Musical Genre Classification on the Marsyas Audio Data Using Convolution NN
Md. Sabbir Ahmed, Md Zalish Mahmud, Shamim Akhter · 2020
Music is an important part of our life as it expresses feelings including joy, happiness, and pain of life. It relieves mental stress by its rhythmic tunes and also becomes a source of entertainment. The modern song involves different instruments and thus combines different genres as well. Music genre classification plays an important role to provide the internal instrumental behaviors of a song specially to trace the signal style, rhythmic structure, and harmonic content of an instrument. Commonly musical genre annotations are performed manually. Besides some automatic machine learning tools including Naïve-Bayes, Decision Trees, k Nearest-Neighbors, Support Vector Machines, and Multilayer Perceptron Neural Nets are employed to classify musical genres. Convolutional is an advanced Neural Network mainly used for image processing, however, it is also performing better in classification with less parameter than multilayer perception. Till now statistical pattern recognition classifier and source separation technique are performed to classify the Marsyas data set. We are proposing a CNN framework to classify musical genre audio signals from the Marsyas data set and analyze the performance.