A novel binary mask estimator based on sparse approximation

While most single-channel noise reduction algorithms fail to improve speech intelligibility, the ideal binary mask (IBM) has demonstrated substantial intelligibility improvements. However, this approach exploits oracle knowledge. The main objective of this paper is to introduce a novel binary mask estimator based on a simple sparse approximation algorithm. Our approach does not require oracle knowledge and instead uses knowledge of speech structure.

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