Improvised emotion and genre detection for songs through signal processing and genetic algorithm

Abstract

Summary Musical tunes are bundle of chords representing emotion which impart diverse of genres. Past history highlighted copious amount of research work emotion and genre classification with still increasingly rapid advancement. Music has various emotional forms as happy, sad, anger and fear. Its various genre forms are Classical, Country, Disco, Hip‐hop, Jazz, and Rock. These emotions and genre can be segregated by identifying the frequency of chords notes (swarams in Tamil music). This paper deals with identifying emotions and genre for classical music both western and south Indian classical music, viz, Carnatic music. The music was clipped and segmented to determining frequency of notes using Shortest Fast Fourier Transformation (STFT). Music features such as mel frequency, pitch beat, zero crossing rate, and spectral centroid were derived from the obtained frequency. Based on the audio features, emotion and genre were identified for the given data set with genetic algorithm as a classification technique. The MIREX‐Mood classification dataset was considered for listing out emotions. The songs from Million song data set and emotion classification repository were considered as ground truth for western classical music and group of Illayaraja Tamil film songs was considered to identify Carnatic music emotions. The classification was done using genetic algorithm. Mel frequency, pitch, and zero crossing rate were considered as individual representations to get best fit ratio and it is found to give accuracy percentage of 99.03%.

Music in Evolution and Evolution in Music

“…Suitable for exploring evolutionary scenarios in a number of domains, genetic programming has proved fruitful in music-generative tasks, allowing for the rapid replaying of the memetic processes hypothesised to have underpinned "real" music-cultural evolution. Beyond music synthesis, GAs have been used for music-analytical purposes (Rafael et al, 2009;Geetha Ramani & Priya, 2019); and for emotion-, genre-and piece/song-recognition tasks (Gutiérrez & García, 2016). In some music-generative systems -such as DarwinTunes -selection is devolved to human choice, the power and reach of the internet making such crowd-based evaluations of candidate patterns relatively easy to solicit.…”

A New Hybridized Dimensionality Reduction Approach Using Genetic Algorithm and Folded Linear Discriminant Analysis Applied to Hyperspectral Imaging for Effective Rice Seed Classification

“…GAs operate using a wrapper-based approach where an optimal subset of features is selected by analysing the relationship between the entire feature sets and classification models for increased classification performance. GAs have been applied for feature selection and reduction, providing very promising results in a number of applications including vowel [46], learning [47], and cancer data classification [48], fish species prediction [49], biometrics authentication [50], activity recognition [51], spam email detection [52], and emotion and genre detection [53].…”