Background noise

Monaural

Physics

Mathematical analysis

Anechoic chamber

Artificial intelligence

Psychology

Active listening

Acoustics

Speech enhancement

Speech recognition

Mathematics

Computer science

Convolution (computer science)

Communication

Artificial neural network

Speech processing

Quantum mechanics

Impulse (physics)

Impulse response

Telecommunications

Reverberation

IEEE/ACM Transactions on Audio Speech and Language Processing

In daily listening environments, human speech is often degraded by room reverberation, especially under highly reverberant conditions. Such degradation poses a challenge for many speech processing systems, where the performance becomes much worse than in anechoic environments. To combat the effect of reverberation, we propose a monaural (single-channel) speech dereverberation algorithm using temporal convolutional networks with self attention. Specifically, the proposed system includes a self-attention module to produce dynamic representations given input features, a temporal convolutional network to learn a nonlinear mapping from such representations to the magnitude spectrum of anechoic speech, and a one-dimensional (1-D) convolution module to smooth the enhanced magnitude among adjacent frames. Systematic evaluations demonstrate that the proposed algorithm improves objective metrics of speech quality in a wide range of reverberant conditions. In addition, it generalizes well to untrained reverberation times, room sizes, measured room impulse responses, real-world recorded noisy-reverberant speech, and different speakers.

Monaural Speech Dereverberation Using Temporal Convolutional Networks With Self Attention