- Title
- Towards visualisation of sound-scapes through dimensionality reduction
- Creator
- Wong, Aaron S. W.; Chalup, Stephan K.
- Relation
- IEEE International Conference on Neural Networks. Proceedings of the IEEE International Joint Conference on Neural Networks (Hong Kong 1-6 June, 2008) p. 2833-2840
- Publisher Link
- http://dx.doi.org/10.1109/IJCNN.2008.4634197
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Resource Type
- conference paper
- Date
- 2008
- Description
- Sound-scapes are useful for understanding our surrounding environments in applications such as security, source tracking or understanding human computer interaction. Accurate position or localisation information from sound-scape samples consists of many channels of high dimensional acoustic data. In this paper we demonstrate how to obtain a visual representation of sound-scapes by applying dimensionality reduction techniques to a range of artificially generated sound-scape datasets. Linear and non-linear dimensionality techniques were compared including principle component analysis (PCA), multi-dimensional scaling (MDS), locally linear embedding (LLE) and isometric feature mapping (ISOMAP). Results obtained by applying the dimensionality reduction techniques led to visual representations of affine positions of the sound source on its sound-scape manifold. These displayed clearly the order relationships of angles and intensities of the generated sound-scape samples. In a simple classification task with the artificial sound data, the successful combination of dimensionality reduction and classifier methods are demonstrated.
- Subject
- sound-scapes; dimensionality reduction; linear; acoustic data
- Identifier
- http://hdl.handle.net/1959.13/45098
- Identifier
- uon:6016
- Identifier
- ISBN:1098-7576
- Rights
- Copyright © 2008 IEEE. Reprinted from the IEEE International Conference on Neural Networks. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Newcastle's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.
- Language
- eng
- Full Text
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