Detecting Generic Music Features with Single Layer Feedforward Network using Unsupervised Hebbian Computation

Abstract

In this work, the authors extract information on distinct baseline features from a popular open-source music corpus and explore new recognition techniques by applying unsupervised Hebbian learning techniques on our single-layer neural network using the same dataset. They show the detailed empirical findings to simulate how such an algorithm can help a single layer feedforward network in training for music feature learning as patterns. The unsupervised training algorithm enhances the proposed neural network to achieve an accuracy of 90.36% for successful music feature detection. For comparative analysis against similar tasks, they put their results with the likes of several previous benchmark works. They further discuss the limitations and thorough error analysis of the work. They hope to discover and gather new information about this particular classification technique and performance, also further understand future potential directions that could improve the art of computational music feature recognition.