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Erschienen in:
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2018 | OriginalPaper | Buchkapitel

Emotion Based Categorization of Music Using Low Level Features and Agglomerative Clustering

verfasst von : Rajib Sarkar, Saikat Dutta, Aneek Roy, Sanjoy Kumar Saha

Erschienen in: Computer Vision, Pattern Recognition, Image Processing, and Graphics

Verlag: Springer Singapore

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Abstract

Music emotion recognition (MER) has become an eminent field of interest in music information retrieval (MIR) group with the objective to provide more flexibility in content based music retrieval. It is quite important to categorize the music according to the emotional characteristics as it enables the users to retrieve the music according to their cognitive state. In this work, we have considered low level time-domain and spectral features extracted from the music signal. Instead of considering a wide range of features, they are judiciously considered based on our perception about the particular emotion. For classification, unsupervised approach based on K-means and Agglomerative clustering are considered. Experiment is carried out on a benchmark dataset. Performance comparison with existing work reflects the superiority of our proposed work.

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Vorheriges Kapitel Automated Translation of Human Postures from Kinect Data to Labanotation
Nächstes Kapitel Transfer Learning by Finetuning Pretrained CNNs Entirely with Synthetic Images
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Metadaten
Titel
Emotion Based Categorization of Music Using Low Level Features and Agglomerative Clustering
verfasst von
Rajib Sarkar
Saikat Dutta
Aneek Roy
Sanjoy Kumar Saha
Copyright-Jahr
2018
Verlag
Springer Singapore
Buch
Computer Vision, Pattern Recognition, Image Processing, and Graphics

Print ISBN: 978-981-13-0019-6

Electronic ISBN: 978-981-13-0020-2

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-981-13-0020-2_44