  In the field of speech enhancement, time domain methods have difficulties in
achieving both high performance and efficiency. Recently, dual-path models have
been adopted to represent long sequential features, but they still have limited
representations and poor memory efficiency. In this study, we propose
Multi-view Attention Network for Noise ERasure (MANNER) consisting of a
convolutional encoder-decoder with a multi-view attention block, applied to the
time-domain signals. MANNER efficiently extracts three different
representations from noisy speech and estimates high-quality clean speech. We
evaluated MANNER on the VoiceBank-DEMAND dataset in terms of five objective
speech quality metrics. Experimental results show that MANNER achieves
state-of-the-art performance while efficiently processing noisy speech.
