Please use this identifier to cite or link to this item: https://doi.org/10.1109/IJCNN.2010.5596470
Title: Classification of musical styles using liquid state machines
Authors: Ju, H.
Xu, J.-X. 
Vandongen, A.M.J. 
Issue Date: 2010
Source: Ju, H.,Xu, J.-X.,Vandongen, A.M.J. (2010). Classification of musical styles using liquid state machines. Proceedings of the International Joint Conference on Neural Networks : -. ScholarBank@NUS Repository. https://doi.org/10.1109/IJCNN.2010.5596470
Abstract: Music Information Retrieval (MIR) is an interdisciplinary field that facilitates indexing and content-based organization of music databases. Music classification and clustering is one of the major topics in MIR. Music can be defined as 'organized sound'. The highly ordered temporal structure of music suggests it should be amendable to analysis by a novel spiking neural network paradigm: the liquid state machine (LSM). Unlike conventional statistical approaches that require the presence of static input data, the LSM has a unique ability to classify music in real-time, due to its dynamics and fading-memory. This paper investigates the performance of an LSM in classifying musical styles (ragtime vs. classical), as well as its ability to distinguish music from note sequences without temporal structure. The results show that the LSM performs admirably in this task. © 2010 IEEE.
Source Title: Proceedings of the International Joint Conference on Neural Networks
URI: http://scholarbank.nus.edu.sg/handle/10635/51123
ISBN: 9781424469178
DOI: 10.1109/IJCNN.2010.5596470
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