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 …

The Deep Noise Suppression (DNS) challenge is designed to foster innovation in the area
of noise suppression to achieve superior perceptual speech quality. We recently organized …

Deep complex convolution recurrent network (DCCRN), which extends CRN with complex
structure, has achieved superior performance in MOS evaluation in Interspeech 2020 deep …

Speech quality is often degraded by acoustic echoes, background noise, and reverberation.
In this paper, we propose a system consisting of deep learning and signal processing to …

Complex-valued processing has brought deep learning-based speech enhancement and
signal extraction to a new level. Typically, the process is based on a time-frequency (TF) …

In speech enhancement, complex neural network has shown promising performance due to
their effectiveness in processing complex-valued spectrum. Most of the recent speech …

Over the past few decades, computational methods have been developed to estimate
perceptual audio quality. These methods, also referred to as objective quality measures, are …

It remains a tough challenge to recover the speech signals contaminated by various noises
under real acoustic environments. To this end, we propose a novel system for denoising in …

Deep learning-based speech enhancement has seen huge improvements and recently also
expanded to full band audio (48 kHz). However, many approaches have a rather high …

This paper applies the dual-signal transformation LSTM network (DTLN) to the task of real-
time acoustic echo cancellation (AEC). The DTLN combines a short-time Fourier transform …