Singer identification for Indian singers using convolutional neural networks

• Published in: International journal of speech technology Vol. 24; no. 3; pp. 781 - 796
• Main Authors: Srinivasa Murthy, Y. V., Koolagudi, Shashidhar G., Jeshventh Raja, T. K.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.09.2021; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Artificial neural networks; Classification; Classifiers; Coefficients; Computation; Datasets; Engineering; Genetic algorithms; Identification; Information retrieval; Linear prediction; Mass media; Music; Musicians & conductors; Neural networks; Signal,Image and Speech Processing; Singers; Singing; Social Sciences; Spectrograms; Indian popular singers; Mel-frequency cepstral coefficients; Singer identification; Linear prediction cepstral coefficients (LPCCs); Chromagrams; Convolutional neural networks; Artist20 dataset; Genetic algorithm based feature selection (GAFS)

Singer identification is one of the important aspects of music information retrieval (MIR). In this work, traditional feature-based and trending convolutional neural network (CNN) based approaches are considered and compared for identifying singers. Two different datasets, namely artist20 and the In...