Abstract
We use a simple network which uses negative feedback of activation and simple Hebbian learning to self-organise in such a way as to produce a feature map which has the property of identifying the relative proportions of the components of the input data. Thus it evaluates the angular properties of the input data space and ignores the magnitude of the input data. When used on unprocessed voice data, the network is shown to extract both the phase and the frequency information from the raw data. We show how to use this network for classification of vowels.
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© 1998 Springer-Verlag Wien
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McGlinchey, S., Fyfe, C. (1998). Automatic Extraction of Phase and Frequency Information from Raw Voice Data. In: Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-6492-1_22
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DOI: https://doi.org/10.1007/978-3-7091-6492-1_22
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-83087-1
Online ISBN: 978-3-7091-6492-1
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