Detail publikace

Integration of Variational Autoencoder and Spatial Clustering for Adaptive Multi-Channel Neural Speech Separation

ŽMOLÍKOVÁ, K. DELCROIX, M. BURGET, L. NAKATANI, T. ČERNOCKÝ, J.

Originální název

Integration of Variational Autoencoder and Spatial Clustering for Adaptive Multi-Channel Neural Speech Separation

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

In this paper, we propose a method combining variational autoencoder model of speech with a spatial clustering approach for multichannel speech separation. The advantage of integrating spatial clustering with a spectral model was shown in several works. As the spectral model, previous works used either factorial generative models of the mixed speech or discriminative neural networks. In our work, we combine the strengths of both approaches, by building a factorial model based on a generative neural network, a variational autoencoder. By doing so, we can exploit the modeling power of neural networks, but at the same time, keep a structured model. Such a model can be advantageous when adapting to new noise conditions as only the noise part of the model needs to be modified. We show experimentally, that our model significantly outperforms previous factorial model based on Gaussian mixture model (DOLPHIN), performs comparably to integration of permutation invariant training with spatial clustering, and enables us to easily adapt to new noise conditions.

Klíčová slova

Multi-channel speech separation, variational autoencoder, spatial clustering, DOLPHIN

Autoři

ŽMOLÍKOVÁ, K.; DELCROIX, M.; BURGET, L.; NAKATANI, T.; ČERNOCKÝ, J.

Vydáno

19. 1. 2021

Nakladatel

IEEE Signal Processing Society

Místo

Shenzhen - virtual

ISBN

978-1-7281-7066-4

Kniha

2021 IEEE Spoken Language Technology Workshop, SLT 2021 - Proceedings

Strany od

889

Strany do

896

Strany počet

8

URL

BibTex

@inproceedings{BUT175809,
  author="Kateřina {Žmolíková} and Marc {Delcroix} and Lukáš {Burget} and Tomohiro {Nakatani} and Jan {Černocký}",
  title="Integration of Variational Autoencoder and Spatial Clustering for Adaptive Multi-Channel Neural Speech Separation",
  booktitle="2021 IEEE Spoken Language Technology Workshop, SLT 2021 - Proceedings",
  year="2021",
  pages="889--896",
  publisher="IEEE Signal Processing Society",
  address="Shenzhen - virtual",
  doi="10.1109/SLT48900.2021.9383612",
  isbn="978-1-7281-7066-4",
  url="https://ieeexplore.ieee.org/document/9383612"
}