The field of speech processing has undergone a transformative shift with the advent of deep
learning. The use of multiple processing layers has enabled the creation of models capable …

Generative adversarial networks (GANs) have seen remarkable progress in recent years.
They are used as generative models for all kinds of data such as text, images, audio, music …

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 …

Speech enhancement is a critical component of many user-oriented audio applications, yet
current systems still suffer from distorted and unnatural outputs. While generative models …

The discrepancy between the cost function used for training a speech enhancement model
and human auditory perception usually makes the quality of enhanced speech …

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 …

For challenging acoustic scenarios as low signal-to-noise ratios, current speech
enhancement systems usually suffer from performance bottleneck in extracting the target …

Generative AI has demonstrated impressive performance in various fields, among which
speech synthesis is an interesting direction. With the diffusion model as the most popular …

Diffusion models have recently shown promising results for difficult enhancement tasks such
as the conditional and unconditional restoration of natural images and audio signals. In this …

The capability of the human to pay attention to both coarse and fine-grained regions has
been applied to computer vision tasks. Motivated by that, we propose a collaborative …