Learning Filterbanks for End-to-End Acoustic Beamforming
- URL: http://arxiv.org/abs/2111.04614v1
- Date: Mon, 8 Nov 2021 16:36:34 GMT
- Title: Learning Filterbanks for End-to-End Acoustic Beamforming
- Authors: Samuele Cornell, Manuel Pariente, Fran\c{c}ois Grondin, Stefano
Squartini
- Abstract summary: Recent work on monaural source separation has shown that performance can be increased by using fully learned filterbanks with short windows.
On the other hand, for conventional beamforming techniques, performance increases with long analysis windows.
In this work we try to bridge the gap between these two worlds and explore fully end-to-end hybrid neural beamforming.
- Score: 8.721077261941234
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Recent work on monaural source separation has shown that performance can be
increased by using fully learned filterbanks with short windows. On the other
hand it is widely known that, for conventional beamforming techniques,
performance increases with long analysis windows. This applies also to most
hybrid neural beamforming methods which rely on a deep neural network (DNN) to
estimate the spatial covariance matrices. In this work we try to bridge the gap
between these two worlds and explore fully end-to-end hybrid neural beamforming
in which, instead of using the Short-Time-Fourier Transform, also the analysis
and synthesis filterbanks are learnt jointly with the DNN. In detail, we
explore two different types of learned filterbanks: fully learned and analytic.
We perform a detailed analysis using the recent Clarity Challenge data and show
that by using learnt filterbanks is possible to surpass oracle-mask based
beamforming for short windows.
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