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Attention mechanisms, such as local and non-local attention, play afundamental role in recent deep learning based speech enhancement (SE) systems.However, natural speech contains many fast-changing and relatively briefacoustic events, therefore, capturing the most informative speech features byindiscriminately using local and non-local attention is challenged. We observethat the noise type and speech feature vary within a sequence of speech and thelocal and non-local operations can respectively extract different features fromcorrupted speech. To leverage this, we propose Selector-Enhancer, adual-attention based convolution neural network (CNN) with a feature-filterthat can dynamically select regions from low-resolution speech features andfeed them to local or non-local attention operations. In particular, theproposed feature-filter is trained by using reinforcement learning (RL) with adeveloped difficulty-regulated reward that is related to network performance,model complexity, and "the difficulty of the SE task". The results show thatour method achieves comparable or superior performance to existing approaches.In particular, Selector-Enhancer is potentially effective for real-worlddenoising, where the number and types of noise are varies on a single noisymixture.

Selector-Enhancer: Learning Dynamic Selection of Local and Non-local Attention Operation for Speech Enhancement