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This work proposes a novel feature selection algorithm to classify Songs intodifferent groups. Classification of musical content is often a non-trivial joband still relatively less explored area. The main idea conveyed in this articleis to come up with a new feature selection scheme that does the classificationjob elegantly and with high accuracy but with simpler but wisely chosen smallnumber of features thus being less prone to over-fitting. This uses a verybasic general idea about the structure of the audio signal which is generallyin the shape of a trapezium. So, using this general idea of the MusicalCommunity we propose three frames to be considered and analyzed for featureextraction for each of the audio signal -- opening, stanzas and closing -- andit has been established with the help of a lot of experiments that this schemeleads to much efficient classification with less complex features in a lowdimensional feature space thus is also a computationally less expensive method.Step by step analysis of feature extraction, feature ranking, dimensionalityreduction using PCA has been carried in this article. Sequential Forwardselection (SFS) algorithm is used to explore the most significant features bothwith the raw Fisher Discriminant Ratio (FDR) and also with the significanteigen-values after PCA. Also during classification extensive validation andcross validation has been done in a monte-carlo manner to ensure validity ofthe claims.

Detecting the Trend in Musical Taste over the Decade -- A Novel Feature Extraction Algorithm to Classify Musical Content with Simple Features