Quantum mechanics

Algorithm

Phase (matter)

Philosophy

Operating system

Intelligibility (philosophy)

Economics

Computer science

Mean opinion score

Physics

Speech enhancement

Pattern recognition (psychology)

Spectrogram

Epistemology

Operations management

Artificial intelligence

Deep learning

Metric (unit)

Artificial neural network

Noise reduction

Convolutional neural network

Recurrent neural network

Encoder

Speech recognition

Convolution (computer science)

Speech enhancement has benefited from the success of deep learning in terms of intelligibility and perceptual quality. Conventional time-frequency (TF) domain methods focus on predicting TF-masks or speech spectrum, via a naive convolution neural network (CNN) or recurrent neural network (RNN). Some recent studies use complex-valued spectrogram as a training target but train in a real-valued network, predicting the magnitude and phase component or real and imaginary part, respectively. Particularly, convolution recurrent network (CRN) integrates a convolutional encoder-decoder (CED) structure and long short-term memory (LSTM), which has been proven to be helpful for complex targets. In order to train the complex target more effectively, in this paper, we design a new network structure simulating the complex-valued operation, called Deep Complex Convolution Recurrent Network (DCCRN), where both CNN and RNN structures can handle complex-valued operation. The proposed DCCRN models are very competitive over other previous networks, either on objective or subjective metric. With only 3.7M parameters, our DCCRN models submitted to the Interspeech 2020 Deep Noise Suppression (DNS) challenge ranked first for the real-time-track and second for the non-real-time track in terms of Mean Opinion Score (MOS).

DCCRN: Deep Complex Convolution Recurrent Network for Phase-Aware Speech Enhancement