sound reproduction are presented.Cobos Serrano, M. (2009). Application of sound source separation methods to advanced spatial audio systems [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/8969Palanci

Intelligibility model optimisation approaches for speech pre-enhancement

The goal of improving the intelligibility of broadcast speech is being met by a recent new direction in speech enhancement: near-end intelligibility enhancement. In contrast to the conventional speech enhancement approach that processes the corrupted speech at the receiver-side of the communication chain, the near-end intelligibility enhancement approach pre-processes the clean speech at the transmitter-side, i.e. before it is played into the environmental noise. In this work, we describe an optimisation-based approach to near-end intelligibility enhancement using models of speech intelligibility to improve the intelligibility of speech in noise. This thesis first presents a survey of speech intelligibility models and how the adverse acoustic conditions affect the intelligibility of speech. The purpose of this survey is to identify models that we can adopt in the design of the pre-enhancement system. Then, we investigate the strategies humans use to increase speech intelligibility in noise. We then relate human strategies to existing algorithms for near-end intelligibility enhancement. A closed-loop feedback approach to near-end intelligibility enhancement is then introduced. In this framework, speech modifications are guided by a model of intelligibility. For the closed-loop system to work, we develop a simple spectral modification strategy that modifies the first few coefficients of an auditory cepstral representation such as to maximise an intelligibility measure. We experiment with two contrasting measures of objective intelligibility. The first, as a baseline, is an audibility measure named 'glimpse proportion' that is computed as the proportion of the spectro-temporal representation of the speech signal that is free from masking. We then propose a discriminative intelligibility model, building on the principles of missing data speech recognition, to model the likelihood of specific phonetic confusions that may occur when speech is presented in noise. The discriminative intelligibility measure is computed using a statistical model of speech from the speaker that is to be enhanced. Interim results showed that, unlike the glimpse proportion based system, the discriminative based system did not improve intelligibility. We investigated the reason behind that and we found that the discriminative based system was not able to target the phonetic confusion with the fixed spectral shaping. To address that, we introduce a time-varying spectral modification. We also propose to perform the optimisation on a segment-by-segment basis which enables a robust solution against the fluctuating noise. We further combine our system with a noise-independent enhancement technique, i.e. dynamic range compression. We found significant improvement in non-stationary noise condition, but no significant differences to the state-of-the art system (spectral shaping and dynamic range compression) where found in stationary noise condition

Evaluation of audio source separation in the context of 3D audio

The emergence and broader availability of 3D audio systems allows for new possibilities in mixing, post-production and playback of audio content. Used in movie post-production for cinemas, as special effect by disk jockeys for example and even for live concerts, 3D rendering immerses the listener more than ever before. When existing audio material is to be employed, Audio Source Separation (ASS) techniques enable the extraction of single sources from a mixture. Modern mixing approaches for 3D audio do not assign individual gains and delays for each source in every channel. A sound scene is rather designed, with individual sources treated as objects to be placed within a scene. The hardware layer is mostly irrelevant for mixing in such a setting. ASS is therefore a valuable tool to ¿disassemble¿ amore traditional monophonic, stereophonic, or multichannel mix. However, due to the complexity of the ASS problem, extracted sources are subject to degradations. While state-of-the-art objective measures for ASS quality build on monaural auditory models, they don¿t take into account binaural listening and the psychoacoustic phenomena that are involved, such as binaural unmasking. In this thesis, an extension to Perceptive Evaluation Methods for Audio Source Separation (PEASS) [41] is proposed with spatial rendering in mind. Additionally a new binaural model for ASS evaluation in the context of 3D audio is presented. The performance of the basic and extended versions of PEASS, as well as the proposed binaural model is evaluated in two subjective studies. The first study is conducted with binaural spatialisation presented over headphones, while the second experiment uses a 3DWave Field Synthesis (WFS) system. A set of artificial ASS degradation algorithms is proposed and used for the stimuli of the subjective studies. Results of the studies indicate monotonic decrease of the perceived quality as a function of the amounts of degradations introduced. The most important degradation is found to be target distortion, followed by onset misallocation and musical noise-type artifacts. Additionally, spatialising the extracted target source away from the residue or having it louder than the residue negatively affects the results, indicating a perceived quality degradation. In 3D WFS conditions, results show evidence for monaural and binaural unmasking. The performance of the proposed binauralmodel is consistently superior to that of the basic or extended PEASS versions. In the binaural spatialisation experiment, a correlation coefficient of 0.60 between subjective and objective results is achieved, versus 0.57 and 0.53 with the extended and basic PEASS version respectively. For the 3D WFS study, the binaural model achieves 0.67 prediction accuracy whereas both PEASS versions get 0.57. The perceptual validity of the WFS formulation is also verified in a localisation experiment. Vertical localisation is found to be nearly as good as physical source localisation for an extended listening area with localisation precision of 6± - 9±. The response time is also used as an indicator of localisation performance

