On the Importance of Audio-Source Separation for Singer Identification in Polyphonic Music

Sharma, Bidisha; Das, Rohan Kumar; Li, Haizhou

doi:10.21437/interspeech.2019-1925

“…While the idea of using SS to improve SID has been attempted before [11,[14][15][16], our work differs from the prior arts in two ways. First, except for the concurrent work [16], the prior arts that we are aware of did not use deep learningbased SS models. In contrast, in our work both the SS model and the SID model employ deep learning.…”

Section: Conv Blockmentioning

confidence: 99%

“…Second, unlike prior arts (including [16]), we investigate one additional way to employ SS to improve SID. Given the separated vocal tracks and instrumental tracks of the audio recordings in the training set, we perform the so-called "data augmentation" [19][20][21][22] by randomly shuffling the separated tracks of different songs and then remixing them.…”

Section: Conv Blockmentioning

confidence: 99%

Addressing The Confounds Of Accompaniments In Singer Identification

Hsieh

¹

,

Cheng

²

,

Fan

³

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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“…Research focuses of existing SID approaches can be grouped into three categories: i) simply considering as an issue of the speaker identification (SPID) [3], ii) directly identifying singers ignoring the influence of background music for the singer voice [4,5], and iii) distinguishing singers by voice characteristics after removing the interference of background music [6]. Hamid et al [3] used i-vector that is first introduced in SPID task, which aims to extract songlevel descriptors built from frame-level timbre features.…”

Section: Introductionmentioning

confidence: 99%

“…Some new methods are proposed using singing voice separation [8,9] as pre-processing. Sharma et al [6] extract the singing vocals from polyphonic songs using Wave-U-Net based approach to overcome the interference of background accompaniment, which outperforms the baseline without audio source separation by a large margin. Our method belongs to the second category.…”

Section: Introductionmentioning

confidence: 99%

Singer Identification Using Deep Timbre Feature Learning with KNN-NET

Zhang

¹

,

Qian

²

,

Yu

³

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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In this paper, we study the issue of automatic singer identification (SID) in popular music recordings, which aims to recognize who sang a given piece of song. The main challenge for this investigation lies in the fact that a singer's singing voice changes and intertwines with the signal of background accompaniment in time domain. To handle this challenge, we propose the KNN-Net for SID, which is a deep neural network model with the goal of learning local timbre feature representation from the mixture of singer voice and background music. Unlike other deep neural networks using the softmax layer as the output layer, we instead utilize the KNN as a more interpretable layer to output target singer labels. Moreover, attention mechanism is first introduced to highlight crucial timbre features for SID. Experiments on the existing artist20 dataset show that the proposed approach outperforms the state-of-the-art method by 4%. We also create singer32 and singer60 datasets consisting of Chinese pop music to evaluate the reliability of the proposed method. The more extensive experiments additionally indicate that our proposed model achieves a significant performance improvement compared to the state-of-the-art methods.

show abstract

“…Research focuses of existing SID approaches can be grouped into three categories: i) simply considering as an issue of the speaker identification (SPID) [3], ii) directly identifying singers ignoring the influence of background music for the singer voice [4,5], and iii) distinguishing singers by voice characteristics after removing the interference of background music [6]. Hamid et al [3] used i-vector that is first introduced in SPID task, which aims to extract song-level descriptors built from frame-level timbre features.…”

Section: Introductionmentioning

confidence: 99%

Singer Identification Using Deep Timbre Feature Learning with KNN-Net

Zhang

¹

,

Qian

²

,

Yu

³

et al. 2021

Preprint

0

View full text Add to dashboard Cite

In this paper, we study the issue of automatic singer identification (SID) in popular music recordings, which aims to recognize who sang a given piece of song. The main challenge for this investigation lies in the fact that a singer's singing voice changes and intertwines with the signal of background accompaniment in time domain. To handle this challenge, we propose the KNN-Net for SID, which is a deep neural network model with the goal of learning local timbre feature representation from the mixture of singer voice and background music. Unlike other deep neural networks using the softmax layer as the output layer, we instead utilize the KNN as a more interpretable layer to output target singer labels. Moreover, attention mechanism is first introduced to highlight crucial timbre features for SID. Experiments on the existing artist20 dataset show that the proposed approach outperforms the state-of-the-art method by 4%. We also create singer32 and singer60 datasets consisting of Chinese pop music to evaluate the reliability of the proposed method. The more extensive experiments additionally indicate that our proposed model achieves a significant performance improvement compared to the state-of-the-art methods.

show abstract

On the Importance of Audio-Source Separation for Singer Identification in Polyphonic Music

Cited by 18 publications

References 25 publications

Addressing The Confounds Of Accompaniments In Singer Identification

Addressing The Confounds Of Accompaniments In Singer Identification

Singer Identification Using Deep Timbre Feature Learning with KNN-NET

Singer Identification Using Deep Timbre Feature Learning with KNN-Net

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