Detection of largest possible repeated patterns in Indian audio songs using spectral features
In the field of Content Based Music Information Retrieval (CB MIR) researchers are always looking for better ways to classify songs aside from the existing classifiers such as genre, mood, scale, tempo, etc. By determining a way to isolate and extract maximum length repeating patterns (MLRPs) in a music file, we can analyze them in order to describe another potential classifier: complexity. Extraction of repeating patterns would also allow users to easily extract ringtones from their favorite songs. In this paper, an effort has been made to describe a method to extract repeating patterns from a given music file through direct signal level as well as feature level comparison. These extracted patterns can be used as ringtones, or for analysis to determine complexity. Features such as mel frequency cepstral coefficients (MFCCs) modulation spectral features (MSFs) and jitter are computed to reduce the computational time observed in signal level comparison.
2016 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)