Frequency-domain monaural speech enhancement has been extensively studied for over
60 years, and a great number of methods have been proposed and applied to many …

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 …

The performance of music source separation (MSS) models has been greatly improved in
recent years thanks to the development of novel neural network architectures and training …

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 …

Language-queried audio source separation (LASS) is a new paradigm for computational
auditory scene analysis (CASA). LASS aims to separate a target sound from an audio …

Recently, convolution-augmented transformer (Conformer) has achieved promising
performance in automatic speech recognition (ASR) and time-domain speech enhancement …

Real-world audio recordings are often degraded by factors such as noise, reverberation,
and equalization distortion. This paper introduces HiFi-GAN, a deep learning method to …

Convolution-augmented transformer (conformer) has recently shown competitive results in
speech-domain applications, such as automatic speech recognition, continuous speech …

Neural network applications generally benefit from larger-sized models, but for current
speech enhancement models, larger scale networks often suffer from decreased robustness …

We propose a novel application of an attention mechanism in neural speech enhancement,
by presenting a U-Net architecture with attention mechanism, which processes the raw …