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Found 5 papers, 3 papers with code
Practical applicability of deep neural networks for overlapping speaker separation
This paper examines the applicability in realistic scenarios of two deep learning based solutions to the overlapping speaker separation problem.
CNN-LSTM models for Multi-Speaker Source Separation using Bayesian Hyper Parameter Optimization
In this paper we propose a novel network for source separation using an encoder-decoder CNN and LSTM in parallel.
Multi-scenario deep learning for multi-speaker source separation
Furthermore, it is concluded that a single model, trained on different scenarios is capable of matching performance of scenario specific models.
Memory Time Span in LSTMs for Multi-Speaker Source Separation
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 black box.
Joint Sound Source Separation and Speaker Recognition
It is shown how state-of-the-art multichannel NMF for blind source separation can be easily extended to incorporate speaker recognition.
