856 research outputs found
Analysis of a non-minimum phase acoustic beamformer
The two input Griffiths-Jim acoustic beamformer is analysed in the frequency domain using
a Wiener type formulation. Unlike previous solutions the approach here is to look at the
problem of non-minimum phase acoustic transfer functions which are encountered in many
real filtering problems. The polynomial transfer function approach gives an elegant way of
obtaining the frequency response of the beamformer and gives new insight to the problem
in general
Rank-1 Constrained Multichannel Wiener Filter for Speech Recognition in Noisy Environments
Multichannel linear filters, such as the Multichannel Wiener Filter (MWF) and
the Generalized Eigenvalue (GEV) beamformer are popular signal processing
techniques which can improve speech recognition performance. In this paper, we
present an experimental study on these linear filters in a specific speech
recognition task, namely the CHiME-4 challenge, which features real recordings
in multiple noisy environments. Specifically, the rank-1 MWF is employed for
noise reduction and a new constant residual noise power constraint is derived
which enhances the recognition performance. To fulfill the underlying rank-1
assumption, the speech covariance matrix is reconstructed based on eigenvectors
or generalized eigenvectors. Then the rank-1 constrained MWF is evaluated with
alternative multichannel linear filters under the same framework, which
involves a Bidirectional Long Short-Term Memory (BLSTM) network for mask
estimation. The proposed filter outperforms alternative ones, leading to a 40%
relative Word Error Rate (WER) reduction compared with the baseline Weighted
Delay and Sum (WDAS) beamformer on the real test set, and a 15% relative WER
reduction compared with the GEV-BAN method. The results also suggest that the
speech recognition accuracy correlates more with the Mel-frequency cepstral
coefficients (MFCC) feature variance than with the noise reduction or the
speech distortion level.Comment: for Computer Speech and Languag
A Low-Cost Robust Distributed Linearly Constrained Beamformer for Wireless Acoustic Sensor Networks with Arbitrary Topology
We propose a new robust distributed linearly constrained beamformer which
utilizes a set of linear equality constraints to reduce the cross power
spectral density matrix to a block-diagonal form. The proposed beamformer has a
convenient objective function for use in arbitrary distributed network
topologies while having identical performance to a centralized implementation.
Moreover, the new optimization problem is robust to relative acoustic transfer
function (RATF) estimation errors and to target activity detection (TAD)
errors. Two variants of the proposed beamformer are presented and evaluated in
the context of multi-microphone speech enhancement in a wireless acoustic
sensor network, and are compared with other state-of-the-art distributed
beamformers in terms of communication costs and robustness to RATF estimation
errors and TAD errors
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