We address the problem of" cocktail-party" source separation in a deep learning framework
called deep clustering. Previous deep network approaches to separation have shown …

The thud of a bouncing ball, the onset of speech as lips open--when visual and audio events
occur together, it suggests that there might be a common, underlying event that produced …

Automatic Speech Recognition (ASR), which is aimed to enable natural human–machine
interaction, has been an intensive research area for decades. Many core technologies, such …

In this paper, we propose the utterance-level permutation invariant training (uPIT) technique.
uPIT is a practically applicable, end-to-end, deep-learning-based solution for speaker …

We propose a novel deep learning training criterion, named permutation invariant training
(PIT), for speaker independent multi-talker speech separation, commonly known as the …

Humans possess a remarkable ability to attend to a single speaker's voice in a multi-talker
background,,. How the auditory system manages to extract intelligible speech under such …

The cocktail party problem, ie, tracing and recognizing the speech of a specific speaker
when multiple speakers talk simultaneously, is one of the critical problems yet to be solved …

Deep clustering is a recently introduced deep learning architecture that uses discriminatively
trained embeddings as the basis for clustering. It was recently applied to spectrogram …

In this paper, we summarize recent progresses made in deep learning based acoustic
models and the motivation and insights behind the surveyed techniques. We first discuss …

The perceptual analysis of acoustic scenes involves binding together sounds from the same
source and separating them from other sounds in the environment. In large social groups …