Multi-Speaker Source Separation

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Most implemented papers archive 2025-07-28

6 shown of 6 papers with code (8 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Directional Sparse Filtering using Weighted Lehmer Mean for Blind Separation of Unbalanced Speech Mixtures
  30 Jan 2021 2 repositories listed
  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.
• CNN-LSTM models for Multi-Speaker Source Separation using Bayesian Hyper Parameter Optimization
  19 Dec 2019 1 repository listed
  In this paper we propose a novel network for source separation using an encoder-decoder CNN and LSTM in parallel.
• Unsupervised Deep Clustering for Source Separation: Direct Learning from Mixtures using Spatial Information
  5 Nov 2018 1 repository listed Syntology ran 1 of 6 samples · 5 unverified
  We present a monophonic source separation system that is trained by only observing mixtures with no ground truth separation information.
• Memory Time Span in LSTMs for Multi-Speaker Source Separation
  24 Aug 2018 1 repository listed
  With deep learning approaches becoming state-of-the-art in many speech (as well as non-speech) related machine learning tasks, efforts are being taken to delve into the neural networks which are often considered as a…
• Multi-scenario deep learning for multi-speaker source separation
  24 Aug 2018 1 repository listed
  Furthermore, it is concluded that a single model, trained on different scenarios is capable of matching performance of scenario specific models.
• Deep learning for monaural speech separation
  4 May 2014 1 repository listed
  In this paper, we study deep learning for monaural speech separation.

Syntology lines on 1 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.