Deep neural network based methods have been successfully applied to music source
separation. They typically learn a mapping from a mixture spectrogram to a set of source …

Sub-band models have achieved promising results due to their ability to model local
patterns in the spectrogram. Some studies further improve the performance by fusing sub …

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

In this paper, we introduce the task of language-queried audio source separation (LASS),
which aims to separate a target source from an audio mixture based on a natural language …

Personalized speech enhancement (PSE) utilizes additional cues like speaker embeddings
to remove background noise and interfering speech and extract the speech from target …

Speech restoration aims to remove distortions in speech signals. Prior methods mainly focus
on single-task speech restoration (SSR), such as speech denoising or speech declipping …

This paper summarizes the music demixing (MDX) track of the Sound Demixing Challenge
(SDX'23). We provide a summary of the challenge setup and introduce the task of robust …

Music source separation (MSS) shows active progress with deep learning models in recent
years. Many MSS models perform separations on spectrograms by estimating bounded ratio …

Automatic target sound extraction (TSE) is a machine learning approach to mimic the human
auditory perception capability of attending to a sound source of interest from a mixture of …

Few-shot audio event detection is a task that detects the occurrence time of a novel sound
class given a few examples. In this work, we propose a system based on segment-level …