Composer classification based on temporal coding in adaptive spiking neural networks

We develop a spiking neural network (SNN) based implementation of a feature based on melodic interval prevalence for composer classification of a musical composition. The network has an adaptive spike-time based weight update rule which accurately captures the classification feature. Compared to the non-neural network based baseline implementation, the SNN implementation has a performance of 95.4%. When the songs are corrupted by gaussian additive noise, the relative degradation in performance of our algorithm is lesser than what is observed in the baseline algorithm.We also demonstrate that the performance degradation of our algorithm is minimal over a wide range of perturbations in the internal parameters of our circuit, demonstrating the power of adaptive SNNs to perform complex discrimination tasks in a fault-tolerant manner.