# Machine Learning with Evolutionary Parameter Tuning for Singing Registers Classification
## 3 Computational Experiments
### Model Strengths and Limitations  
Although the data collection process followed standardized procedures, there are opportunities for improvement. One key limitation lies in the consistency of recording conditions, as the audio data were obtained using a single microphone in a single environment. Future research should explore datasets incorporating more diverse recording setups to enhance robustness. Additionally, although this research employed a substantial number of samples per singing register category, the dataset's scope was constrained by the limited number of singers. While  
Figure 10: Feature importance (SHAP) scores for classification of the test datasets for each ML model: (**a**) SVC, (**b**) MLP, (**c**) XGB.  
experienced singers were deliberately selected to ensure reliable execution of vocal registers--a necessary condition once some registers may require considerable expertise--increasing the number and diversity of singers, i.e., including a broader range of vocal characteristics, age groups, and expertise levels would enrich the dataset, improve model generalizability, and enable more comprehensive evaluations of ML and DL methods.  
Expanding the analysis to include high-level audio descriptors, such as Mel-Frequency Cepstral Coefficients [42], is highly recommended. These descriptors are widely regarded in audio processing for their ability to simulate the human auditory capacity and could offer deeper insights into the acoustic properties of singing registers. Another promising direction is exploring advanced ML or DL models, particularly architectures like recurrent neural networks capable of directly processing raw audio signals. Such models could uncover latent features overlooked by traditional descriptors, thereby advancing the understanding of intricate, non-linear relationships within the data [43; 44].  
The encouraging performance of the models developed in the present work opens new avenues for practical applications. In particular, one promising direction could be represented by the development of real-time, online classification models for distinguishing between singing registers. Such models could be integrated into user-friendly tools for singing pedagogy. For instance, a smartphone application could leverage these algorithms to assist vocal coaches and students refine vocal techniques. By providing immediate feedback, the app could serve as an educational aid that promotes safer vocal practices, reducing the risk of phonatory injuries.  
Further research should focus on creating new, high-quality datasets with flexible audio signal collection using accessible devices like smartphones to democratize participation and facilitate innovation. These datasets could support the integration of ML and DL models into low-cost embedded systems, enabling the development of affordable tools for a wider audience.  
Finally, while the present study lays a strong foundation for singing register classification using computational techniques, future work should focus on enhancing feature extraction processes, diversifying datasets, and exploring state-of-the-art ML and DL models. Additionally, translating these advancements into practical, accessible tools would bridge the gap between research and real-world applications, ultimately benefiting educators, students, and practitioners in the field of vocal training.