Deep Neural Networks with Depthwise Separable Convolution for Music Genre Classification

Liang, Yunming; Zhou, Yi; Wan, Tongtang; Shu, Xiaofeng

doi:10.1109/icicsp48821.2019.8958603

Cited by 5 publications

(2 citation statements)

References 8 publications

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“…A 1D CNN extracts local features by sliding a small kernel over the data [23,31]. Furthermore, by combining multiple convolutional and pooling layers, 1D CNNs can be used to construct deeper models, thereby enhancing the learning capabilities.…”

Section: ) Fcnnd Model Structurementioning

confidence: 99%

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An Enhanced Structural Damage Identification Approach Using Statistical Analysis and Optimization

Kuo,

Lee

2023

IEEE Sensors J.

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Section: ) Fcnnd Model Structurementioning

confidence: 99%

“…Literature [30] extends MobileNetV3, exploring the performance of DW separable convolutions in mobile vision tasks. Liang et al introduced a deep neural network for music genre classification using DW separable convolutions [31] to explore the application of DW separable convolutions in music genre classification.…”

mentioning

confidence: 99%

An Enhanced Structural Damage Identification Approach Using Statistical Analysis and Optimization

Kuo,

Lee

2023

IEEE Sensors J.

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Music Genre Classification ChatBot

Jain

Sharma

Nagrath

et al. 2021

Proceedings of Second International Conference on Computing, Communications, and Cyber-Security

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A related convolutional neural network for cancer diagnosis using microRNA data classification

Jaddi,

Abdullah,

Goh

et al. 2024

Healthcare Tech Letters

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This paper develops a method for cancer classification from microRNA data using a convolutional neural network (CNN)‐based model optimized by genetic algorithm. The convolutional neural network has performed well in various recognition and perception tasks. This paper contributes to the cancer classification using a union of two CNNs. The method's performance is boosted by the relationship between CNNs and exchanging knowledge between them. Besides, communication between small sizes of CNNs reduces the need for large size CNNs and, consequently, the computational time and memory usage while preserving high accuracy. The method proposed is tested on microRNA dataset containing the genomic information of 8129 patients for 29 different types of cancer with 1046 gene expression. The classification accuracy of the selected genes obtained by the proposed approach is compared with the accuracy of 22 well‐known classifiers on a real‐world dataset. The classification accuracy of each cancer type is also ranked with the results of 77 classifiers reported in previous works. The proposed approach shows accuracy of 100% in 24 out of 29 classes and in seven cases out of 29, the method achieved 100% accuracy that no classifier in other studies has reached. Performance analysis is performed using performance metrics.

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Deep Neural Networks with Depthwise Separable Convolution for Music Genre Classification

Cited by 5 publications

References 8 publications

An Enhanced Structural Damage Identification Approach Using Statistical Analysis and Optimization

An Enhanced Structural Damage Identification Approach Using Statistical Analysis and Optimization

Music Genre Classification ChatBot

A related convolutional neural network for cancer diagnosis using microRNA data classification

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