Learning to Dequantize Speech Signals by Primal-dual Networks: an Approach for Acoustic Sensor Networks

Christoph Brauer; Ziyue Zhao; Dirk A. Lorenz; Tim Fingscheidt

ICASSP 2019accepted0 citations

Learning to Dequantize Speech Signals by Primal-dual Networks: an Approach for Acoustic Sensor Networks

Christoph Brauer, Ziyue Zhao, Dirk A. Lorenz, Tim Fingscheidt

Abstract

We introduce a method to improve the quality of simple scalar quantization in the context of acoustic sensor networks by combining ideas from sparse reconstruction, artificial neural networks and weighting filters. We start from the observation that optimization methods based on sparse reconstruction resemble the structure of a neural network. Hence, building upon a successful enhancement method, we unroll the algorithms and use this to build a neural network which we train to obtain enhanced decoding. In addition, the weighting filter from code-excited linear predictive (CELP) speech coding is integrated into the loss function of the neural network, achieving perceptually improved reconstructed speech. Our experiments show that our proposed trained methods allow for better speech reconstruction than the reference optimization methods.

BibTeX

@inproceedings{icassp2019_learningtodequan,
  title = {Learning to Dequantize Speech Signals by Primal-dual Networks: an Approach for Acoustic Sensor Networks},
  author = {Christoph Brauer and Ziyue Zhao and Dirk A. Lorenz and Tim Fingscheidt},
  booktitle = {ICASSP 2019},
  year = {2019}
}