By the same authors

Music Genre Classification using Masked Conditional Neural Networks

Research output: Contribution to journalArticlepeer-review

Standard

Music Genre Classification using Masked Conditional Neural Networks. / Medhat, Fady; Chesmore, David; Robinson, John.

In: International Conference on Neural Information Processing, 18.02.2018.

Research output: Contribution to journalArticlepeer-review

Harvard

Medhat, F, Chesmore, D & Robinson, J 2018, 'Music Genre Classification using Masked Conditional Neural Networks', International Conference on Neural Information Processing. https://doi.org/10.1007/978-3-319-70096-0_49

APA

Medhat, F., Chesmore, D., & Robinson, J. (2018). Music Genre Classification using Masked Conditional Neural Networks. International Conference on Neural Information Processing. https://doi.org/10.1007/978-3-319-70096-0_49

Vancouver

Medhat F, Chesmore D, Robinson J. Music Genre Classification using Masked Conditional Neural Networks. International Conference on Neural Information Processing. 2018 Feb 18. https://doi.org/10.1007/978-3-319-70096-0_49

Author

Medhat, Fady ; Chesmore, David ; Robinson, John. / Music Genre Classification using Masked Conditional Neural Networks. In: International Conference on Neural Information Processing. 2018.

Bibtex - Download

@article{a12d7def42d34ee39e43f4de3ee8f592,
title = "Music Genre Classification using Masked Conditional Neural Networks",
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. ",
keywords = "MCLNN, CLNN, RBM",
author = "Fady Medhat and David Chesmore and John Robinson",
year = "2018",
month = feb,
day = "18",
doi = "10.1007/978-3-319-70096-0_49",
language = "English",
journal = "International Conference on Neural Information Processing",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - Music Genre Classification using Masked Conditional Neural Networks

AU - Medhat, Fady

AU - Chesmore, David

AU - Robinson, John

PY - 2018/2/18

Y1 - 2018/2/18

N2 - 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.

AB - 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.

KW - MCLNN

KW - CLNN

KW - RBM

U2 - 10.1007/978-3-319-70096-0_49

DO - 10.1007/978-3-319-70096-0_49

M3 - Article

JO - International Conference on Neural Information Processing

JF - International Conference on Neural Information Processing

ER -