Discriminative Learning for Monaural Speech Separation Using Deep Embedding Features

Abstract

Deep clustering (DC) and utterance-level permutation invariant training (uPIT) have been demonstrated promising for speakerindependent speech separation. DC is usually formulated as two-step processes: embedding learning and embedding clustering, which results in complex separation pipelines and a huge obstacle in directly optimizing the actual separation objectives. As for uPIT, it only minimizes the chosen permutation with the lowest mean square error, doesn't discriminate it with other permutations. In this paper, we propose a discriminative learning method for speaker-independent speech separation using deep embedding features. Firstly, a DC network is trained to extract deep embedding features, which contain each source's information and have an advantage in discriminating each target speakers. Then these features are used as the input for uPIT to directly separate the different sources. Finally, uPIT and DC are jointly trained, which directly optimizes the actual separation objectives. Moreover, in order to maximize the distance of each permutation, the discriminative learning is applied to fine tuning the whole model. Our experiments are conducted on WSJ0-2mix dataset. Experimental results show that the proposed models achieve better performances than DC and uPIT for speaker-independent speech separation.

Graph Convolution-Based Deep Clustering for Speech Separation

“…Deep clustering approaches have been proposed and showed success in separation tasks [5], [14]. Many techniques reported consisted of multiple stages separately optimized under different criteria, such as signal representation and embedding [15]. Some embedding clustering methods add phase information on multi-channel, and other research concerns the low-delay of deep clustering approaches [16], [17].…”

VoSE: An algorithm to Separate and Enhance Voices from Mixed Signals using Gradient Boosting

“…uPIT suffers from permutation problem. To overcome the issue of permutation authors proposed Deep Clustering (DC) with uPIT [45,46]. DC, Deep Attractor Network [47,] and uPIT can predict the assignments at the utterance level of all TF bins at once, without the need for frame based assignment, which is the main cause of the permutation problem.…”

“…Qualitative comparison is drawn with other models using source-to-distortion ratio (SDR) [44]. Other measurement measures include signal-to-distortion ratio improvement (SI-SDR) [45], perceptual estimate of speech efficiency (PESQ) scores [46], scale-invariant signal to-noise ratio (SI-SNR) [47]. SDR, SI-SDR, PESQ, SI-SNR higher values reflect better quality of separation.…”

L3DAS21 Challenge: Machine Learning for 3D Audio Signal Processing

“…Also U-Net-based approaches provide competitive results in this context, both for monaural [6,7] and multichannel SE tasks [8], at the expense of higher computational power demand. Other techniques to perform SE include recurrent neural networks (RNNs) [9], graph-based spectral subtraction [10], discriminative learning [11], dilated convolutions [12].…”