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

TL;DR

This paper introduces a new, simple feature selection algorithm for classifying musical content with high accuracy, using minimal features based on the structure of audio signals, and validates its effectiveness through extensive experiments.

Contribution

It proposes a novel feature extraction and selection scheme that simplifies musical classification by focusing on key structural segments and reduces computational complexity.

Findings

Efficient classification with fewer features.

Reduced overfitting due to smaller feature set.

Validated through extensive cross-validation experiments.

Abstract

This work proposes a novel feature selection algorithm to classify Songs into different groups. Classification of musical content is often a non-trivial job and still relatively less explored area. The main idea conveyed in this article is to come up with a new feature selection scheme that does the classification job elegantly and with high accuracy but with simpler but wisely chosen small number of features thus being less prone to over-fitting. This uses a very basic general idea about the structure of the audio signal which is generally in the shape of a trapezium. So, using this general idea of the Musical Community we propose three frames to be considered and analyzed for feature extraction for each of the audio signal -- opening, stanzas and closing -- and it has been established with the help of a lot of experiments that this scheme leads to much efficient classification with less complex features in a low dimensional feature space thus is also a computationally less expensive method. Step by step analysis of feature extraction, feature ranking, dimensionality reduction using PCA has been carried in this article. Sequential Forward selection (SFS) algorithm is used to explore the most significant features both with the raw Fisher Discriminant Ratio (FDR) and also with the significant eigen-values after PCA. Also during classification extensive validation and cross validation has been done in a monte-carlo manner to ensure validity of the claims.