Toby Christian Lawin-Ore

Analysis of rate constraints for MWF-based noise reduction in acoustic sensor networks

In an acoustic sensor network, consisting of spatially distributed microphone nodes, a significant noise reduction can be achieved using the centralized multi-channel Wiener filter (MWF), requiring all available microphone signals in the entire network. However the limited bandwidth of the communication link typically does not al low to transmit all microphone signals between the different nodes. Recently, a distributed node-specific MWF-based noise reduction scheme has been presented, where each node only transmits a filtered combination of its microphone signals. In this paper, the performance gain of the centralized MWF and the distributed node-specific MWF-based scheme are analyzed as a function of the available band width of the communication link.

A multichannel Wiener filter with partial equalization for distributed microphones

In speech enhancement applications, the multichannel Wiener filter (MWF) is widely used to reduce noise and thus improve signal quality. The MWF performs noise reduction by estimating the desired signal component in one of the microphones, referred to as the reference microphone. However, for distributed microphones, the selection of the reference microphone has a significant impact on the broadband output SNR of the MWF, largely depending on the acoustical transfer function (ATF) between the desired source and the reference microphone. In this paper, a multichannel Wiener filtering approach using a soft combined reference is presented. Simulation results show that the proposed scheme leads to a higher broadband output SNR compared to an arbitrarily selected reference microphone, moreover achieving a partial equalization of the overall acoustic system.

Analysis of the average performance of the multi-channel Wiener filter for distributed microphone arrays using statistical room acoustics

For most multi-microphone noise reduction algorithms, e.g. the multi-channel Wiener filter (MWF), it is well known that the performance depends on the acoustic scenario at hand, i.e. the used microphone array, the position of the desired source and the noise field. Since the position of the desired source is not always known a priori, it is of great interest in many applications to be able to compute the average performance for a specific microphone array, which can be obtained by averaging the performance over all feasible source positions. A possible but either time-consuming or computationally complex approach to achieve this is to use measurements or simulations for a large number of source positions.In this paper, we propose to use the statistical properties of the acoustical transfer functions (ATFs) between the desired source and the microphones to derive analytical expressions for the spatially averaged performance measures (output SNR, noise reduction, speech distortion) of the MWF, assuming a homogeneous and known noise field. In addition, we show that although the spatially averaged performance measures do not express the performance of the MWF for a given position of the source and/or the microphones, they can be used to derive approximate analytical expressions for the average performance of the MWF for a given position of the microphones. Experimental results show that the proposed analytical expressions can be used to easily compare the performance of different microphone arrays, e.g. in an acoustic sensor network, without having to measure or numerically simulate a large number of ATFs. HighlightsWe derive analytical expressions for the average performance measures of the MWF.Expressions depend on room properties and distance between source and microphones.Analytically computed average performance fits well with numerically simulated performance.An efficient way to compare the performance of different microphone arrays is given.Performance can be computed without having to measure or numerically simulate ATFs.

Alternative formulation and robustness analysis of the multichannel wiener filter for spatially distributed microphones

The multichannel Wiener filter (MWF) is a well-known multi-microphone noise reduction technique, which aims to estimate the speech component in one of the microphone signals. Assuming a single speech source, the rank-one property of the speech correlation matrix can be exploited to derive the so-called rank-one MWF (R1-MWF). In this paper, we present an alternative formulation of the MWF (A-MWF), which exploits the assumed rank-one property of the speech correlation matrix in a different way as the R1-MWF. Furthermore, we present a theoretical robustness analysis of the different MWF formulations in presence of spatially white noise. Experimental results show that similarly to the R1-MWF, the proposed A-MWF is less sensitive to estimation errors of the speech correlation matrix and yields a higher output SNR than the standard MWF.

Phase reference for the generalized multichannel Wiener filter

The multichannel Wiener filter (MWF) is a well-established noise reduction technique for speech processing. Most commonly, the speech component in a selected reference microphone is estimated. The choice of this reference microphone influences the broadband output signal-to-noise ratio (SNR) as well as the speech distortion. Recently, a generalized formulation for the MWF (G-MWF) was proposed that uses a weighted sum of the individual transfer functions from the speaker to the microphones to form a better speech reference resulting in an improved broadband output SNR. For the MWF, the influence of the phase reference is often neglected, because it has no impact on the narrow-band output SNR. The G-MWF allows an arbitrary choice of the phase reference especially in the context of spatially distributed microphones.In this work, we demonstrate that the phase reference determines the overall transfer function and hence has an impact on both the speech distortion and the broadband output SNR. We propose two speech references that achieve a better signal-to-reverberation ratio (SRR) and an improvement in the broadband output SNR. Both proposed references are based on the phase of a delay-and-sum beamformer. Hence, the time-difference-of-arrival (TDOA) of the speech source is required to align the signals. The different techniques are compared in terms of SRR and SNR performance.

Average output SNR of the multichannel Wiener filter using statistical room acoustics

The performance of the multi-channel Wiener filter (MWF), which is often used for noise reduction in speech enhancement applications, depends on the noise field and on the acoustic transfer functions (ATFs) between the desired source and the microphone array. Recently, using statistical room acoustics an analytical expression for the spatially averaged output SNR, given the relative distance between the source and the microphone array, has been derived for the MWF in a diffuse noise field, requiring only the room properties to be known. In this paper, we show that this analytical expression can be extended to compute the average output SNR of the MWF for a specific microphone configuration, enabling to compare the performance of different microphone configurations, e.g. in an acoustic sensor network. Simulation results show that the average output SNR obtained using the statistical properties of ATFs is similar to the average output SNR obtained using simulated ATFs, therefore providing an efficient way to compare different microphone configurations.