Speeh/music classification by using statistical neural networks

• Published in: Proceedings of the IEEE 12th Signal Processing and Communications Applications Conference, 2004 pp. 227 - 229
• Main Authors: Bolat, B., Kucuk, O.
• Format: Conference Proceeding
• Language: English; Turkish
• Published: IEEE 2004
• Subjects: Cepstral analysis; Discrete Fourier transforms; Gaussian processes; Multiple signal classification; Neural networks; Performance evaluation; Root mean square; Testing
• ISBN: 0780383184; 9780780383180
• DOI: 10.1109/SIU.2004.1338300

This paper represents a framework for speech/music classification by using statistical neural networks. Zero crossing rate, root mean square power and spectral centroid were used as features. A dataset including 150 audio instances was labeled manually and 105 of them were used to train different networks, which are the probabilistic neural network (PNN), the generalised regression neural network (GRNN) and the radial basis functions (RBF). The remainder of the dataset was used as test item. Training and test performance of these three network types were discussed.