Spectral–Temporal Receptive Field-Based Descriptors and Hierarchical Cascade Deep Belief Network for Guitar Playing Technique Classification

Wang, Chien-Yao; Chang, Pao-Chi; Ding, Jian-Jiun; Tai, Tzu-Chiang; Santoso, Andri; Liu, Yuting; Wang, Jia-Ching

doi:10.1109/tcyb.2020.3014207

Cited by 8 publications

(2 citation statements)

References 24 publications

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“…Deep belief network (DBN) is one of unsupervised learning algorithms [12][13][14]. It is composed of RBM, so there is no connection in the same layer.…”

Section: Deep Belief Networkmentioning

confidence: 99%

[Retracted] Automatic Classification Method of Music Genres Based on Deep Belief Network and Sparse Representation

Pan

2022

Journal of Mathematics

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Aiming at the problems of poor classification effect, low accuracy, and long time in the current automatic classification methods of music genres, an automatic classification method of music genres based on deep belief network and sparse representation is proposed. The music signal is preprocessed by framing, pre-emphasis, and windowing, and the characteristic parameters of the music signal are extracted by Mel frequency cepstrum coefficient analysis. The restricted Boltzmann machine is trained layer by layer to obtain the connection weights between layers of the depth belief network model. According to the output classification, the connection weights in the model are fine-tuned by using the error back-propagation algorithm. Based on the deep belief network model after fine-tuning training, the structure of the music genre classification network model is designed. Combined with the classification algorithm of sparse representation, for the training samples of sparse representation music genre, the sparse solution is obtained by using the minimum norm, the sparse representation of test vector is calculated, the category of training samples is judged, and the automatic classification of music genre is realized. The experimental results show that the music genre automatic classification effect of the proposed method is better, the classification accuracy rate is higher, and the classification time can be effectively shortened.

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“…Deep belief network (DBN) is one of unsupervised learning algorithms [12][13][14]. It is composed of RBM, so there is no connection in the same layer.…”

Section: Deep Belief Networkmentioning

confidence: 99%

[Retracted] Automatic Classification Method of Music Genres Based on Deep Belief Network and Sparse Representation

Pan

2022

Journal of Mathematics

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show abstract

“…Su et al, in [22], apply sparse dictionary learning to classify guitar playing techniques (e.g., vibrato, hammer-on, pull-off). The same classification problem is solved in [23] using a deep belief network.…”

Section: Introductionmentioning

confidence: 99%

Guitar Effects Recognition and Parameter Estimation With Convolutional Neural Networks

Comunità¹,

Stowell²,

Reiss³

2021

J. Audio Eng. Soc.

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A Customizable Mathematical Model for Determining the Difficulty of Guitar Triad Chords for Machine Learning

Sharma

2023

Lecture Notes in Networks and Systems

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Spectral–Temporal Receptive Field-Based Descriptors and Hierarchical Cascade Deep Belief Network for Guitar Playing Technique Classification

Cited by 8 publications

References 24 publications

[Retracted] Automatic Classification Method of Music Genres Based on Deep Belief Network and Sparse Representation

[Retracted] Automatic Classification Method of Music Genres Based on Deep Belief Network and Sparse Representation

Guitar Effects Recognition and Parameter Estimation With Convolutional Neural Networks

A Customizable Mathematical Model for Determining the Difficulty of Guitar Triad Chords for Machine Learning

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