Clustered multi-channel dereverberation for ad-hoc microphone arrays

Abstract

A novel unsupervised multi-channel dereverberation approach in ad-hoc microphone arrays context based on removing microphones with relatively higher level of reverberation from the array and applying the dereverberation method on a subset of microphones with lower level of reverberation is proposed in this paper. This approach does not require any prior information about the number of microphones and their relative locations, however based on kurtosis of Linear Prediction (LP) residual signals, microphones located close to the active source are detected and utilized for the dereverberation process. The proposed method is a clustered enhancement method which can be applied with any dereverberation algorithm. The proposed method is not dependent on the recording setup so it requires no predefined threshold and it can be applied to unknown rooms with unseen speakers. Dereverberation results suggest that regardless of the applied dereverberation method, using a consciously chosen subset of microphones always yield better dereverberation results compared to blind use of all microphones. Abstract-A novel unsupervised multi-channel dereverberation approach in ad-hoc microphone arrays context based on removing microphones with relatively higher level of reverberation from the array and applying the dereverberation method on a subset of microphones with lower level of reverberation is proposed in this paper. This approach does not require any prior information about the number of microphones and their relative locations, however based on kurtosis of Linear Prediction (LP) residual signals, microphones located close to the active source are detected and utilized for the dereverberation process. The proposed method is a clustered enhancement method which can be applied with any dereverberation algorithm. The proposed method is not dependent on the recording setup so it requires no predefined threshold and it can be applied to unknown rooms with unseen speakers. Dereverberation results suggest that regardless of the applied dereverberation method, using a consciously chosen subset of microphones always yield better dereverberation results compared to blind use of all microphones.

Distributed Microphone Arrays, Emerging Speech and Audio Signal Processing Platforms: A Review

“…Ad hoc microphone arrays [1,3] consist of a set of recording devices (referred to as nodes [5]) randomly distributed in an acoustic environment to record an unknown acoustic scene with wide spatial coverage (Figure 1). The nodes can be identical [5] or different [6] in terms of their structure and number of elements [7,8] (Table 1). Ad hoc arrays eliminate the restrictions on microphone and source placement at fixed locations and facilitate dynamic and flexible recording experiences.…”

“…Three beamforming techniques (listed below) have been applied to ad hoc microphone arrays. Generally, delay and sum beamforming (DSB) [8,49] is more flexible and does not require limiting requirements, whereas more advanced beamforming methods assume some prior knowledge, which might not be the case for general scenarios.…”

“…Speech enhancement can cover applications, such as noise cancellation [22,83], beamforming [5], and echo cancellation [8]. These applications can be used separately or jointly as a combined speech enhancement method.…”

“…The goal is to retrieve ( ) from ( ) (25). A clustered speech enhancement approach based on beamforming and auto-regressive (AR) modelling of the speech signal was proposed and tested by Pasha and Ritz [8]. They showed that removing the microphone located far from the active source and exclusively applying the multi-channel dereverberation method for the microphones located nearer the source improved the speech enhancement performance.…”

“…As machine learning techniques are highly sensitive to the training set and parameters, using them in the flexible and uncertain distributed scenarios is very challenging. However, researchers have managed to define informative discriminative features for clustering and classifying microphones and signals, features that are independent of any specific setup [8,91] and can discriminate among the microphones within an ad hoc array regardless of array topology. These methods flexibly exclude a subset of the microphones (nodes) from the multi-channel process (e.g., multi-channel speech enhancement [8]) and are based on certain predefined selection criteria [5].…”