# Machine Learning with Evolutionary Parameter Tuning for Singing Registers Classification
## 1 Introduction
The experimental results showed that it was possible to reach an Unweighted Average Recall (UAR) between 40% and 47%, relying on the deep embedding with the SVM algorithm and the Residual neural network (ResNet) architecture as the feature extractor. [PERSON] et al. [11] collected a database consisting of 2004 audio files from professional opera singers to provide singers early guidance improved by ML models, thereby averting probable phonomicrosurgery and/or premature end to their  
Figure 3: Illustration of the muscles involved in voice production and control: (**a**) superior view of the vocal folds, including the cartilaginous framework and laryngeal muscles; (**b**) medial view of the croquernotoid joint, which is formed between the artyenoid and crocid cartilages; (**c**) posterolateral view of the cricothyroid joint, formed by the thyroid and crocid cartilages. The arrows in (**b**,**c**) show the possible directions of movement of the artyenoid and crocid cartilages due to the activation of the LCA and CT muscles, respectively. Reprinted with permission from [PERSON] [3]. Copyright 2016, Acoustical Society of America. professional careers. To achieve the latter goal, the Authors employed the Random Forest (RF) model to construct an efficient Fach classifier (the German Fach system categorizes singers, especially opera performers, based on their vocal range, weight, and timbre. While it is employed globally, it is particularly prevalent in the context of Europe, in particular within German-speaking regions and repertory opera houses [16]). Thus, it was possible to achieve an accuracy of almost 80% in most of the examined voice types. [PERSON] et al. [12] used the SVM model, tuned with the radial basis function (RBF) kernel, to classify Chinese popular music based on six timbre qualities of the singing voice. The authors could achieve an accuracy of 79.84% in the timbre classification task, considering the usage of five-fold stratified cross-validation as a performance assessment strategy. Although research exploring ML techniques to detect specific nuances in singing voices is found in the literature, this approach remains relatively under-researched. In addition, the reported classification performance and computational efficiency of the previously reported research suggest potential improvement. Complementarily, to the best of our knowledge, existing studies have not evaluated the efficacy of hybrid models optimized by metaheuristics for the considered task, as has been done in investigations with similar contexts [17]. These gaps in the available literature motivate our investigation into the utilization of hybrid models optimized by metaheuristics for singing-voice analysis. The performance of ML models depends heavily on the choice of hyperparameters, once it influences the model's ability to learn patterns in the data [18; 19; 20]. In addition, tuning a model for optimal performance is a challenging and time-intensive process, especially when many hyperparameters need adjustment, as this necessitates exploring a vast number of possible combinations [21]. Furthermore, manually adjusting these hyperparameters or through exhaustive search can be inefficient and not guarantee the best configuration. Thus, bio-inspired optimization algorithms, such as the Differential Evolution (DE), have stood out for handling this task well in different scenarios. Thus, such an approach can offer superior performance in identifying subtle characteristics within singing voices compared to existing methods [20]. Furthermore, this work also sought to optimize the computational efficiency of the proposed model to ensure its practical applicability in real-world scenarios. Thus, we chose to use low-level audio descriptors and, consequently, low computational cost, combined with traditional machine-learning models since Deep Learning (DL) models can have a significantly higher computational cost [22].