INTRODUCTION
Classification of Music Genres using AI & ML algorithms is an important thing to discuss on. Sound is a form of an audio signal having parameters like frequency, decibel, bandwidth etc. Hence, an audio signal can be expressed as a function of Amplitude(a) and Time(t). Hence, these audio signals come in different formats which make it possible and easy for the computer to read & analyze them. Some formats are: mp3 format, WMA format and wav format.
Companies (such as Soundcloud, Apple Music, Spotify, etc) use music classification, either to place recommendations to their customers, or simply as a product. To be able to do any of the above functions, determining music genres is the first step. To achieve this, we can take the help of Artificial Intelligence & Machine Learning algorithms.
These algorithms prove to be very handy in Music Analysis too. Hence, Music Analysis is done based on a song’s digital signatures for some factors, including acoustics, danceability, energy etc., to determine the kind of songs that a person has interest to listen to.
Classification of genre can be very valuable to explain some interesting problems such as creating song references, tracking down related songs,etc. Hence, it would be a very valuable addition to music information retrieval systems. Hence, automatic classification of music into genres can provide a framework for development and evaluation of features for any type of content- based analysis of musical signals, which would surely reduce human efforts.
The concept of automatic music genre classification has become very popular nowadays. Hence, Dividing music into genres is arbitrary, but there is a particular criteria that are related to instrumentation, structure of the rhythm and texture of the music, etc. Hence, this plays an important role in characterizing a particular genre. For decades, genre classification for digitally available music has been performed manually. So, techniques for automatic genre classification would be a valuable addition to the development of audio information retrieval systems for music.
What is Music Genre Classification?
Music Genre Classification is an area which has attracted the interest of many researchers like Charles Burlin, Raphael Lenain from Stanford University, etc. Also, Support Vector Machines, Neural Networks, Decision Trees and K-Nearest Neighbours are various methods in use to perform classification. Convolutional Neural Network is in use to perform classification in this process.
Process of Music Genre Classification-
Objectives:
1. Developing a machine learning model that classifies music into genres,
based on various different features, instead of manually entering the genre.
2.To reach a good accuracy so that the model classifies new music into its genre correctly
3.This model should be better than at least a few pre existing models.
Working principle and Methodology
•Dataset Collection:The music data is collected from music channels using a TV tuner card.
Spectrogram Generation
• Hence, a spectrogram is a 2D representation of a signal, having time on the x-axis and frequency on the y-axis.
• Classifier
Diagram of classifier working and spectrogram generation:
