Medhat, Fady orcid.org/0000-0003-2827-4487, Chesmore, Edwin David orcid.org/0000-0002-0688-8376 and Robinson, John Allen orcid.org/0000-0003-0995-3513 (2018) Music Genre Classification using Masked Conditional Neural Networks. In: Neural Information Processing:24th International Conference, ICONIP 2017, Guangzhou, China, November 14-18, 2017, Proceedings, Part II. International Conference on Neural Information Processing, 14-18 Nov 2017 Lecture Notes in Computer Science . Springer , CHN
Abstract
The ConditionaL Neural Networks (CLNN) and the Masked ConditionaL Neural Networks (MCLNN) exploit the nature of multi-dimensional temporal signals. The CLNN captures the conditional temporal influence between the frames in a window and the mask in the MCLNN enforces a systematic sparseness that follows a filterbank-like pattern over the network links. The mask induces the network to learn about time-frequency representations in bands, allowing the network to sustain frequency shifts. Additionally, the mask in the MCLNN automates the exploration of a range of feature combinations, usually done through an exhaustive manual search. We have evaluated the MCLNN performance using the Ballroom and Homburg datasets of music genres. MCLNN has achieved accuracies that are competitive to state-of-the-art handcrafted attempts in addition to models based on Convolutional Neural Networks.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Keywords: | MCLNN,CLNN,RBM |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) The University of York > Faculty of Sciences (York) > Electronic Engineering (York) |
Depositing User: | Pure (York) |
Date Deposited: | 23 Feb 2022 16:00 |
Last Modified: | 03 Apr 2025 04:25 |
Published Version: | https://doi.org/10.1007/978-3-319-70096-0_49 |
Status: | Published |
Publisher: | Springer |
Series Name: | Lecture Notes in Computer Science |
Identification Number: | 10.1007/978-3-319-70096-0_49 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:184065 |