Title: Multi-Objective Learning and Mask-Based Post-Processing for Deep Neural Network Based Speech Enhancement,
Abstract: We propose a multi-objective framework to learn both secondary targets not
directly related to the intended task of speech enhancement (SE) and the
primary target of the clean log-power spectra (LPS) features to be used
directly for constructing the enhanced speech signals. In deep neural network
(DNN) based SE we introduce an auxiliary structure to learn secondary
continuous features, such as mel-frequency cepstral coefficients (MFCCs), and
categorical information, such as the ideal binary mask (IBM), and integrate it
into the original DNN architecture for joint optimization of all the
parameters. This joint estimation scheme imposes additional constraints not
available in the direct prediction of LPS, and potentially improves the
learning of the primary target. Furthermore, the learned secondary information
as a byproduct can be used for other purposes, e.g., the IBM-based
post-processing in this work. A series of experiments show that joint LPS and
MFCC learning improves the SE performance, and IBM-based post-processing
further enhances listening quality of the reconstructed speech.