The 2018 Signal Separation Evaluation Campaign
Fabian-Robert Stöter,Antoine Liutkus,Nobutaka Ito +2 more
- Iss: 10891, pp 293-305
TLDR
SiSEC 2018 as mentioned in this paper was focused on audio and pursued the effort towards scaling up and making it easier to prototype audio separation software in an era of machine-learning-based systems.Abstract:
This paper reports the organization and results for the 2018 community-based Signal Separation Evaluation Campaign (SiSEC 2018). This year’s edition was focused on audio and pursued the effort towards scaling up and making it easier to prototype audio separation software in an era of machine-learning based systems. For this purpose, we prepared a new music separation database: MUSDB18, featuring close to 10 h of audio. Additionally, open-source software was released to automatically load, process and report performance on MUSDB18. Furthermore, a new official Python version for the BSS Eval toolbox was released, along with reference implementations for three oracle separation methods: ideal binary mask, ideal ratio mask, and multichannel Wiener filter. We finally report the results obtained by the participants.read more
Citations
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SDR – Half-baked or Well Done?
TL;DR: The scale-invariant signal-to-distortion ratio (SI-SDR) as mentioned in this paper is a more robust measure for single-channel separation, which has been proposed in the BSS_eval toolkit.
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A Consolidated Perspective on Multimicrophone Speech Enhancement and Source Separation
TL;DR: This paper proposes to analyze a large number of established and recent techniques according to four transverse axes: 1) the acoustic impulse response model, 2) the spatial filter design criterion, 3) the parameter estimation algorithm, and 4) optional postfiltering.
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Detection and Classification of Acoustic Scenes and Events: Outcome of the DCASE 2016 Challenge
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Posted Content
Music Source Separation in the Waveform Domain
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Open-Unmix - A Reference Implementation for Music Source Separation
TL;DR: Open-Unmix provides implementations for the most popular deep learning frameworks, giving researchers a flexible way to reproduce results and provides a pre-trained model for end users and even artists to try and use source separation.
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