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Asian Journal of Engineering, Sciences and Technology - Volume 7, Issue 2 2017
By Danyal Imran, Hina Wadiwala, Muhammad Atif Tahir, Muhammad Rafi
Music genre is a conventional category that identifies some piece of music as belonging to a shared tradition or set of conventions characterized by similarities in form, style or subject matter. Traditional method of genre classification tends to extract features and use them to predict labels. These features are independent of each other and do not provide meaning to music genre classification process. In order to achieve semantic meaning of features, feed-forward neural network model with stochastic gradients descent and back propagation algorithm with the categorical cross entropy loss function is investigated in this paper. The main objective is to identify complex patterns that can help in music genre classification. Experiments are performed on AMG1608 dataset and results have indicated significant performance gains when compared with existing approaches.