Noise reduction

Pure mathematics

Voice activity detection

Block (permutation group theory)

Erasure

Dual (grammatical number)

Computer network

Mathematics

Noise (video)

Operating system

Algorithm

Image (mathematics)

Literature

Speech coding

Encoder

Artificial intelligence

Programming language

Geometry

Speech enhancement

Path (computing)

Computer science

Speech recognition

Field (mathematics)

Decoding methods

Speech processing

Convolutional neural network

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 MAN-NER achieves state-of-the-art performance while efficiently processing noisy speech.

MANNER: Multi-View Attention Network For Noise Erasure