Audio Recognition of the Percussion Sounds Generated by a 3D Auto-Drum Machine System via Machine Learning

Abstract

A novel 3D auto-drum machine system for the generation and recording of percussion sounds is developed and presented. The capabilities of the machine, along with a calibration, sound production, and collection protocol are demonstrated. The sounds are generated by a drumstick at pre-defined positions and by known impact forces from the programmable 3D auto-drum machine. The generated percussion sounds are accompanied by the spatial excitation coordinates and the correspondent impact forces, allowing for large databases to be built, which are required by machine learning models. The recordings of the radiated sound by a microphone are analyzed using a pre-trained deep learning model, evaluating the consistency of the physical sample generation method. The results demonstrate the ability to perform regression and classification tasks when fine tuning the deep learning model with the gathered data. The produced databases can properly train machine learning models, aiding in the investigation of alternative and cost-effective materials and geometries with relevant sound characteristics and in the development of accurate vibroacoustic numerical models for studying percussion instruments sound synthesis.