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

• URL: http://arxiv.org/abs/2008.13609v1
• Date: Mon, 31 Aug 2020 13:57:31 GMT
• Title: Detecting Generic Music Features with Single Layer Feedforward Network using Unsupervised Hebbian Computation
• Authors: Sourav Das and Anup Kumar Kolya
• Abstract summary: The authors extract information on such features from a popular open-source music corpus. They apply unsupervised Hebbian learning techniques on their single-layer neural network using the same dataset. The unsupervised training algorithm enhances their proposed neural network to achieve an accuracy of 90.36% for successful music feature detection.
• Score: 3.8707695363745223
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the ever-increasing number of digital music and vast music track features through popular online music streaming software and apps, feature recognition using the neural network is being used for experimentation to produce a wide range of results across a variety of experiments recently. Through this work, the authors extract information on such features from a popular open-source music corpus and explored new recognition techniques, by applying unsupervised Hebbian learning techniques on their single-layer neural network using the same dataset. The authors 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 their proposed neural network to achieve an accuracy of 90.36% for successful music feature detection. For comparative analysis against similar tasks, authors put their results with the likes of several previous benchmark works. They further discuss the limitations and thorough error analysis of their work. The authors hope to discover and gather new information about this particular classification technique and its performance, and further understand future potential directions and prospects that could improve the art of computational music feature recognition.

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