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A review of deep learning techniques for speech processing
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 …
learning. The use of multiple processing layers has enabled the creation of models capable …
A review of speaker diarization: Recent advances with deep learning
Speaker diarization is a task to label audio or video recordings with classes that correspond
to speaker identity, or in short, a task to identify “who spoke when”. In the early years …
to speaker identity, or in short, a task to identify “who spoke when”. In the early years …
Ego4d: Around the world in 3,000 hours of egocentric video
We introduce Ego4D, a massive-scale egocentric video dataset and benchmark suite. It
offers 3,670 hours of daily-life activity video spanning hundreds of scenarios (household …
offers 3,670 hours of daily-life activity video spanning hundreds of scenarios (household …
[HTML][HTML] A survey of sound source localization with deep learning methods
This article is a survey of deep learning methods for single and multiple sound source
localization, with a focus on sound source localization in indoor environments, where …
localization, with a focus on sound source localization in indoor environments, where …
Speaker recognition based on deep learning: An overview
Speaker recognition is a task of identifying persons from their voices. Recently, deep
learning has dramatically revolutionized speaker recognition. However, there is lack of …
learning has dramatically revolutionized speaker recognition. However, there is lack of …
CHiME-6 challenge: Tackling multispeaker speech recognition for unsegmented recordings
Following the success of the 1st, 2nd, 3rd, 4th and 5th CHiME challenges we organize the
6th CHiME Speech Separation and Recognition Challenge (CHiME-6). The new challenge …
6th CHiME Speech Separation and Recognition Challenge (CHiME-6). The new challenge …
Wavesplit: End-to-end speech separation by speaker clustering
We introduce Wavesplit, an end-to-end source separation system. From a single mixture, the
model infers a representation for each source and then estimates each source signal given …
model infers a representation for each source and then estimates each source signal given …
End-to-end neural speaker diarization with self-attention
Speaker diarization has been mainly developed based on the clustering of speaker
embeddings. However, the clustering-based approach has two major problems; ie,(i) it is not …
embeddings. However, the clustering-based approach has two major problems; ie,(i) it is not …
End-to-end neural speaker diarization with permutation-free objectives
In this paper, we propose a novel end-to-end neural-network-based speaker diarization
method. Unlike most existing methods, our proposed method does not have separate …
method. Unlike most existing methods, our proposed method does not have separate …
pyaudioanalysis: An open-source python library for audio signal analysis
Audio information plays a rather important role in the increasing digital content that is
available today, resulting in a need for methodologies that automatically analyze such …
available today, resulting in a need for methodologies that automatically analyze such …