2,788 research outputs found
On End-to-end Multi-channel Time Domain Speech Separation in Reverberant Environments
This paper introduces a new method for multi-channel time domain speech
separation in reverberant environments. A fully-convolutional neural network
structure has been used to directly separate speech from multiple microphone
recordings, with no need of conventional spatial feature extraction. To reduce
the influence of reverberation on spatial feature extraction, a dereverberation
pre-processing method has been applied to further improve the separation
performance. A spatialized version of wsj0-2mix dataset has been simulated to
evaluate the proposed system. Both source separation and speech recognition
performance of the separated signals have been evaluated objectively.
Experiments show that the proposed fully-convolutional network improves the
source separation metric and the word error rate (WER) by more than 13% and 50%
relative, respectively, over a reference system with conventional features.
Applying dereverberation as pre-processing to the proposed system can further
reduce the WER by 29% relative using an acoustic model trained on clean and
reverberated data.Comment: Presented at IEEE ICASSP 202
Deep Learning for Audio Signal Processing
Given the recent surge in developments of deep learning, this article
provides a review of the state-of-the-art deep learning techniques for audio
signal processing. Speech, music, and environmental sound processing are
considered side-by-side, in order to point out similarities and differences
between the domains, highlighting general methods, problems, key references,
and potential for cross-fertilization between areas. The dominant feature
representations (in particular, log-mel spectra and raw waveform) and deep
learning models are reviewed, including convolutional neural networks, variants
of the long short-term memory architecture, as well as more audio-specific
neural network models. Subsequently, prominent deep learning application areas
are covered, i.e. audio recognition (automatic speech recognition, music
information retrieval, environmental sound detection, localization and
tracking) and synthesis and transformation (source separation, audio
enhancement, generative models for speech, sound, and music synthesis).
Finally, key issues and future questions regarding deep learning applied to
audio signal processing are identified.Comment: 15 pages, 2 pdf figure
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