Emotion and Theme Recognition in Music with Frequency-Aware RF-Regularized CNNs

Khaled Koutini; Shreyan Chowdhury; Verena Haunschmid; Hamid Eghbal-Zadeh; Gerhard Widmer

Emotion and Theme Recognition in Music with Frequency-Aware RF-Regularized CNNs

Khaled Koutini, Shreyan Chowdhury, Verena Haunschmid, Hamid Eghbal-Zadeh, Gerhard Widmer

Institute of Computational Perception

Research output: Chapter in Book/Report/Conference proceeding › Conference proceedings › peer-review

Abstract

We present CP-JKU submission to MediaEval 2019; a Receptive Field-(RF)-regularized and Frequency-Aware CNN approach for tagging music with emotion/mood labels. We perform an investigation regarding the impact of the RF of the CNNs on their performance on this dataset. We observe that ResNets with smaller receptive fields -- originally adapted for acoustic scene classification -- also perform well in the emotion tagging task. We improve the performance of such architectures using techniques such as Frequency Awareness and Shake-Shake regularization, which were used in previous work on general acoustic recognition tasks.

Original language	English
Title of host publication	Proceedings of the MediaEval Benchmark Workshop 2019
Number of pages	3
Publication status	Published - 2019

Fields of science

202002 Audiovisual media
102 Computer Sciences
102001 Artificial intelligence
102003 Image processing
102015 Information systems

JKU Focus areas

Digital Transformation

Cite this

@inproceedings{d60233568ecc4e37b249f4d35f1b8554,

title = "Emotion and Theme Recognition in Music with Frequency-Aware RF-Regularized CNNs",

abstract = "We present CP-JKU submission to MediaEval 2019; a Receptive Field-(RF)-regularized and Frequency-Aware CNN approach for tagging music with emotion/mood labels. We perform an investigation regarding the impact of the RF of the CNNs on their performance on this dataset. We observe that ResNets with smaller receptive fields -- originally adapted for acoustic scene classification -- also perform well in the emotion tagging task. We improve the performance of such architectures using techniques such as Frequency Awareness and Shake-Shake regularization, which were used in previous work on general acoustic recognition tasks.",

author = "Khaled Koutini and Shreyan Chowdhury and Verena Haunschmid and Hamid Eghbal-Zadeh and Gerhard Widmer",

year = "2019",

language = "English",

booktitle = "Proceedings of the MediaEval Benchmark Workshop 2019",

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T1 - Emotion and Theme Recognition in Music with Frequency-Aware RF-Regularized CNNs

AU - Koutini, Khaled

AU - Chowdhury, Shreyan

AU - Haunschmid, Verena

AU - Eghbal-Zadeh, Hamid

AU - Widmer, Gerhard

PY - 2019

Y1 - 2019

N2 - We present CP-JKU submission to MediaEval 2019; a Receptive Field-(RF)-regularized and Frequency-Aware CNN approach for tagging music with emotion/mood labels. We perform an investigation regarding the impact of the RF of the CNNs on their performance on this dataset. We observe that ResNets with smaller receptive fields -- originally adapted for acoustic scene classification -- also perform well in the emotion tagging task. We improve the performance of such architectures using techniques such as Frequency Awareness and Shake-Shake regularization, which were used in previous work on general acoustic recognition tasks.

AB - We present CP-JKU submission to MediaEval 2019; a Receptive Field-(RF)-regularized and Frequency-Aware CNN approach for tagging music with emotion/mood labels. We perform an investigation regarding the impact of the RF of the CNNs on their performance on this dataset. We observe that ResNets with smaller receptive fields -- originally adapted for acoustic scene classification -- also perform well in the emotion tagging task. We improve the performance of such architectures using techniques such as Frequency Awareness and Shake-Shake regularization, which were used in previous work on general acoustic recognition tasks.

M3 - Conference proceedings

BT - Proceedings of the MediaEval Benchmark Workshop 2019

ER -