State-of-the-art approaches for visually-guided audio source separation typically assume
sources that have characteristic sounds, such as musical instruments. These approaches …

Conv-TasNet is a recently proposed waveform-based deep neural network that achieves
state-of-the-art performance in speech source separation. Its architecture consists of a …

In many situations, we would like to hear desired sound events (SEs) while being able to
ignore interference. Target sound extraction (TSE) tackles this problem by estimating the …

We consider the problem of separating a particular sound source from a single-channel
mixture, based on only a short sample of the target source (from the same recording). Using …

While there has been much recent progress using deep learning techniques to separate
speech and music audio signals, these systems typically require large collections of isolated …

In recent years, music source separation has been one of the most intensively studied
research areas in music information retrieval. Improvements in deep learning lead to a big …

In music source separation, the number of sources may vary for each piece and some of the
sources may belong to the same family of instruments, thus sharing timbral characteristics …

Separating the signals from the mixture when the signals share same frequency and time
domain pattern is always a matter of concern. In natural environment, sound events often …

There exists an unequivocal distinction between the sound produced by a static source and
that produced by a moving one, especially when the source moves towards or away from the …

Emulating the human ability to solve the cocktail party problem, ie, focus on a source of
interest in a complex acoustic scene, is a long standing goal of audio source separation …