Improving music source separation based on deep neural networks through data augmentation and network blending

Uhlich, Stefan; Porcu, Marcello; Giron, Franck; Enenkl, Michael; Kemp, Thomas; Takahashi, Naoya; Mitsufuji, Yuki

doi:10.1109/icassp.2017.7952158

Cited by 170 publications

(150 citation statements)

References 25 publications

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“…Neural network based regression methods have been used to solve music separation and speech separation in [6,7,8,10,11]. Regression based source separation methods learn a mapping from a mixture of sources to a target source to be separated.…”

Section: Regression Based Source Separationmentioning

confidence: 99%

Source Separation with Weakly Labelled Data: an Approach to Computational Auditory Scene Analysis

Kong

Wang

Song

et al. 2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Source separation is the task to separate an audio recording into individual sound sources. Source separation is fundamental for computational auditory scene analysis. Previous work on source separation has focused on separating particular sound classes such as speech and music. Many of previous work require mixture and clean source pairs for training. In this work, we propose a source separation framework trained with weakly labelled data. Weakly labelled data only contains the tags of an audio clip, without the occurrence time of sound events. We first train a sound event detection system with Au-dioSet. The trained sound event detection system is used to detect segments that are mostly like to contain a target sound event. Then a regression is learnt from a mixture of two randomly selected segments to a target segment conditioned on the audio tagging prediction of the target segment. Our proposed system can separate 527 kinds of sound classes from AudioSet within a single system. A U-Net is adopted for the separation system and achieves an average SDR of 5.67 dB over 527 sound classes in AudioSet.Index Terms-Source separation, weakly labelled data, computational auditory scene analysis, AudioSet.

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Section: Regression Based Source Separationmentioning

confidence: 99%

Source Separation with Weakly Labelled Data: an Approach to Computational Auditory Scene Analysis

Kong

Wang

Song

et al. 2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…This technique has been popular for some time among the machine learning community. It has also been shown beneficial for MIR tasks such as singing voice detection and source separation [20][21][22] (but not yet for SID).…”

Section: Data Augmentation: Separate Shuffle and Remixmentioning

confidence: 99%

Addressing The Confounds Of Accompaniments In Singer Identification

Hsieh

Cheng

Fan

et al. 2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Identifying singers is an important task with many applications. However, the task remains challenging due to many issues. One major issue is related to the confounding factors from the background instrumental music that is mixed with the vocals in music production. A singer identification model may learn to extract non-vocal related features from the instrumental part of the songs, if a singer only sings in certain musical contexts (e.g., genres). The model cannot therefore generalize well when the singer sings in unseen contexts. In this paper, we attempt to address this issue. Specifically, we employ open-unmix, an open source tool with state-of-the-art performance in source separation, to separate the vocal and instrumental tracks of music. We then investigate two means to train a singer identification model: by learning from the separated vocal only, or from an augmented set of data where we "shuffle-and-remix" the separated vocal tracks and instrumental tracks of different songs to artificially make the singers sing in different contexts. We also incorporate melodic features learned from the vocal melody contour for better performance. Evaluation results on a benchmark dataset called the artist20 shows that this data augmentation method greatly improves the accuracy of singer identification.

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“…When training on a small dataset like MUSDB, data augmentation is regularly cited as a way to improve separation performances [8]. In this experiment, we try to figure out to what extent data augmentation can improve separation performances.…”

Section: Data Augmentationmentioning

confidence: 99%

Singing Voice Separation: A Study on Training Data

Pretet

Hennequin²,

Royo-Letelier³

et al. 2019

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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In the recent years, singing voice separation systems showed increased performance due to the use of supervised training. The design of training datasets is known as a crucial factor in the performance of such systems. We investigate on how the characteristics of the training dataset impacts the separation performances of state-ofthe-art singing voice separation algorithms. We show that the separation quality and diversity are two important and complementary assets of a good training dataset. We also provide insights on possible transforms to perform data augmentation for this task.

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Improving music source separation based on deep neural networks through data augmentation and network blending

Cited by 170 publications

References 25 publications

Source Separation with Weakly Labelled Data: an Approach to Computational Auditory Scene Analysis

Source Separation with Weakly Labelled Data: an Approach to Computational Auditory Scene Analysis

Addressing The Confounds Of Accompaniments In Singer Identification

Singing Voice Separation: A Study on Training Data

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