This paper proposes the application of Fisher linear discriminants to the problem of speech/music classification. Fisher linear discriminants can classify between two different classes, and are based on the calculation of some kind of centroid for the training data corresponding with each one of these classes. Based on that information a linear boundary is established, which will be used for the classification process. Some results will be given demonstrating the superior behavior of this classification algorithm compared with the well-known K-nearest neighbor algorithm. It will also be demonstrated that it is possible to obtain very good results in terms of probability of error using only one feature extracted from the audio signal, being thus possible to reduce the complexity of this kind of systems in order to implement them in real-time.
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