Audio Source Separation and Speech Enhancement

By Emmanuel Vincent, Tuomas Virtanen, Sharon Gannot. 2018

290 citations recorded by the source corpus.

Audio source separation and speech enhancement aim to extract one or more source signals of interest from an audio recording involving several sound sources. These technologies are among the most studied in audio signal processing today and bear a critical role in the success of hearing aids, hands-free phones, voice command and other noise-robust audio analysis systems, and music post-production software.

Subfields: Artificial Intelligence, Speech & Audio, Signal Processing

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Citations in the corpus

Voice Separation with an Unknown Number of Multiple Speakers (2020, methodology): “It was extensively explored over the years (Logeshwari & Mala, 2012; Martin & Cohen, 2018; Ernst et al., 2018), where traditionally signal processing techniques were applied to the task (Choi et al., 2005).”
A Review of Speaker Diarization: Recent Advances with Deep Learning (2021, methodology): “, [81, 82, 83], herein most of the recent techniques used in the DIHARD challenge series [84, 85, 86], LibriCSS meeting recognition task [87, 88], and CHiME-6 challenge track 2 [89, 90, 91] are covered.”
Wavesplit: End-to-End Speech Separation by Speaker Clustering (2020, unknown): “In particular, speech separation aims at isolating individual speaker voices from a recording with overlapping speech [8].”
VoiceHome-2, an extended corpus for multichannel speech processing in real homes (2019, unknown): “While this remains the most frequent use case today, speaker localization, speech enhancement, and ASR in distant-microphone scenarios remain challenging (Baker et al., 2009; Wölfel and McDonough, 2009; Cohen et al., 2010; Virtanen et al., 2012; Li et al., 2015; Vincent et al., 2018).”
Global and Local Simplex Representations for Multichannel Source Separation (2020, background): “Audio separation capabilities are required in various multi-microphone devices, such as: smart-phones, smart voice assistants and hearing aids, thus leading to an intense research in the ﬁeld over the last decades [2]– [4].”
LibriMix: An Open-Source Dataset for Generalizable Speech Separation (2020, background): “A fundamental problem towards robust speech processing in real-world acoustic environments is to be able to automatically extract or separate target source signals present in an input mixture recording [1].”
Improved Speech Enhancement with the Wave-U-Net (2018, background): “Audio source separation refers to the problem of extracting one or more target sources while sup14 pressing interfering sources and noise[18].”
A Survey of Sound Source Localization with Deep Learning Methods (2021, unknown): “As brieﬂy stated before, in the multi-source scenario, SSL is closely related to the source separation problem: localization can help separation and separation can help localization [22], [94].”
Machine learning in acoustics: Theory and applications. (2019, background): “2,102 Since large amounts of training data might not be available, DL is not always practical.”