Utilizing the Neuronal Behavior of Spiking Neurons to Recognize Music Signals Based on Time Coding Features

This paper presents a Spiking Neural Network(SNN) architecture to distinguish two musical instruments: piano and violin. The acoustic characteristics of music such as frequency and time convey a lot of information that help humans in distinguishing music instruments within few seconds. SNNs are neural networks that work effectively with temporal data. In this study, 2-layer SNN temporal based architecture is implemented for instrument (piano and violin) recognition. Further, this research investigates the behaviour of spiking neurons for piano and violin samples through different spike based statistics. Additionally, a Gamma metric that utilises spike time information and Root Mean Square Error (RMSE) from the membrane potential are used for classification and recognition. SNN achieved an overall classification accuracy of 92.38% and 93.19%, indicating the potential of SNNs in this inherently temporal recognition and classification domain. On the other hand, we implemented rate-coding techniques using machine learning (ML) techniques. Through this research, we demonstrated that SNN are more effective than conventional ML methods for capturing important the acoustic characteristics of music such as frequency and time. Overall, this research showed the potential capability of temporal coding over rate coding techniques while processing spatial and temporal data.