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DOI: 10.7155/jgaa.00146
Specific Selection of FFT Amplitudes from Audio Sports and News Broadcasting for Classification Purposes
Vol. 11, no. 1, pp. 277-307, 2007. Regular paper.
Abstract In this paper we investigate the problem of classification between
sports and news broadcasting. We detect and classify files that
consist of speech and music or background noise (news
broadcasting), and speech and a noisy background (sports
broadcasting). More specifically, this study investigates feature
extraction and training and classification procedures. We compare
the Average Magnitude Difference Function (AMDF) method, which we
consider more robust to background noise, with a novel proposed
method. This method uses several spectral audio features which may
be considered as specific semantic information. We base the
extraction of these features on the theory of computational
geometry using an Onion Algorithm (OA). We tested the
classification procedure as well as the learning ability of the
two methods using a Learning Vector Quantizer One (LVQ1) neural
network. The results of the experiment showed that the OA method
has a faster learning procedure, which we characterise as an
accurate feature extraction method for several audio cases.
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