A typical neural speech enhancement (SE) approach mainly handles speech and noise
mixtures, which is not optimal for singing voice enhancement scenarios where singing is …

In this work, we address the novel and rarely considered source separation task of
decomposing piano concerto recordings into separate piano and orchestral tracks. Being a …

In this work, we propose Exformer, a time-domain architecture for target speaker extraction. It
consists of a pre-trained speaker embedder network and a separator network based on …

The integration of multimodality in natural language processing (NLP) tasks seeks to exploit
the complementary information contained in two or more modalities, such as text, audio and …

Learning effective vocal representations from a waveform mixture is a crucial but
challenging task for deep neural network (DNN)-based singing voice separation (SVS) …

Cutting-edge AI-driven tools are currently employed for replicating human voices, leading to
the emergence of audio deepfakes. Initially designed to enhance experiences like audio …

The role of air traffic controllers is to direct and manage highly dynamic flights. Their work
requires both efficiency and accuracy. Previous studies have shown that fatigue in air traffic …

While piano music transcription models have shown high performance for solo piano
recordings, their performance de-grades when applied to ensemble recordings. This study …

Singing-voice separation is a separation task that involves a singing voice and musical
accompaniment. In this paper, we propose a novel, unsupervised methodology for extracting …

Singing voice separation (SVS) and speaker identification (SI) are two classic problems in
speech signal processing. Deep neural networks (DNNs) solve these two problems by …