Timbre-Vibrato Model for Singer Identification

Loni, Deepali Yoginath; Subbaraman, Shaila

doi:10.1007/978-981-13-1747-7_27

“…Typically, traditional approaches [13]- [18] have trained classifiers on MFCCs or linear prediction coefficients (LPCs) to capture vocal timbre. Some methods [19]- [21] have attempted to capture singing expressions by designing handcrafted features that can capture vibrato in singing voices. In addition, recent methods [22], [23] leveraged deep learning techniques to classify singing voices from their spectrograms without relying on handcrafted features.…”

Section: A Representation Learning For Singing Voicesmentioning

confidence: 99%

Self-Supervised Contrastive Learning for Singing Voices

Yakura

¹

,

Watanabe

²

,

Goto

³

2022

IEEE/ACM Trans. Audio Speech Lang. Process.

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This study introduces self-supervised contrastive learning to acquire feature representations of singing voices. To acquire robust representations in an unsupervised manner, regular self-supervised contrastive learning trains neural networks to make the feature representation of a sample close to those of its computationally transformed versions. Similarly, we employ two transformations-pitch shifting and time stretching-considering the nature of singing voices. Nevertheless, we use them reversely: we train networks to push away representations of the transformed versions. The networks then attempt to discriminate changes in vocal timbres introduced by pitch shifting without time stretching and those in singing expressions introduced by time stretching without pitch shifting. Consequently, the acquired representations become attentive to vocal timbre and singing expression. This was confirmed through a singer identification task, where we trained a classifier to learn the relationship between the feature representations to the corresponding singer labels of 500 singers. As a result, the employed transformations helped the classifier improve the classification accuracy by 9.12% (top-1 accuracy: 63.08%) compared with the case where the feature representations fed to the classifier were acquired without the transformations (top-1 accuracy: 53.96%). Furthermore, the proposed approach can be extended to acquire feature representations attentive to either vocal timbre or singing expression but not to the other by changing how the transformations are incorporated. We particularly explored the characteristics of such vocal timbre-or singing expression-oriented feature representations against song genre, singer gender, and vocal technique, and confirmed that they successfully capture different aspects of singing voices.

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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

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.

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Timbre-Vibrato Model for Singer Identification

Cited by 3 publications

References 21 publications

Self-Supervised Contrastive Learning for Singing Voices

Self-Supervised Contrastive Learning for Singing Voices

Singer Identification Using Deep Timbre Feature Learning with KNN-NET

Singer Identification Using Deep Timbre Feature Learning with KNN-Net

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