RTF-Based Binaural MVDR Beamformer Exploiting an External Microphone in a Diffuse Noise Field

Besides suppressing all undesired sound sources, an important objective of a binaural noise reduction algorithm for hearing devices is the preservation of the binaural cues, aiming at preserving the spatial perception of the acoustic scene. A well-known binaural noise reduction algorithm is the binaural minimum variance distortionless response beamformer, which can be steered using the relative transfer function (RTF) vector of the desired source, relating the acoustic transfer functions between the desired source and all microphones to a reference microphone. In this paper, we propose a computationally efficient method to estimate the RTF vector in a diffuse noise field, requiring an additional microphone that is spatially separated from the head-mounted microphones. Assuming that the spatial coherence between the noise components in the head-mounted microphone signals and the additional microphone signal is zero, we show that an unbiased estimate of the RTF vector can be obtained. Based on real-world recordings, experimental results for several reverberation times show that the proposed RTF estimator outperforms the widely used RTF estimator based on covariance whitening and a simple biased RTF estimator in terms of noise reduction and binaural cue preservation performance.Comment: Accepted at ITG Conference on Speech Communication 201

DNN-based mask estimation for distributed speech enhancement in spatially unconstrained microphone arrays

Deep neural network (DNN)-based speech enhancement algorithms in microphone arrays have now proven to be efficient solutions to speech understanding and speech recognition in noisy environments. However, in the context of ad-hoc microphone arrays, many challenges remain and raise the need for distributed processing. In this paper, we propose to extend a previously introduced distributed DNN-based time-frequency mask estimation scheme that can efficiently use spatial information in form of so-called compressed signals which are pre-filtered target estimations. We study the performance of this algorithm under realistic acoustic conditions and investigate practical aspects of its optimal application. We show that the nodes in the microphone array cooperate by taking profit of their spatial coverage in the room. We also propose to use the compressed signals not only to convey the target estimation but also the noise estimation in order to exploit the acoustic diversity recorded throughout the microphone array.Comment: Submitted to TASL

Effective Binaural Multi-Channel Processing Algorithm for Improved Environmental Presence

Binaural noise-reduction algorithms based on multi-channel Wiener filter (MWF) are promising techniques to be used in binaural assistive listening devices. The real-time implementation of the existing binaural MWF methods, however, involves challenges to increase the amount of noise reduction without imposing speech distortion, and at the same time preserving the binaural cues of both speech and noise components. Although significant efforts have been made in the literature, most developed methods so far have focused only on either the former or latter problem. This paper proposes an alternative binaural MWF algorithm that incorporates the non-stationarity of the signal components into the framework. The main objective is to design an algorithm that would be able to select the sources that are present in the environment. To achieve this, a modified speech presence probability (SPP) and a single-channel speech enhancement algorithm are utilized in the formulation. The resulting optimal filter also avoids the poor estimation of the second-order clean speech statistics, which is normally done by simple subtraction. Theoretical analysis and performance evaluation using realistic recorded data shows the advantage of the proposed method over the reference MWF solution in terms of the binaural cues preservation, as well as the noise reduction and speech distortion

A Multi-Channel Noise Estimator Based on Improved Minima Controlled Recursive Averaging for Speech Enhancement

This article introduces an extension of the improved minima-controlled recursive averaging noise estimation from single to multi-channel speech enhancement systems. With the spatial information of microphone array signals being fully exploited, more accurate estimate of the noise spectrum can be obtained over the single-channel counterpart. Computer simulation demonstrates superior performance of the proposed noise estimator in terms of noise tracking performance and noise estimation error. Furthermore, the use of the proposed technique with the multi-channel Wiener filter yields improved signal-to-noise ratio and speech distortion

Robust Multichannel Microphone Beamforming

In this thesis, a method for the design and implementation of a spatially robust multichannel microphone beamforming system is presented. A set of spatial correlation functions are derived for 2D and 3D far-field/near-field scenarios based on von Mises(-Fisher), Gaussian, and uniform source location distributions. These correlation functions are used to design spatially robust beamformers and blocking beamformers (nullformers) designed to enhance or suppress a known source, where the target source location is not perfectly known due to either an incorrect location estimate or movement of the target while the beamformers are active. The spatially robust beam/null-formers form signal and interferer plus noise references which can be further processed via a blind source separation algorithm to remove mutual components - removing the interference and sensor noise from the signal path and vice versa. The noise reduction performance of the combined beamforming and blind source separation system approaches that of a perfect information MVDR beamformer under reverberant conditions. It is demonstrated that the proposed algorithm can be implemented on low-power hardware with good performance on hardware similar to current mobile platforms using a four-element microphone array

Speech enhancement in binaural hearing protection devices

The capability of people to operate safely and effective under extreme noise conditions is dependent on their accesses to adequate voice communication while using hearing protection. This thesis develops speech enhancement algorithms that can be implemented in binaural hearing protection devices to improve communication and situation awareness in the workplace. The developed algorithms which emphasize low computational complexity, come with the capability to suppress noise while enhancing speech