Singer Identification from Smaller Snippets of Audio Clips Using Acoustic Features and DNNs

Abstract

Singer identification (SID) is one of the crucial tasks of music information retrieval (MIR). The presence of background accompaniment makes the task little complicated. The performance of SID with the combination of the cepstral and chromagram features has been analyzed in this work. Mel-frequency cepstral coefficients (MFCCs) and linear prediction cepstral features (LPCCs) have been computed as cepstral features and added to 12-dimensional chroma vector which is obtained from chromagram. Two different datasets have been used for experimentation, of which one is standard artist-20 and the other one is Indian singers database, which is proposed by us, with 20 Indian singers. Two different classifiers, namely random forest (RF) and deep neural networks (DNNs) are considered based on their performance in estimating the singers. The proposed approach is found to be efficient even if the input clip is of length five seconds.