TechTalks from event: IEEE Workshop on Applications of Signal Processing to Audio and Acoustics

Microphone Arrays

  • Diffuseness Estimation With High Temporal Resolution Via Spatial Coherence Between Virtual First-Order Microphones Authors: Oliver Thiergart, International Audio Laboratories Erlangen, Giovanni Del Galdo, Fraunhofer Institute for Integrated Circuits IIS, and Emanu¨el Habets, International Audio Laboratories Erlangen
    The diffuseness of sound can be estimated with practical microphone setups by considering the spatial coherence between two microphone signals. In applications where small arrays of omnidirectional microphones are preferred, the diffuseness estimation is impaired by a high signal coherence in diffuse fields at lower frequencies, which is particularly problematic when carrying out the estimation with high temporal resolution. Therefore, we propose to exploit the spatial coherence between two virtual first-order microphones derived from the omnidirectional array. This represents a flexible method to accurately estimate the diffuseness in high- SNR regions at lower frequencies with high temporal resolution.
  • Spatial Soundfield Recording Over a Large Area using Distributed Higher Order Microphones Authors: Prasanga Samarasinghe, Thushara Abhayapala, The Australian National University, and Mark Poletti, Industrial Research Limited, New Zealand
    Recording and reproduction of spatial sound fields over a large area is an unresolved problem in acoustic signal processing. This is due to the the inherent restriction in recording higher order harmonic components using practically realizable microphone arrays. As the frequency increases and as the region of interest becomes large the number of microphones needed in effective recording increases beyond practicality. In this paper, we show how to use higher order microphones, distributed in a large area, to record and accurately reconstruct spatial sound fields. We use sound field coefficient translation between origins to combine distributed field recording to a single sound field over the entire region. We use simulation examples in (i) interior and (ii) exterior fields to corroborate our design.
  • Compressed sensing for acoustic response reconstruction: interpolation of the early part Authors: R´emi Mignot, Laurent Daudet, ESPCI ParisTech, and Franc¸ois Ollivier, Institut Jean Le Rond d'Alembert, UPMC
    The goal of this paper is to interpolate Room Impulse Responses (RIRs) within a whole volume, from a few measurements. We here focus on the early reflections, that have the key property of being sparse in the time domain: this can be exploited in a framework of model-based Compressed Sensing. Starting from a set of RIRs randomly sampled in space by a 3D microphone array, we use a modified Matching Pursuit algorithm to estimate the position of a small set of virtual sources. Then, the reconstruction of the RIRs at interpolated positions is performed using a projection onto a basis of monopoles. This approach is validated both by numerical and experimental measurements using a 120-microphone 3D array.
  • Block-wise Incremental Adaptation Algorithm for Maximum Kurtosis Beamforming Authors: Kenichi Kumatani, Disney Research, John McDonough, and Bhiksha Raj, Carnegie Mellon University
    In prior work, the current authors investigated beamforming algorithms that exploit the non-Gaussianity of human speech. The beamformers we proposed were designed to maximize the kurtosis or negentropy of the subband output subject to the distortionless constraint for the direction of interest. Such techniques are able to suppress interference signals as well as reverberation effects without signal cancellation. However, multiple passes of processing were required for each utterance in order to estimate the active weight vector. Hence, they were unsuitable for online implementation. In this work, we propose an online implementation of the maximum kurtosis beamformer. In a set of distant speech recognition experiments on far-field data, we demonstrate the effectiveness of the proposed technique. Compared to a single channel of the array, the proposed algorithm reduced word error rate from 15.4% to 6.5%.
  • Decorrelation for Adaptive Beamforming Applied to Arbitrarily Sampled Spherical Arrays Authors: Ines Hafizovic, University of Oslo and Squarehead Technology AS, Carl-Inge Colombo Nilsen, and Sverre Holm, University of Oslo
    Correlated signals lead to signal cancellation in adaptive beamformers applied to microphone arrays. This is commonly counteracted by spatial smoothing. Unfortunately, spatial smoothing can only be used with array geometries consisting of identical, shifted subarrays, making it unsuitable for spherical arrays in general. We suggest a transformation that makes spatial smoothing applicable to any wellsampled spherical array, and we show results for the case of Minimum Variance Distortionless Response (MVDR) beamforming.
  • Robust Beamforming and Steering of Arbitrary Beam Patterns using Spherical Arrays Authors: Joshua Atkins, Johns Hopkins University
    Spherical microphone and loudspeaker arrays present a compact method for analysis and synthesis of arbitrary threedimensional sound fields. Issues such as sensor self noise, sensor placement errors, and mismatch require robustness constraints in beamformer design. We present a method for designing robust beam-patterns with an arbitrary shape and an efficient method for steering the resulting patterns in three dimensions. This technique is used for two applications: synthesizing spherical microphone array recordings over loudspeaker arrays and binaurally over headphones with head tracking.