On Loss Functions for Supervised Monaural Time-Domain Speech Enhancement

Morten Kolbæk, Zheng-Hua Tan, Søren Holdt Jensen, Jesper Jensen

Research output: Contribution to journalJournal articleResearchpeer-review

87 Citations (Scopus)
179 Downloads (Pure)

Abstract

Many deep learning-based speech enhancement algorithms are designed to minimize the mean-square error (MSE) in some transform domain between a predicted and a target speech signal. However, optimizing for MSE does not necessarily guarantee high speech quality or intelligibility, which is the ultimate goal of many speech enhancement algorithms. Additionally, only little is known about the impact of the loss function on the emerging class of time-domain deep learning-based speech enhancement systems. We study how popular loss functions influence the performance of time-domain deep learning-based speech enhancement systems. First, we demonstrate that perceptually inspired loss functions might be advantageous over classical loss functions like MSE. Furthermore, we show that the learning rate is a crucial design parameter even for adaptive gradient-based optimizers, which has been generally overlooked in the literature. Also, we found that waveform matching performance metrics must be used with caution as they in certain situations can fail completely. Finally, we show that a loss function based on scale-invariant signal-to-distortion ratio (SI-SDR) achieves good general performance across a range of popular speech enhancement evaluation metrics, which suggests that SI-SDR is a good candidate as a general-purpose loss function for speech enhancement systems.

Original languageEnglish
Article number8966946
JournalIEEE/ACM Transactions on Audio, Speech, and Language Processing
Volume28
Pages (from-to)825-838
Number of pages14
ISSN2329-9290
DOIs
Publication statusPublished - 23 Jan 2020

Keywords

  • Speech enhancement
  • fully convolutional neural networks
  • objective intelligibility
  • time-domain

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