Musical Source Separation: An Introduction

Cano, Estefanía; Fitzgerald, Derry; Liutkus, Antoine; Plumbley, Mark D.; Stöter, Fabian-Robert

doi:10.1109/msp.2018.2874719

Cited by 84 publications

(57 citation statements)

References 24 publications

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“…; Bellisario and Pijanowski ; Cano et al. ). A soundscape contains multiple dimensions of ecological information, and SS is expected to play a crucial part in assisting information retrieval.…”

Section: A Roadmap To Effective Retrieval Of Soundscape Informationmentioning

confidence: 99%

Source separation in ecoacoustics: a roadmap towards versatile soundscape information retrieval

Lin

Tsao

2019

Remote Sens Ecol Conserv

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A comprehensive assessment of ecosystem dynamics requires the monitoring of biological, physical and social changes. Changes that cannot be observed visually may be trackable acoustically through soundscape analysis. Soundscapes vary greatly depending on geophysical events, biodiversity and human activities. However, retrieving source-specific information from geophony, biophony and anthropophony remains a challenging task, due to interference by simultaneous sound sources. Audio source separation is a technique that aims to recover individual sound sources when only mixtures are accessible. Here, we review techniques of monoaural audio source separation with the fundamental theories and assumptions behind them. Depending on the availability of prior information about the source signals, the task can be approached as a blind source separation or a model-based source separation. Most blind source separation techniques depend on assumptions about the behaviour of the source signals, and their performance may deteriorate when the assumptions fail. Model-based techniques generally do not require specific assumptions, and the models are directly learned from labelled data. With the recent advances of deep learning, the model-based techniques can yield state-of-the-art separation performance, accordingly facilitate content-based audio information retrieval. Source separation techniques have been adopted in several ecoacoustic applications to evaluate the contributions from biodiversity and anthropogenic disturbance to soundscape dynamics. They can also be employed as nonlinear filters to improve the recognition of bioacoustic signals. To effectively retrieve ecological information from soundscapes, source separation is a crucial tool. We believe that the future integrations of ecological hypotheses and deep learning can realize a high-performance source separation for ecoacoustics, and accordingly improve soundscape-based ecosystem monitoring. Therefore, we outline a roadmap for applying source separation to assist in soundscape information retrieval and hope to promote cross-disciplinary collaboration. (biophony), their habitats (geophony) and human development (anthropophony) (Dumyahn and Pijanowski 2011; Pijanowski et al. 2011; Sueur and Farina 2015; Krause and Farina 2016). By listening to biotic sounds, the composition of soniferous animals can be characterized and applied to assess wildlife biodiversity (Andr e et al. 2011; Saito et al. 2015; Lin et al. 2017a; Ross et al. 2018). On the other hand, abiotic sounds can be used to evaluate the ecosystem dynamics associated with

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Section: A Roadmap To Effective Retrieval Of Soundscape Informationmentioning

confidence: 99%

Source separation in ecoacoustics: a roadmap towards versatile soundscape information retrieval

Lin

Tsao

2019

Remote Sens Ecol Conserv

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“…The Erkomaishvili dataset is one of few publicly available datasets on polyphonic a cappella singing (Cuesta et al, 2018;Scherbaum et al, 2019). Due to the overdubbing procedure, the audio material provides a suitable scenario for studying source separation (Cano et al, 2019), audio segmentation (Rosenzweig, 2017), and audio restoration techniques (Godsill et al, 2002).…”

Section: Applications For Mir and Musicologymentioning

confidence: 99%

Erkomaishvili Dataset: A Curated Corpus of Traditional Georgian Vocal Music for Computational Musicology

Rosenzweig¹,

Scherbaum²,

Shugliashvili³

et al. 2020

Transactions of the International Society for Music Information Retrieval

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The analysis of recorded audio material using computational methods has received increased attention in ethnomusicological research. We present a curated dataset of traditional Georgian vocal music for computational musicology. The corpus is based on historic tape recordings of three-voice Georgian songs performed by the the former master chanter Artem Erkomaishvili. In this article, we give a detailed overview of the audio material, transcriptions, and annotations contained in the dataset. Beyond its importance for ethnomusicological research, this carefully organized and annotated corpus constitutes a challenging scenario for music information retrieval tasks such as fundamental frequency estimation, onset detection, and score-to-audio alignment. The corpus is publicly available and accessible through score-following web-players.

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“…A large number of these methods are centered around "classical" signal processing methods. For a more detailed overview see (Rafii, Liutkus, Stöter, Mimilakis, & Bittner, 2017) and (Cano, FitzGerald, Liutkus, Plumbley, & Stöter, 2019). Many of these methods were hand-crafted and tuned to a small number of music recordings (Araki et al, 2012;Ono, Koldovsky, Miyabe, & Ito, 2013;Vincent et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Open-Unmix - A Reference Implementation for Music Source Separation

Stöter¹,

Uhlich²,

Liutkus³

et al. 2019

JOSS

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Music source separation is the task of decomposing music into its constitutive components, e.g., yielding separated stems for the vocals, bass, and drums. Such a separation has many applications ranging from rearranging/repurposing the stems (remixing, repanning, upmixing) to full extraction (karaoke, sample creation, audio restoration). Music separation has a long history of scientific activity as it is known to be a very challenging problem. In recent years, deep learning-based systems-for the first time-yielded high-quality separations that also lead to increased commercial interest. However, until now, no open-source implementation that achieves state-of-the-art results is available. Open-Unmix closes this gap by providing a reference implementation based on deep neural networks. It serves two main purposes. Firstly, to accelerate academic research as Open-Unmix provides implementations for the most popular deep learning frameworks, giving researchers a flexible way to reproduce results. Secondly, we provide a pre-trained model for end users and even artists to try and use source separation. Furthermore, we designed Open-Unmix to be one core component in an open ecosystem on music separation, where we already provide open datasets, software utilities, and open evaluation to foster reproducible research as the basis of future development.

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Musical Source Separation: An Introduction

Cited by 84 publications

References 24 publications

Source separation in ecoacoustics: a roadmap towards versatile soundscape information retrieval

Source separation in ecoacoustics: a roadmap towards versatile soundscape information retrieval

Erkomaishvili Dataset: A Curated Corpus of Traditional Georgian Vocal Music for Computational Musicology

Open-Unmix - A Reference Implementation for Music Source Separation

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