Modulation vocoder for analysis, processing and synthesis of audio signals with application to frequency selective pitch transposition

[no abstract

Perceptual models in speech quality assessment and coding

The ever-increasing demand for good communications/toll quality speech has created a renewed interest into the perceptual impact of rate compression. Two general areas are investigated in this work, namely speech quality assessment and speech coding. In the field of speech quality assessment, a model is developed which simulates the processing stages of the peripheral auditory system. At the output of the model a "running" auditory spectrum is obtained. This represents the auditory (spectral) equivalent of any acoustic sound such as speech. Auditory spectra from coded speech segments serve as inputs to a second model. This model simulates the information centre in the brain which performs the speech quality assessment. [Continues.

A Parametric Approach for Efficient Speech Storage, Flexible Synthesis and Voice Conversion

During the past decades, many areas of speech processing have benefited from the vast increases in the available memory sizes and processing power. For example, speech recognizers can be trained with enormous speech databases and high-quality speech synthesizers can generate new speech sentences by concatenating speech units retrieved from a large inventory of speech data. However, even in today's world of ever-increasing memory sizes and computational resources, there are still lots of embedded application scenarios for speech processing techniques where the memory capacities and the processor speeds are very limited. Thus, there is still a clear demand for solutions that can operate with limited resources, e.g., on low-end mobile devices. This thesis introduces a new segmental parametric speech codec referred to as the VLBR codec. The novel proprietary sinusoidal speech codec designed for efficient speech storage is capable of achieving relatively good speech quality at compression ratios beyond the ones offered by the standardized speech coding solutions, i.e., at bitrates of approximately 1 kbps and below. The efficiency of the proposed coding approach is based on model simplifications, mode-based segmental processing, and the method of adaptive downsampling and quantization. The coding efficiency is also further improved using a novel flexible multi-mode matrix quantizer structure and enhanced dynamic codebook reordering. The compression is also facilitated using a new perceptual irrelevancy removal method. The VLBR codec is also applied to text-to-speech synthesis. In particular, the codec is utilized for the compression of unit selection databases and for the parametric concatenation of speech units. It is also shown that the efficiency of the database compression can be further enhanced using speaker-specific retraining of the codec. Moreover, the computational load is significantly decreased using a new compression-motivated scheme for very fast and memory-efficient calculation of concatenation costs, based on techniques and implementations used in the VLBR codec. Finally, the VLBR codec and the related speech synthesis techniques are complemented with voice conversion methods that allow modifying the perceived speaker identity which in turn enables, e.g., cost-efficient creation of new text-to-speech voices. The VLBR-based voice conversion system combines compression with the popular Gaussian mixture model based conversion approach. Furthermore, a novel method is proposed for converting the prosodic aspects of speech. The performance of the VLBR-based voice conversion system is also enhanced using a new approach for mode selection and through explicit control of the degree of voicing. The solutions proposed in the thesis together form a complete system that can be utilized in different ways and configurations. The VLBR codec itself can be utilized, e.g., for efficient compression of audio books, and the speech synthesis related methods can be used for reducing the footprint and the computational load of concatenative text-to-speech synthesizers to levels required in some embedded applications. The VLBR-based voice conversion techniques can be used to complement the codec both in storage applications and in connection with speech synthesis. It is also possible to only utilize the voice conversion functionality, e.g., in games or other entertainment applications