Machine Learning with Evolutionary Parameter Tuning for Singing Registers Classification
Behind human voice production, a complex biological mechanism generates and modulates sound. Recent research has explored machine-learning (ML) techniques to analyze singing-voice characteristics. However, the classification efficiency reported in such research works suggests the possibility of impr...
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MDPI AG
2025-02-01
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| Online Access: | https://www.mdpi.com/2624-6120/6/1/9 |
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| author | Tales Boratto Gabriel de Oliveira Costa Alexsandro Meireles Anna Klara Sá Teles Rocha Alves Camila M. Saporetti Matteo Bodini Alexandre Cury Leonardo Goliatt |
| author_facet | Tales Boratto Gabriel de Oliveira Costa Alexsandro Meireles Anna Klara Sá Teles Rocha Alves Camila M. Saporetti Matteo Bodini Alexandre Cury Leonardo Goliatt |
| author_sort | Tales Boratto |
| collection | DOAJ |
| description | Behind human voice production, a complex biological mechanism generates and modulates sound. Recent research has explored machine-learning (ML) techniques to analyze singing-voice characteristics. However, the classification efficiency reported in such research works suggests the possibility of improvement. In addition, there is also scope for further improvement through the application of still under-utilized optimization techniques. Thus, the present article proposes a novel approach that leverages the Differential Evolution (DE) algorithm to optimize hyperparameters within three selected ML models, with the aim of classifying singing-voice registers i.e., chest, mixed, and head registers). To develop the present study, a dataset of 350 audio files encompassing the three aforementioned registers was constructed. Then, the TSFEL Python library was employed to extract 14 pieces of temporal information from the audio signals for subsequent classification by the employed ML models. The obtained findings demonstrated that the Extreme Gradient Boosting model, optimized with DE, achieved an average classification accuracy of 97.60%, thus indicating the efficacy of the proposed approach for singing-voice register classification. |
| format | Article |
| id | doaj-art-b109e04bd1f34b3990f5a7a589ec71e9 |
| institution | DOAJ |
| issn | 2624-6120 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Signals |
| spelling | doaj-art-b109e04bd1f34b3990f5a7a589ec71e92025-08-20T02:43:07ZengMDPI AGSignals2624-61202025-02-0161910.3390/signals6010009Machine Learning with Evolutionary Parameter Tuning for Singing Registers ClassificationTales Boratto0Gabriel de Oliveira Costa1Alexsandro Meireles2Anna Klara Sá Teles Rocha Alves3Camila M. Saporetti4Matteo Bodini5Alexandre Cury6Leonardo Goliatt7Graduate Program in Computational Modeling, Federal University of Juiz de Fora, Juiz de Fora 36036-900, MG, BrazilDepartment of Mechatronics Engineering, Federal Institute of Southeast Minas Gerais, Juiz de Fora 36080-001, MG, BrazilDepartment of Languages and Literature, Federal University of Espírito Santo, Vitória 29075-910, ES, BrazilGraduate Program in Nursing, Federal University of Juiz de Fora, Juiz de Fora 36036-900, MG, BrazilDepartment of Computational Modeling, Polytechnic Institute, Rio de Janeiro State University, Nova Friburgo 22000-900, RJ, BrazilDipartimento di Economia, Management e Metodi Quantitativi, Università degli Studi di Milano, Via Conservatorio 7, 20122 Milano, ItalyDepartment of Computational and Applied Mechanics, Federal University of Juiz de Fora, Juiz de Fora 36036-900, MG, BrazilDepartment of Computational and Applied Mechanics, Federal University of Juiz de Fora, Juiz de Fora 36036-900, MG, BrazilBehind human voice production, a complex biological mechanism generates and modulates sound. Recent research has explored machine-learning (ML) techniques to analyze singing-voice characteristics. However, the classification efficiency reported in such research works suggests the possibility of improvement. In addition, there is also scope for further improvement through the application of still under-utilized optimization techniques. Thus, the present article proposes a novel approach that leverages the Differential Evolution (DE) algorithm to optimize hyperparameters within three selected ML models, with the aim of classifying singing-voice registers i.e., chest, mixed, and head registers). To develop the present study, a dataset of 350 audio files encompassing the three aforementioned registers was constructed. Then, the TSFEL Python library was employed to extract 14 pieces of temporal information from the audio signals for subsequent classification by the employed ML models. The obtained findings demonstrated that the Extreme Gradient Boosting model, optimized with DE, achieved an average classification accuracy of 97.60%, thus indicating the efficacy of the proposed approach for singing-voice register classification.https://www.mdpi.com/2624-6120/6/1/9singing registersmachine learningdifferential evolutionclassificationoptimization |
| spellingShingle | Tales Boratto Gabriel de Oliveira Costa Alexsandro Meireles Anna Klara Sá Teles Rocha Alves Camila M. Saporetti Matteo Bodini Alexandre Cury Leonardo Goliatt Machine Learning with Evolutionary Parameter Tuning for Singing Registers Classification Signals singing registers machine learning differential evolution classification optimization |
| title | Machine Learning with Evolutionary Parameter Tuning for Singing Registers Classification |
| title_full | Machine Learning with Evolutionary Parameter Tuning for Singing Registers Classification |
| title_fullStr | Machine Learning with Evolutionary Parameter Tuning for Singing Registers Classification |
| title_full_unstemmed | Machine Learning with Evolutionary Parameter Tuning for Singing Registers Classification |
| title_short | Machine Learning with Evolutionary Parameter Tuning for Singing Registers Classification |
| title_sort | machine learning with evolutionary parameter tuning for singing registers classification |
| topic | singing registers machine learning differential evolution classification optimization |
| url | https://www.mdpi.com/2624-6120/6/1/9 |
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