University of Illinois Urbana-Champaign

VECTOR-QUANTIZED SPEECH SEPARATION

Jiang, Xilin

Content Files
FA21-ECE499Thesis-Jiang, Xilin.pdf

Permalink

https://hdl.handle.net/2142/115012

Description

Title
VECTOR-QUANTIZED SPEECH SEPARATION
Author(s)
Jiang, Xilin
Issue Date
2021-12
Keyword(s)
  • source separation
  • vector quantization
  • signal representation
  • neural networks
Language
en
Abstract
In this paper, we present a novel method to address the single-channel speech separation problem. We propose a two-step training procedure for speech separation in a discrete latent space. In the first step, we learn multiple vector-quantized codebooks to optimize reconstruction and entropy and functions to transform between discrete codes and waveform. In the second step, we train multiple classifiers to select codes from codebooks to synthesize speech sources. The proposed method reaches comparable speech separation performance and is general enough to be applicable to other regression problems.
Graduation Semester
2022-10-15T16:08:38-05:00
Type of Resource
text
Handle URL
https://hdl.handle.net/2142/115012

Owning Collections

Undergraduate Theses at Illinois PRIMARY