Is it Violin or Viola? Classifying the Instruments’ Music Pieces using Descriptive Statistics.

Classifying music pieces based on their instrument sounds is pivotal for analysis and application purposes. Given its importance, techniques using machine learning have been proposed to classify violin and viola music pieces. The violin and viola are two different instruments with three overlapping strings of the same notes, and it is challenging for ordinary people or even musicians to distinguish the sound produced by these instruments. However, the classification of musical instrument pieces was barely performed by prior research. To solve this problem, we propose a technique using descriptive statistics to reliably distinguish between violin and viola music pieces. Likewise, a similar technique on the basis of histogram is introduced alongside the main descriptive statistics approach. These approaches are derived based on the nature of the instruments’ strings and the range of their pieces. We also solve the problem in the current literature which divide the audio into segments for processing instead of managing the whole song. Thereby, we compile a dataset of recordings that comprises of violin and viola solo pieces from the Baroque, Classical, Romantic, and Modern eras. Experiment results suggest that our approach achieves high accuracy on solo pieces as compared to other methods with 0.97 accuracy on Baroque pieces. Classifying music pieces based on their instrument sounds is pivotal for analysis and application purposes. Given its importance, techniques using machine learning have been proposed to classify violin and viola music pieces. The violin and viola are two different instruments with three overlapping strings of the same notes, and it is challenging for ordinary people or even musicians to distinguish the sound produced by these instruments. However, the classification of musical instrument pieces was barely performed by prior research. To solve this problem, we propose a technique using descriptive statistics to reliably distinguish between violin and viola music pieces. Likewise, a similar technique on the basis of histogram is introduced alongside the main descriptive statistics approach. These approaches are derived based on the nature of the instruments’ strings and the range of their pieces. We also solve the problem in the current literature which divide the audio into segments for processing instead of managing the whole song. Thereby, we compile a dataset of recordings that comprises of violin and viola solo pieces from the Baroque, Classical, Romantic, and Modern eras. Experiment results suggest that our approach achieves high accuracy on solo pieces as compared to other methods with 0.97 accuracy on Baroque pieces. [ABSTRACT FROM AUTHOR]