Instrumentality Classification Evaluation System for Natural Sounds^*

Exploring the captivating realm of natural sounds in music creation typically presents unique challenges. Although the simplicity of producing melodies with glasses filled with varying amounts of water is enchanting, replicating such versatility with other natural sounds poses difficulties. The limitations lie in the inherent properties of most natural sounds, which lack the pitch modulation freedom akin to glass instruments, resulting in homogeneity or complexity that may not be conducive to direct musical application. This study addresses these challenges by proposing an evaluation method that leverages neural networks and deep learning techniques. Our method uses a dataset of natural sounds to train a deep neural network model, extracting feature vectors that encapsulate their sonic characteristics. Subsequently, these extracted features are applied to instrumental sound datasets, enabling the modeling of natural sound dimensions within the instrumental realm. Through a classification model, the obtained features facilitate the comparison of natural and instrumental ensembles, offering insights into the suitability of natural sounds as musical instruments. By applying this methodology, our study illustrates how natural sounds such as cowbells can be evaluated in comparison with conventional instruments such as the marimba. This approach enhances our understanding of natural sound potential in music composition and performance, offering new avenues for creative exploration and innovation in sound design.