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Electrical Engineering and Systems Science > Audio and Speech Processing

Title: Directional Sparse Filtering using Weighted Lehmer Mean for Blind Separation of Unbalanced Speech Mixtures

Abstract: In blind source separation of speech signals, the inherent imbalance in the source spectrum poses a challenge for methods that rely on single-source dominance for the estimation of the mixing matrix. We propose an algorithm based on the directional sparse filtering (DSF) framework that utilizes the Lehmer mean with learnable weights to adaptively account for source imbalance. Performance evaluation in multiple real acoustic environments show improvements in source separation compared to the baseline methods.
Comments: (c) 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
Subjects: Audio and Speech Processing (eess.AS); Machine Learning (cs.LG); Sound (cs.SD); Signal Processing (eess.SP)
Journal reference: Proceedings of the 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021, pp. 4485-4489
DOI: 10.1109/ICASSP39728.2021.9414336
Cite as: arXiv:2102.00196 [eess.AS]
  (or arXiv:2102.00196v3 [eess.AS] for this version)

Submission history

From: Karn Watcharasupat [view email]
[v1] Sat, 30 Jan 2021 09:36:36 GMT (46kb)
[v2] Tue, 2 Feb 2021 02:55:41 GMT (45kb)
[v3] Fri, 14 May 2021 15:34:51 GMT (46kb)

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