Algorithm

Waveform

Operating system

Telecommunications

Set (abstract data type)

Computer science

Decoding methods

Speech enhancement

Image (mathematics)

Artificial intelligence

Time domain

Laptop

Programming language

Radar

Noise reduction

Noise (video)

Encoder

Speech recognition

Computer vision

We present a causal speech enhancement model working on the raw waveform that runs in real-time on a laptop CPU. The proposed model is based on an encoder-decoder architecture with skip-connections. It is optimized on both time and frequency domains, using multiple loss functions. Empirical evidence shows that it is capable of removing various kinds of background noise including stationary and non-stationary noises, as well as room reverb. Additionally, we suggest a set of data augmentation techniques applied directly on the raw waveform which further improve model performance and its generalization abilities. We perform evaluations on several standard benchmarks, both using objective metrics and human judgements. The proposed model matches state-of-the-art performance of both causal and non causal methods while working directly on the raw waveform.

Real Time Speech Enhancement in the Waveform Domain