Specific Selection of FFT Amplitudes from Audio Sports and News Broadcasting for Classification Purposes - Poulos, Marios; Bokos, George; Kanellopoulos, Nikolaos; Papavlasopoulos, Sozon; Avlonitis, Markos (Journal of Graph Algorithms and Applications, 2007)
In this paper we investigate the problem of classification betweensports and news broadcasting. We detect and classify files thatconsist of speech and music or background noise (newsbroadcasting), and speech and a noisy background (sportsbroadcasting). More specifically, this study investigates featureextraction and training and classification procedures. We comparethe Average Magnitude Difference Function (AMDF) method, which weconsider more robust to background noise, with a novel proposedmethod. This method uses several spectral audio features which maybe considered as specific semantic information. We base theextraction of these features on the theory of computationalgeometry using an Onion Algorithm (OA). We tested theclassification procedure as well as the learning ability of thetwo methods using a Learning Vector Quantizer One (LVQ1) neuralnetwork. The results of the experiment showed that the OA methodhas a faster learning procedure, which we characterise as anaccurate feature extraction method for several audio cases. <!---->