3 research outputs found
On the difference-to-sum power ratio of speech and wind noise based on the Corcos model
The difference-to-sum power ratio was proposed and used to suppress wind
noise under specific acoustic conditions. In this contribution, a general
formulation of the difference-to-sum power ratio associated with a mixture of
speech and wind noise is proposed and analyzed. In particular, it is assumed
that the complex coherence of convective turbulence can be modelled by the
Corcos model. In contrast to the work in which the power ratio was first
presented, the employed Corcos model holds for every possible air stream
direction and takes into account the lateral coherence decay rate. The obtained
expression is subsequently validated with real data for a dual microphone
set-up. Finally, the difference-to- sum power ratio is exploited as a spatial
feature to indicate the frame-wise presence of wind noise, obtaining improved
detection performance when compared to an existing multi-channel wind noise
detection approach.Comment: 5 pages, 3 figures, IEEE-ICSEE Eilat-Israel conference (special
session
Simulating Multi-channel Wind Noise Based on the Corcos Model
A novel multi-channel artificial wind noise generator based on a fluid
dynamics model, namely the Corcos model, is proposed. In particular, the model
is used to approximate the complex coherence function of wind noise signals
measured with closely-spaced microphones in the free-field and for
time-invariant wind stream direction and speed. Preliminary experiments focus
on a spatial analysis of recorded wind noise signals and the validation of the
Corcos model for diverse measurement set-ups. Subsequently, the Corcos model is
used to synthetically generate wind noise signals exhibiting the desired
complex coherence. The multi-channel generator is designed extending an
existing single-channel generator to create N mutually uncorrelated signals,
while the predefined complex coherence function is obtained exploiting an
algorithm developed to generate multi-channel non-stationary noise signals
under a complex coherence constraint. Temporal, spectral and spatial
characteristics of synthetic signals match with those observed in measured wind
noise. The artificial generation overcomes the time-consuming challenge of
collecting pure wind noise samples for noise reduction evaluations and provides
flexibility in the number of generated signals used in the simulations.Comment: 5 pages, 2 figures, IWAENC 201
Generating coherence-constrained multisensor signals using balanced mixing and spectrally smooth filters
Publisher Copyright: © 2021 Acoustical Society of America. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.The spatial properties of a noise field can be described by a spatial coherence function. Synthetic multichannel noise signals exhibiting a specific spatial coherence can be generated by properly mixing a set of uncorrelated, possibly non-stationary, signals. The mixing matrix can be obtained by decomposing the spatial coherence matrix. As proposed in a widely used method, the factorization can be performed by means of a Choleski or eigenvalue decomposition. In this work, the limitations of these two methods are discussed and addressed. In particular, specific properties of the mixing matrix are analyzed, namely, the spectral smoothness and the mix balance. The first quantifies the mixing matrix-filters variation across frequency and the second quantifies the number of input signals that contribute to each output signal. Three methods based on the unitary Procrustes solution are proposed to enhance the spectral smoothness, the mix balance, and both properties jointly. A performance evaluation confirms the improvements of the mixing matrix in terms of objective measures. Furthermore, the evaluation results show that the error between the target and the generated coherence is lowered by increasing the spectral smoothness of the mixing matrix.Peer reviewe