Singer Identification by Vocal Parts Detection and Singer Classification Using LSTM Neural Networks

Abstract

Abstract: Identification of singers is considered an important research area in audio signal processing. It has acquired the scientist’s intrigues in two primary branches,1) recognizing vocal parts of polyphonic music, and 2) Classifying Singer. Here, we plan to handle the two issues, simultaneously. Techniques like GMM, SVM, and HMM have previously been utilized in classifying singers. In this work, we proposed a system for singer identification using Deep Learning and Feed Forward Neural Networks, which to the best of our knowledge have not been used for this purpose before. Preprocessing involves examining large sets of audio features to extract the most efficient set for the recognition stage. Our work is divided into several stages. To begin with, the vocal parts of all music files are identified utilizing an LSTM network which can perform well for the time series information, for example, audio signals. Then, at that point, an MLP network is integrated and contrasted with an SVM classifier in order to classify the gender of the singers. At last, one more LSTM network is involved to recognize every singer ID, and contrasted with MLP network in a similar task. In each step, various classifiers are analyzed and the outcomes are looked at, which affirm the effectiveness of our technique contrasted with the best in class.