Electrical engineering

Pattern recognition (psychology)

Noise reduction

Time domain

Operating system

Geodesy

Encoder

Engineering

Voltage

Benchmark (surveying)

Artificial intelligence

Geography

Speech enhancement

Computer science

Speech recognition

Computer vision

Artificial neural network

Transformer

In this paper, we propose a transformer-based architecture, called two-stage transformer neural network (TSTNN) for end-to-end speech denoising in the time domain. The proposed model is composed of an encoder, a two-stage transformer module (TSTM), a masking module and a decoder. The encoder maps input noisy speech into feature representation. The TSTM exploits four stacked two-stage transformer blocks to efficiently extract local and global information from the encoder output stage by stage. The masking module creates a mask which will be multiplied with the encoder output. Finally, the decoder uses the masked encoder feature to reconstruct the enhanced speech. Experimental results on the benchmark dataset show that the TSTNN outperforms most state-of-the-art models in time or frequency domain while having significantly lower model complexity.

TSTNN: Two-Stage Transformer Based Neural Network for Speech Enhancement in the Time Domain