# Machine Learning with Evolutionary Parameter Tuning for Singing Registers Classification
## 1 Introduction
Regarding the signal-based representation of speech, [PERSON] and [PERSON] [5] (p. 9) stated that \"the acoustic signal of speech is the physical event transmitted in telecommunications or recorded on magnetic tape, laser disc, or other mediums\". Such a signal contains both the linguistic message of the conveyed speech and all the information needed to represent the human voice properly. Within the latter context, the source-filter theory developed by [PERSON] [6] is the usual choice when analyzing speech acoustics. Indeed, according to [PERSON]'s theory [6], a simplified version of the vocal tract, i.e., the tube model, can represent the acoustics of speech. In particular, the vibrating vocal folds act as the signal source of sound, and the articulators of the vocal tracts, acting as filters, change the sound resonances. Moreover, in the context of speech, each vowel has a unique filter that modifies the source sound. Such filters act as frequency selectors, producing different resonance frequencies known as formants in speech technology literature. Finally, the same theoretical framework can be applied to analyze the singing voice [7]. Indeed, in the present article, [PERSON]'s theory  
Figure 2: The figure reports a typical tensile stress-strain curve for the vocal fold along the anterior-posterior direction, measured during loading and unloading at a frequency of 1 Hz. The slope of the tangent line (represented by dashed lines) to the stress-strain curve indicates the tangent stiffness. Due to the viscous nature of the vocal folds, the stress is generally higher during loading than unloading. This curve was derived by averaging data over 30 cycles following a 10-cycle preconditioning. Reprinted with permission from [PERSON] [3]. Copyright 2016, Acoustical Society of America. was leveraged to analyze the singing voice since singing and speaking can be explained by the same underlying acoustic theory. Vocal resonance is a fundamental acoustic phenomenon in singing, influenced both by anatomical characteristics of the vocal tract and by the technique employed by the singer. Recognizing different types of vocal registers can provide valuable information for applications in areas such as vocal training, diagnosis of vocal conditions, and musicological analysis. However, due to the complexities involved, such as individual variations between singers, identifying and classifying resonance patterns presents significant challenges. With the modernization of technology and advances in computing, it has become possible to process large amounts of data and identify complex patterns with high precision. Thus, in addition to techniques that can capture temporal dependencies in time series data, such as autoregressive models [8; 9], machine-learning methods have been gaining prominence because they make it possible to find certain patterns in signals that carry information that would be difficult to perceive beyond machines. As a result, the study of singing records has become relevant in many application contexts, for example, in vocal pedagogy [10], vocal health [11], and music technology [12]. In addition, it is also possible to use the sound of the voice to help in the medical diagnosis of certain diseases, such as COVID-19 [13], Alzheimer [14], and autism [15]. Thus, still in the context of ML algorithms leveraged to detect specific nuances in singing voices, [PERSON] et al. [10] evaluated the openSMILE feature set with Signal Feature space Support Vector Machine (SF-SVM), End-to-End Deep Learning (E2 EDL), and deep embedding with Support Vector Machine (SVM) to classify audio files according to seven paralinguistic singing characteristics commonly used in vocal pedagogy. The authors built a singing-voice quality and technique database consisting of processed audio clips downloaded from YouTube, annotated with their respective labels to achieve the latter aim.