Deep learning is becoming an increasingly important tool for image reconstruction in
fluorescence microscopy. We review state-of-the-art applications such as image restoration …

Deep Learning (DL) is a sub field of Machine Learning (ML) that has considerable
prospective in many areas of study like computer vision, image, and audio processing. A …

Phase is important for perceptual quality of speech. However, it seems intractable to directly
estimate phase spectra through supervised learning due to their lack of spectrotemporal …

Existing objective evaluation metrics for voice conversion (VC) are not always correlated
with human perception. Therefore, training VC models with such criteria may not effectively …

This paper investigates several aspects of training a RNN (recurrent neural network) that
impact the objective and subjective quality of enhanced speech for real-time single-channel …

Deep neural networks have become an indispensable technique for audio source
separation (SS). It was recently reported that a variant of CNN architecture called MM …

Convolutional recurrent networks (CRN) integrating a convolutional encoder-decoder (CED)
structure and a recurrent structure have achieved promising performance for monaural …

The dual-path RNN (DPRNN) was proposed to more effectively model extremely long
sequences for speech separation in the time domain. By splitting long sequences to smaller …

We present RemixIT, a simple yet effective self-supervised method for training speech
enhancement without the need of a single isolated in-domain speech nor a noise waveform …

Speech enhancement (SE) aims to improve speech quality and intelligibility, which are both
related to a smooth transition in speech segments that may carry linguistic information, eg …