A Fuzzy Approach to Drum Cymbals Classification

The many factors that influence the sound of a cymbal, combined with the external aspects that modify its sound perception, make the study of the acoustics of these instruments more challenging. Within the context of machine learning, most researches involving cymbal classification from their sounds aim to identify those instruments according to their types. However, there is a lack of studies investigating the acoustic elements of cymbals using machine learning techniques as tools. Hence, this paper proposes to classify cymbals according to their constitutive materials, since the metallic alloy assumes a significant portion is responsible for their acoustics. In addition, there is an interest in evaluating a fuzzy logic approach as a classifier applied to three sets of attributes, formed from temporal features and Mel Frequency Cepstral Coefficients extracted from audio signals, comparing triangular and Gaussian membership functions. For this, 276 audios, referring to 4 drum cymbals, were collected from a standardized procedure for capturing the sounds that considered variations in microphones and environments. As a result, the implemented model achieved 94.72% of average accuracy with a standard deviation of 2.51%, considering the Gaussian membership function and Mel Frequency Cepstral Coefficients as the audio descriptor.