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Emotion-Driven Music Recommender System: A Novel Deep Learning Approach for Enhanced User Experience.

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Title: Emotion-Driven Music Recommender System: A Novel Deep Learning Approach for Enhanced User Experience.
Authors: Bidlan, Ritika1 ritika_dcsa@pu.ac.in, Chawla, Sonal1
Source: International Journal of Performability Engineering. May2026, Vol. 22 Issue 5, p253-262. 10p.
Subjects: Emotion recognition, Recommender systems, Deep learning, Principal components analysis, Dimensional reduction algorithms, Human-computer interaction
Abstract: Emotion identification using audio is a significant difficulty in interactions between humans and computers, as emotional indicators within speech are usually complex and dependent on context. Conventional techniques encounter difficulties in precise classification owing to high-dimensional characteristics and restricted predictability. This paper is devoted to the recognition of emotions from audio with a novel approach based on a hybrid ResNet single-channel feature-tailored architecture. The elicitation of emotions by the suggested predication system is an indicator of robust classification error. The suggested methodology is a dimensionality reduction methodology based on Principal Component Analysis and takes advantage of ANOVA to provide detailed statistical validation to improve feature selection and general performance of the model. The model possesses a high accuracy of 94.50% in addition to high precision, recall and F1 scores, indicating that the model is well able to identify emotional states. When comparing our study with previous literature, it is evident that our model has superior performance, and it is more effective than both traditional machine learning approaches and other approaches of deep learning. It is a part of the speech emotion recognition method development that may be used in personalized music recommendation systems and other human-computer interaction technologies. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Performability Engineering is the property of Totem Publisher, Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Emotion-Driven Music Recommender System: A Novel Deep Learning Approach for Enhanced User Experience.
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Performability+Engineering%22">International Journal of Performability Engineering</searchLink>. May2026, Vol. 22 Issue 5, p253-262. 10p.
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  Data: <searchLink fieldCode="DE" term="%22Emotion+recognition%22">Emotion recognition</searchLink><br /><searchLink fieldCode="DE" term="%22Recommender+systems%22">Recommender systems</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Principal+components+analysis%22">Principal components analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Dimensional+reduction+algorithms%22">Dimensional reduction algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Human-computer+interaction%22">Human-computer interaction</searchLink>
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  Data: Emotion identification using audio is a significant difficulty in interactions between humans and computers, as emotional indicators within speech are usually complex and dependent on context. Conventional techniques encounter difficulties in precise classification owing to high-dimensional characteristics and restricted predictability. This paper is devoted to the recognition of emotions from audio with a novel approach based on a hybrid ResNet single-channel feature-tailored architecture. The elicitation of emotions by the suggested predication system is an indicator of robust classification error. The suggested methodology is a dimensionality reduction methodology based on Principal Component Analysis and takes advantage of ANOVA to provide detailed statistical validation to improve feature selection and general performance of the model. The model possesses a high accuracy of 94.50% in addition to high precision, recall and F1 scores, indicating that the model is well able to identify emotional states. When comparing our study with previous literature, it is evident that our model has superior performance, and it is more effective than both traditional machine learning approaches and other approaches of deep learning. It is a part of the speech emotion recognition method development that may be used in personalized music recommendation systems and other human-computer interaction technologies. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of International Journal of Performability Engineering is the property of Totem Publisher, Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.23940/ijpe.26.05.p3.253262
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      – Code: eng
        Text: English
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        PageCount: 10
        StartPage: 253
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      – SubjectFull: Emotion recognition
        Type: general
      – SubjectFull: Recommender systems
        Type: general
      – SubjectFull: Deep learning
        Type: general
      – SubjectFull: Principal components analysis
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      – SubjectFull: Dimensional reduction algorithms
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      – SubjectFull: Human-computer interaction
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      – TitleFull: Emotion-Driven Music Recommender System: A Novel Deep Learning Approach for Enhanced User Experience.
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              M: 05
              Text: May2026
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              Y: 2026
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            – TitleFull: International Journal of Performability Engineering
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