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Figure 1 Block diagram of the proposed speech separation system.
We have presented a new localization-based stereo speech separation system using deep networks.
The cochannel speech separation system in [9] models speaker characteristics using GMMs and ignores the temporal information of speech signals.
For properly designing and testing the speech separation system, the database is split into two different subsets, one for design and another for test.
In this paper, we propose a new stereo speech separation system where deep neural networks are used to generate soft T-F mask for separation.
In this article, a single channel speech separation system is proposed that estimates the pitch range of one or two speakers and segregates the target speech from the interference.
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Recent CASA-based speech separation systems have shown promising results in separating target speech from interference [3, Chapters 3 and 4].
This paper focuses on the practical challenge of adaptation control for speech separation systems.
(5). Figure 1 Speech mixing and ADF separation system in noise.
The speech separation module of the system uses MVDR beamformers whose adaptation is guided by the voice activity classification.
This article proposes an improved CASA system for speech separation.
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