Transfer Learning for Music Classification and Regression Tasks Using Artist Tags

Wang, Lei; Zhu, Hongning; Zhang, Xulong; Li, Shengchen; Li, Wei

doi:10.1007/978-981-15-2756-2_7

Cited by 10 publications

(4 citation statements)

References 9 publications

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“…With the successful application of the deep model on many task [38]- [42], many deep model methods were tried on SVS. Kim et al [24] proposed a Korean SVS system based on a long-short term memory recurrent neural network.…”

Section: Related Work a Singing Voice Synthesismentioning

confidence: 99%

SUSing: SU-net for Singing Voice Synthesis

Zhang¹,

Wang²,

Cheng³

et al. 2022

Preprint

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Singing voice synthesis is a generative task that involves multi-dimensional control of the singing model, including lyrics, pitch, and duration, and includes the timbre of the singer and singing skills such as vibrato. In this paper, we proposed SU-net for singing voice synthesis named SUSing. Synthesizing singing voice is treated as a translation task between lyrics and music score and spectrum. The lyrics and music score information is encoded into a two-dimensional feature representation through the convolution layer. The two-dimensional feature and its frequency spectrum are mapped to the target spectrum in an autoregressive manner through a SU-net network. Within the SU-net the stripe pooling method is used to replace the alternate global pooling method to learn the vertical frequency relationship in the spectrum and the changes of frequency in the time domain. The experimental results on the public dataset Kiritan show that the proposed method can synthesize more natural singing voices.

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Section: Related Work a Singing Voice Synthesismentioning

confidence: 99%

SUSing: SU-net for Singing Voice Synthesis

Zhang¹,

Wang²,

Cheng³

et al. 2022

Preprint

Self Cite

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“…For instance, Kumar et al [34] have used this supervised representation learning from audioset to improve environmental sound classification tested on ESC-50 dataset [35]. In the musical domain, Million Song Dataset [36] is used to determine supervised representation to ameliorate other musical audio classification tasks [37], [38]. Apart from these, researchers have successfully transferred Inception-v4 [39] model, which is trained on image classification, to the acoustic domain for the classification of bird sounds [40].…”

Section: Background and Related Workmentioning

confidence: 99%

High-Fidelity Audio Generation and Representation Learning with Guided Adversarial Autoencoder

Haque¹,

Rana²,

Schuller³

2020

Preprint

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“…Eghbal-zadeh et al [13] proposed an approach to extract song-level descriptors built from frame-level timbral features. The biggest limitation of feature engineering is that it is difficult to find a feature that accurately describes different singing voices [14]. In addition to the feature representation, most research focus on the classifier.…”

Section: Introductionmentioning

confidence: 99%

MetaSID: Singer Identification with Domain Adaptation for Metaverse

Zhang¹,

Wang²,

Cheng³

et al. 2022

Preprint

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Metaverse has stretched the real world into unlimited space. There will be more live concerts in Metaverse. The task of singer identification is to identify the song belongs to which singer. However, there has been a tough problem in singer identification, which is the different live effects. The studio version is different from the live version, the data distribution of the training set and the test set are different, and the performance of the classifier decreases. This paper proposes the use of the domain adaptation method to solve the live effect in singer identification. Three methods of domain adaptation combined with Convolutional Recurrent Neural Network (CRNN) are designed, which are Maximum Mean Discrepancy (MMD), gradient reversal (Revgrad), and Contrastive Adaptation Network (CAN). MMD is a distance-based method, which adds domain loss. Revgrad is based on the idea that learned features can represent different domain samples. CAN is based on class adaptation, it takes into account the correspondence between the categories of the source domain and target domain. Experimental results on the public dataset of Artist20 show that CRNN-MMD leads to an improvement over the baseline CRNN by 0.14. The CRNN-RevGrad outperforms the baseline by 0.21. The CRNN-CAN achieved state of the art with the F1 measure value of 0.83 on album split.

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Transfer Learning for Music Classification and Regression Tasks Using Artist Tags

Cited by 10 publications

References 9 publications

SUSing: SU-net for Singing Voice Synthesis

SUSing: SU-net for Singing Voice Synthesis

High-Fidelity Audio Generation and Representation Learning with Guided Adversarial Autoencoder

MetaSID: Singer Identification with Domain Adaptation for Metaverse

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