Unbiased coherent-to-diffuse ratio estimation for dereverberation

We investigate the estimation of the time- and frequency-dependent coherent-to-diffuse ratio (CDR) from the measured spatial coherence between two omnidirectional microphones. We illustrate the relationship between several known CDR es-timators using a geometric interpretation in the complex plane, discuss the problem of estimator bias, and propose unbiased versions of the estimators. Furthermore, we show that knowl-edge of either the direction of arrival (DOA) of the target source or the coherence of the noise field is sufficient for an unbiased CDR estimation. Finally, we apply the CDR estimators to the problem of dereverberation, using automatic speech recognition word error rate as objective performance measure

Small Microphone Array: Algorithms and Hardware

This report describes the processing algorithms and gives an overview of the hardware for the small microphone array unit in the IM2.RTMAP (Real-time Microphone Array Processing) project. The algorithms include techniques for speech enhancement, speaker localisation and speaker segmentation. The hardware consists of a DSP platform with 8 audio inputs and outputs, as well as a Fireware interface for communication with a PC or other modules

Structured Sparsity Models for Multiparty Speech Recovery from Reverberant Recordings

We tackle the multi-party speech recovery problem through modeling the acoustic of the reverberant chambers. Our approach exploits structured sparsity models to perform room modeling and speech recovery. We propose a scheme for characterizing the room acoustic from the unknown competing speech sources relying on localization of the early images of the speakers by sparse approximation of the spatial spectra of the virtual sources in a free-space model. The images are then clustered exploiting the low-rank structure of the spectro-temporal components belonging to each source. This enables us to identify the early support of the room impulse response function and its unique map to the room geometry. To further tackle the ambiguity of the reflection ratios, we propose a novel formulation of the reverberation model and estimate the absorption coefficients through a convex optimization exploiting joint sparsity model formulated upon spatio-spectral sparsity of concurrent speech representation. The acoustic parameters are then incorporated for separating individual speech signals through either structured sparse recovery or inverse filtering the acoustic channels. The experiments conducted on real data recordings demonstrate the effectiveness of the proposed approach for multi-party speech recovery and recognition.Comment: 31 page

On the application of minimum noise tracking to cancel cosine shaped residual noise

It has been shown recently that for coherence based dual microphone array speech enhancement systems, cross-spectral subtraction is an efficient technique aimed to reduce the correlated noise components. The zero-phase filtering criterion employed in these methods is derived from the standard coherence function that is modified to incorporate the noise cross power spectrum between the two channels. However, there has been limited success at applying coherence based filters when speech processing is carried out under relatively harsh acoustic conditions (SNR below -5dB) or when the speech and noise sources are closely spaced. We propose an alternative method that is effective, and that attempts to use a phase-based filtering criterion by substituting the cross power spectrum of the noisy signals received on the two channels by its real part. Then, a variant of the running minimum noise tracking procedure is applied on the estimated speech spectrum as an adaptive postfiltering to reduce the cosine shaped power spectrum of the remaining residual musical noise to a minimum spectral floor. Using that adaptive postfilter, a softdecision scheme is implemented to control the amount of noise suppression. Our preliminary results based on experiments conducted on real speech signals show an improved performance of the proposed method over the coherence based approaches. These results also show that it performs well on speech while producing less spectral distortion even in severe noisy conditions

Traitement paramétrique des signaux audio dans le contexte des prothèses auditives

Modèle à moyenne mobile > -- Modèle autorégressif > -- Modèle autorégressif à moyenne mobile > -- Remarque sur le lien entre AR, MA et ARMA -- Evaluation des paramètres d'un processus AR(p) -- Critères de sélection de l'ordre d'un modèle AR(p) -- Notion d'enveloppe spectrale -- Méthodes élaborées dans le domaine fréquentiel -- Méthodes élaborées dans le domaine de corrélation -- Réduction de bruit dans le domaine fréquentiel -- A two-microphone algorithm for speech enhancement -- State of the art -- Zelinski's approach in the case of two-microphone arrangement -- Two-microphone speech enhancement system -- Performance evaluation and results -- Réduction de bruit dans le domaine de corrélation -- Estimation de la puissance du bruit -- Compensation des effets du bruit -- Amélioration de la procédure de compensation -- Perspectives de développement -- Traitement paramétrique en présence de bruit -- Disposition du traitement combiné -- Amélioration de la précision de l'estimateur de variance du bruit

Implementation and evaluation of a low complexity microphone array for speaker recognition

Includes bibliographical references (leaves 83-86).This thesis discusses the application of a microphone array employing a noise canceling beamforming technique for improving the robustness of speaker recognition systems in a diffuse noise field

雑音特性の変動を伴う多様な環境で実用可能な音声強調

筑波大学 (University of Tsukuba)201

Noise reduction method for the heart sound records from digital stethoscope

In recent years, digital instruments have been widely used in the medical area with the rapid development of digital technology. The digital stethoscope, which converts the acoustic sound waves in to electrical signals and then amplifies them, is gradually replacing the conventional acoustic stethoscope with the advantage of additional usage such as restoring, replaying and processing the signals for optimal listening. As the sounds are transmitted in to electrical form, they can be recorded for further signal processing. One of the major problems with recording heart sounds is noise corruption. Although there are many solutions available to noise reduction problems, it was found that most of them are based on the assumption that the noise is an additive white noise [1]. More research is required to find different de-noising techniques based on the specific noise present. Therefore, this study is motivated to answer the research question: ‘How might the noise be reduced from the heart sound records collected from digital stethoscope with suitable noise reduction method’. This research question is divided into three sub-questions, including the identification of the noise spectrum, the design of noise reduction method and the assessment of the method. In the identification stage, five main kinds of noise were chosen and their characteristics and spectrums were discussed. Compared with different kinds of adaptive filters, the suitable noise reduction filter for this study was confirmed. To assess the effect of the method, 68 pieces of sound resources were collected for the experiment. These sounds were selected based on the noise they contain. A special noise reduction method was developed for the noise. This method was tested and assessed with those sound samples by two factors: the noise level and the noise kind. The results of the experiment showed the effect of the noise reduction method for each kind of noise. The outcomes indicated that this method was suitable for heart sound noise reduction. The findings of this study, including the analysis of noise level and noise kind, indicated and concluded that the chosen method for heart sound noise reduction performed well. This is perhaps the first attempt to understand and assess the noise reduction method with classified heart sound signals which are collected from the real healthcare environment. This noise reduction method may provide a de-noising solution for the specific noise present in heart sound