Development of a deep learning system for hummed melody identification for BertsoBot

Abstract

The system introduced in this work tries to solve the problem of melody classification. The proposed approach is based on extracting the spectrogram of the audio of each melody and then using deep supervised learning approaches to classify them into categories. As found out experimentally, the Transfer Learning technique is required alongside Data Augmentation in order to improve the accuracy of the system. The results shown in this thesis, focus further work on this field by providing insight on the performance of different tested Learning Models. Overall, DenseNets have proved themselves the best architectures o use in this context reaching a significant prediction accuracy.