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Emotion-Based Multimodal Music Class...

Quaranta, Eleonora,

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Emotion-Based Multimodal Music Classifier for Recommender Systems /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Emotion-Based Multimodal Music Classifier for Recommender Systems // Eleonora Quaranta.
Author:	Quaranta, Eleonora,
Description:	1 electronic resource (208 pages)
Notes:	Source: Masters Abstracts International, Volume: 86-12.
Contained By:	Masters Abstracts International86-12.
Subject:	Computer science. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=32153930
ISBN:	9798280767126

Emotion-Based Multimodal Music Classifier for Recommender Systems /
Quaranta, Eleonora,

Emotion-Based Multimodal Music Classifier for Recommender Systems /Eleonora Quaranta. - 1 electronic resource (208 pages)

Source: Masters Abstracts International, Volume: 86-12.

In recent years, advancements in artificial intelligence have driven a growing demand for personalized user experiences across various digital platforms. In the music domain, this trend is reflected in the need for more sophisticated recommendation systems beyondtraditional collaborative filtering methods. This thesis introduces an emotion-based multimodal music classifier, leveraging bothaudio features and song lyrics to capture the emotional content of music. By focusing on song content and emotional attributes, this approach aims to lay the groundwork for recommendation systems capable of providing users with a more customized and emotionally resonant experience, also addressing the cold-start problem typical of collaborative filtering-based recommendation.Following an overview of existing literature on the topic and the examination of the challenges posed by the specific field of interest, the first contribution of this work is the creation of a suitable dataset for Music Emotion Recognition: this is achieved by extending a subset of the Music4All-Onion dataset with emotion-based labels for song lyrics using an eight-class emotional model. Audio data, available in the form of pre-extracted acoustic features, is analyzed using unsupervised machine learning methods to meaningfully model the underlying structures and patterns associated with emotional music content. Different baselines methods are identified as benchmarks to comparatively evaluate the proposed approaches. The final emotion-based multimodal classifier primarily relies ontextual data in the form of song lyrics, incorporating acoustic information as an auxiliary feature to enhance the emotional classification. The model achieves promising results in the context of Music Emotion Recognition, considering the data availability issues characterizing research in this field.

English

ISBN: 9798280767126Subjects--Topical Terms:

523869
Computer science.
Subjects--Index Terms:

Multimodal classification

Emotion-Based Multimodal Music Classifier for Recommender Systems /
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520 $a In recent years, advancements in artificial intelligence have driven a growing demand for personalized user experiences across various digital platforms. In the music domain, this trend is reflected in the need for more sophisticated recommendation systems beyondtraditional collaborative filtering methods. This thesis introduces an emotion-based multimodal music classifier, leveraging bothaudio features and song lyrics to capture the emotional content of music. By focusing on song content and emotional attributes, this approach aims to lay the groundwork for recommendation systems capable of providing users with a more customized and emotionally resonant experience, also addressing the cold-start problem typical of collaborative filtering-based recommendation.Following an overview of existing literature on the topic and the examination of the challenges posed by the specific field of interest, the first contribution of this work is the creation of a suitable dataset for Music Emotion Recognition: this is achieved by extending a subset of the Music4All-Onion dataset with emotion-based labels for song lyrics using an eight-class emotional model. Audio data, available in the form of pre-extracted acoustic features, is analyzed using unsupervised machine learning methods to meaningfully model the underlying structures and patterns associated with emotional music content. Different baselines methods are identified as benchmarks to comparatively evaluate the proposed approaches. The final emotion-based multimodal classifier primarily relies ontextual data in the form of song lyrics, incorporating acoustic information as an auxiliary feature to enhance the emotional classification. The model achieves promising results in the context of Music Emotion Recognition, considering the data availability issues characterizing research in this field.
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653 $a Multimodal classification
653 $a Deep learning
653 $a Machine learning
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