Audio content description with wavelets and neural nets

Precision audio content description is one of the key components of next generation Internet multimedia search machines. We examine the usability of a combination of 39 different wavelets and three different types of neural nets for precision audio content description. More specifically, we develop a novel wavelet dispersion measure that measures obtained ranks of wavelet coefficients. Our dispersion measure in conjunction with a probabilistic radial basis neural network trained by only three independent example sets obtains a success rate of approximately 78% in identifying unknown complex classical music movements.