Linear prediction based dereverberation for spherical microphone arrays

Dereverberation is an important preprocessing step in many speech systems, both for human and machine listening. In many situations, including robot audition, the sound sources of interest can be incident from any direction. In such circumstances, a spherical microphone array allows direction of arrival estimation which is free of spatial aliasing and directionindependent beam patterns can be formed. This contribution formulates the Weighted Prediction Error algorithm in the spherical harmonic domain and compares the performance to a space domain implementation. Simulation results demonstrate that performing dereverberation in the spherical harmonic domain allows many more microphones to be used without increasing the computational cost. The benefit of using many microphones is particularly apparent at low signal to noise ratios, where for the conditions tested up to 71% improvement in speech-to-reverberation modulation ratio was achieved

Spherical microphone array acoustic rake receivers

Several signal independent acoustic rake receivers are proposed for speech dereverberation using spherical microphone arrays. The proposed rake designs take advantage of multipaths, by separately capturing and combining early reflections with the direct path. We investigate several approaches in combining reflections with the direct path source signal, including the development of beam patterns that point nulls at all preceding reflections. The proposed designs are tested in experimental simulations and their dereverberation performances evaluated using objective measures. For the tested configuration, the proposed designs achieve higher levels of dereverberation compared to conventional signal independent beamforming systems; achieving up to 3.6 dB improvement in the direct-to-reverberant ratio over the plane-wave decomposition beamformer

Multichannel equalisation for high-order spherical microphone arrays using beamformed channels

High-order spherical microphone arrays offer many practical benefits including relatively fine spatial resolution in all directions and rotation invariant processing using eigenbeams. Spatial filtering can reduce interference from noise and reverberation but in even moderately reverberant environments the beam pattern fails to suppress reverberation to a level adequate for typical applications. In this paper we investigate the feasibility of applying dereverberation by considering multiple beamformer outputs as channels to be dereverberated. In one realisation we process directly in the spherical harmonic domain where the beampatterns are mutually orthogonal. In a second realisation, which is not limited to spherical microphone arrays, beams are pointed in the direction of dominant reflections. Simulations demonstrate that in both cases reverberation is significantly reduced and, in the best case, clarity index is improved by 15 dB

Spatial Diffuseness Features for DNN-Based Speech Recognition in Noisy and Reverberant Environments

We propose a spatial diffuseness feature for deep neural network (DNN)-based automatic speech recognition to improve recognition accuracy in reverberant and noisy environments. The feature is computed in real-time from multiple microphone signals without requiring knowledge or estimation of the direction of arrival, and represents the relative amount of diffuse noise in each time and frequency bin. It is shown that using the diffuseness feature as an additional input to a DNN-based acoustic model leads to a reduced word error rate for the REVERB challenge corpus, both compared to logmelspec features extracted from noisy signals, and features enhanced by spectral subtraction.Comment: accepted for ICASSP201

Microphone array signal processing for robot audition

Robot audition for humanoid robots interacting naturally with humans in an unconstrained real-world environment is a hitherto unsolved challenge. The recorded microphone signals are usually distorted by background and interfering noise sources (speakers) as well as room reverberation. In addition, the movements of a robot and its actuators cause ego-noise which degrades the recorded signals significantly. The movement of the robot body and its head also complicates the detection and tracking of the desired, possibly moving, sound sources of interest. This paper presents an overview of the concepts in microphone array processing for robot audition and some recent achievements

Spatial dissection of a soundfield using spherical harmonic decomposition

A real-world soundfield is often contributed by multiple desired and undesired sound sources. The performance of many acoustic systems such as automatic speech recognition, audio surveillance, and teleconference relies on its ability to extract the desired sound components in such a mixed environment. The existing solutions to the above problem are constrained by various fundamental limitations and require to enforce different priors depending on the acoustic condition such as reverberation and spatial distribution of sound sources. With the growing emphasis and integration of audio applications in diverse technologies such as smart home and virtual reality appliances, it is imperative to advance the source separation technology in order to overcome the limitations of the traditional approaches. To that end, we exploit the harmonic decomposition model to dissect a mixed soundfield into its underlying desired and undesired components based on source and signal characteristics. By analysing the spatial projection of a soundfield, we achieve multiple outcomes such as (i) soundfield separation with respect to distinct source regions, (ii) source separation in a mixed soundfield using modal coherence model, and (iii) direction of arrival (DOA) estimation of multiple overlapping sound sources through pattern recognition of the modal coherence of a soundfield. We first employ an array of higher order microphones for soundfield separation in order to reduce hardware requirement and implementation complexity. Subsequently, we develop novel mathematical models for modal coherence of noisy and reverberant soundfields that facilitate convenient ways for estimating DOA and power spectral densities leading to robust source separation algorithms. The modal domain approach to the soundfield/source separation allows us to circumvent several practical limitations of the existing techniques and enhance the performance and robustness of the system. The proposed methods are presented with several practical applications and performance evaluations using simulated and real-life dataset

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

Joint source localization and dereverberation by sound field interpolation using sparse regularization

In this paper, source localization and dereverberation are formulated jointly as an inverse problem. The inverse problem consists in the interpolation of the sound field measured by a set of microphones by matching the recorded sound pressure with that of a particular acoustic model. This model is based on a collection of equivalent sources creating either spherical or plane waves. In order to achieve meaningful results, spatial, spatio-temporal and spatio-spectral sparsity can be promoted in the signals originating from the equivalent sources. The inverse problem consists of a large-scale optimization problem that is solved using a first order matrix-free optimization algorithm. It is shown that once the equivalent source signals capable of effectively interpolating the sound field are obtained, they can be readily used to localize a speech sound source in terms of Direction of Arrival (DOA) and to perform dereverberation in a highly reverberant environment