Automated music genre classification using machine learning

Automatic music genre classification using Machine Learning algorithms like- Logistic Regression and K-Nearest Neighbours

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.

Music Genre Classification using Various Machine Learning Techniques

Music genre classifier is a machine learning and deep learning based music genre classification system. Here is how it is made: We trained three deep learning models and two machine learning models on the GTZAN dataset which is a dataset for different music geners. We applied three neural networks and two machine learning models which are Light Gradient boosting machine and Extra Tree Classifier. Then all the models vote to get the finbal fresult. Now, when we have a new song to classify then, we use librosa libraray to extract its features and give them to the model to give us the final result i.e. the genre of that music

Audio classification or sound classification can be referred to as the process of analyzing audio recordings. This amazing technique has multiple applications in the field of AI and data science such as chatbots, automated voice translators, virtual assistants, music genre identification, and text to speech applications. In this case we are classifying the given dataset consisting of 6500 speech files into the corresponding emotions portrayed and their levels. We will be implementing Audio classification by using the TensorFlow machine learning framework. We would be considering a raw audio dataset and categorize it based on the two 􏰜􏰕􏰟􏰆􏰜􏰏 􏰠􏰡􏰆􏰏􏰢􏰓 􏰕􏰑􏰖 􏰠􏰡􏰆􏰏􏰣􏰓. Followed by pre-processing, creating, and training a deep learning model to perform classification. We convert the audio signal from raw audio into two spectrograms before being fed into the models. A spectrogram is a visual way of representing the signal strength of a signal over time at various frequencies present in a particular waveform.

During the project for the DIGITAL SIGNAL IMAGE MANAGEMENT course I learned how to manage and process audio and image files. The aim of the project was the classification, through machine learning and deep learning models, of musical genres by extracting specific audio features from the "gtzan dataset" dataset files with which to train the models (SVM, Linear Regression, Decision tree , Random Forest, Neural Network). Mel spectograms were also extracted to train convolutional neural network models. In addition, the extracted audio features have been used to develop a model of music retrieval which given an audio track in input produces as output 5 audio tracks recommended meiante the use of cousine similarity.

Automatic Music Genre Classification using Machine Learning

For my ML Final Project, I created a machine learning classification model to distinguish between four genre classes of music using data from SPotify

Majority of the commercial music platforms rely heavily on deep learning and natural language processing for the purpose of finding similarities between songs, classification of songs, new lyric generation, informational retrieval, gaining meaningful insights or efficiently analyze music. My main focus was to use raw lyrics data and classify them as one of the 5 genres (Hip-Hop, Rock, Pop, Country and Jazz). For this purpose, architecture such as Machine learning classifiers, Deep Neural Networks, LSTM and Bi-Directional Transformers were investigated. Various Data preprocessing and network optimization techniques were utilised and While, It was observed that keras BERT model and LSTM produced similar results, BERT worked the best than the rest.

This repository is a collection of code for classifying music into different genres using various machine learning, deep learning, and neural network techniques. Link: https://manishankar9977-music-genre-classification-app-p5in0m.streamlit.app/

Spotify Rock Music Genre Classification using Machine Learning 🎸

Building music genre classification using machine learning algorithms and librosa

CS667 Machine Learning Final Project - Music Genre Classification using the GTZAN Dataset and other supplemental data

BERTunes-Classifier: Developed a music genre classification system achieving 93% accuracy using machine learning algorithms and textual analysis of song lyrics.

Design a Music Genre Recommendation System in Python Using a Decision Tree Classifier. This code contains training, testing, prediction, and model storage in Jupyter Notebook. Begin your machine learning career with this repo for Decision Tree music genre classification.

Music genre classification is a task in machine learning that involves predicting the genre of a given music track. This is typically done using audio features such as tempo, melody, and rhythm, which are then used to train a model to classify music into various genres such as pop, rock, jazz, or classical.

Innovative companies like Spotify and Shazam leverage music data in a very clever way to provide amazing services to their users. They use recommendation algorithms and automatic genre classification which greatly contributes to increasing user experience. From this project, we aim to perform such tasks of genre classification and music recommendation when musical features are provided. We basically aim to create a music recommender system and a playlist generator for companies like Spotify and Pandora. Inference of musical genre, whilst seemingly innate to the human mind, remains a challenging task for the machine learning community. We used various machine learning algorithms to achieve our goal. We made use of classification algorithms such as Logistic Regression, Naive Bayes Classifier, Neural Networks and Random Forest Classifier to identify genre of the music track. We also applied K-means clustering algorithm to create song clusters and recommend a song which the user is most likely to enjoy.

During the period of November 2019 and August 2020 I carried out my internship atT ́el ́ecom Paris in Paris. I worked under the Supervision of Isabelle Block as part of theImages team. We overtook the endeavour of automated musical analysis for musical genreclassification. The goal was to refine the notion of harmonic trajectory descriptor andinvestigate how to improve classification on symbolic music data. The hypothesis is thatthe harmonic trajectory of musical piece is an imprint that defines the genre of the pieceat a certain level.In this work, we focus on different methods to utilize the harmonic trajectory descriptoreither for classification purposes or transcription and key estimation purposes. We workedwith large databases of symbolic music scores, such as the Lakh Data-set. We presented amodular, in regards to descriptors, system for supervised learning, mainly using supportvector machines.

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Este repositorio contiene un proyecto de clasificación de géneros musicales utilizando técnicas de aprendizaje automático. Se implementan varios algoritmos de clasificación, incluyendo Regresión Logística, K Vecinos Más Cercanos, y Árboles de Decisión, y se comparan sus rendimientos utilizando validación cruzada

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This repository contains the development of a web application named Algorithm-Visualizer using HTML, CSS, and JavaScript to visually demonstrate sorting algorithms. The project aims to enhance understanding and retention of algorithmic concepts through dynamic visual representation.

The tool is made to classify different musical genres from audio files which a user will upload.

Real-Time Music Genre Classification using Machine Learning and Deep Learning techniques on the GTZAN Dataset. This project utilizes SVM, XGBoost and CNN models to classify music genres and features a graphical interface for real-time audio predictions.

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Music Genre Classification using Machine Learning on Spotify Data

Machine Learning based Music Genre Classification using DSP

Music feature analysis and Music Genre classification using Apache Spark and Machine Learning

Building music genre classification using machine learning algorithum and librosa