# Machine Learning with Evolutionary Parameter Tuning for Singing Registers Classification
## 4 Conclusions  
The presented article introduced the problem of identifying different singing registers by leveraging a hybrid learning strategy that consisted of optimizing the hyperparameters of ML models through a DE algorithm. To accomplish the latter task, a database consisting of 350 audio files from three different singing registers was built. In order to retrieve valuable information from the latter signals, 14 temporal features were extracted from each recorded signal. Finally, three ML models were evaluated using such features as input data.  
The top-performing DE-optimized XGB model achieved a high classification performance, with an average classification accuracy of approximately 97.60% and a low standard deviation of 1.2%. The latter results suggested that the extracted features effectively captured the relevant information for the singing register recognition task, thus enabling the XGB model to learn robust decision boundaries for accurate classification. In addition, an analysis of the confusion matrices for the three employed ML models revealed that most of the classification confusion occurred between the mixed and chest registers, as expected, due to the more remarkable similarity between them than with the head voice registers.  
In the latter sense, the main contribution of the present research work consisted of proposing a hybrid ML tool for singing register classification with a flexible computational framework that allows for automated ML model parameters search. The investigated method combines evolutionary search on internal parameters with ML models, providing an accurate alternative for singing register categorization tasks. The found significance lies in its ability to automatically adjust the ML model's internal parameters, effectively compensating for variations in the input data. Furthermore, the latter strategy helps optimize the user's effort in determining the optimal internal parameters for a classifier because manual tuning may be time-consuming and require particular ML understanding.  
Conceptualization: T.B. and G.d.O.C.; Methodology: L.G., T.B. and A.M.; Software: T.B. and C.M.S.; Validation: C.M.S. and M.B.; Formal Analysis: A.M., L.G., M.B. and A.C.; Investigation: G.d.O.C., A.C. and A.K.S.T.R.A.; Resources: A.M., A.K.S.T.R.A., M.B. and L.G.; Funding: M.B.; Data Curation: T.B. and G.d.O.C.; Writing--original draft: A.M., A.K.S.T.R.A., T.B., A.C. and G.d.O.C.; Writing--review and editing: L.G., M.B., C.M.S., A.C. and A.M.; Supervision: A.C. and L.G. All authors have read and agreed to the published version of the manuscript.  
The authors acknowledge the support of the funding agencies CNPq (grants 401796/2021-3, 307688/2022-4, 303982/2022-5, 409433/2022-5, and 402533/2023-2), FAPES (grant 518/2021), FAPEMIG (grants APQ-04458-23 and APQ-00032-24) and CAPES (finance code 001) for their financial support.  
The developed code and collected data presented in the study are openly available in the GitHub repository with url: [[https://github.com/LGoliatt/signals-3429481](https://github.com/LGoliatt/signals-3429481)]([https://github.com/LGoliatt/signals-3429481](https://github.com/LGoliatt/signals-3429481)) (accessed on 17 February 2025).  
The authors declare no conflicts of interest.