musicgenre
There are 5 repositories under musicgenre topic.
Prajwal10031999/Song-Genre-Classification-in-PySparks-MLlib
A PySpark MLlib classification model to classify songs based on a number of characteristics into a set of 23 electronic genres.
SonamSangpoLama/Music-Genre-Classification
Music genres is the taste, style and relax giving flow of a music. The genre of music refers to multiple types and categorization of music. The different types of famous music genre that we widely known are rock, jazz, reggae, classical, folk, blues, R & B, metal, dubstep, techno, country music, electro and pop. The key success of music in music industry is the genres of classified music that becomes a significant part of communicating music that provides bonding with relatively to human and masses of people. In contrast, the genre that falls under top-level style of rock are punk, indie, shoegaze, AOR and metal. They are basically subgenre of a music classification and it is important describing music to other people. In practical life, music is often used for multiple purposes due to physiological and social effects. Companies like Spotify, Soundcloud, Apple Music, Wynk & products like Shazam use music classification to provide their customers different flavour of music by recommending music they prefer to listen. we use python libraries such as Librosa and PyAudio library for audio processing in Python. We apply and use GTZAN dataset that is composed of 1000 audio tracks each 30-second-long representing 10 genres with 22050Hz mono audio file of 16bit in .au format for dataset. The functionality and working of music genre classification determine the help of Machine Learning algorithms. The algorithm such as KNN and artificial neural network (ANN) analyses and find out the similar similarity of genre features of music and classify it.
hsprcode/Audio-Signal-Classification-using-Convolutional-Neural-Networks
Extracted features and classified GTZAN Dataset via deep neural networks with reduced number of parameters and achieved a maximum of 81.62% classification accuracy using 1D-CNN.
NitishaS-812k/Music-Genre-Classifier
This repository contains the code for some models that classify music files into their specific genres
saibhaskardevatha/music-genre-classification
With day-by-day increasing internet penetration, huge amount of useful data is available at proximity to people. Although it seems that there is ease of access to data, but this exponentially increasing amount of data brings to table a new problem – most of this chunk is unclassified. Through this project, we aim to resolve this problem with something very close to people – music. We aim to explore various methodologies used to develop an automatic music genre classifier and thus, help in comparing efficiency to these methods.