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music_genre_classifier

Given an MP3 file we attempt to predict it's genre.

music_recommendation

Music recommendation system including Neural Network

music_recommendation_system

Music recommendation system based on analysis of the audiosignal

musicbox

网易云音乐命令行版本

musicbrainz

using musicbrainz to tell me when new albums from bands i like are out!

musicbrainz-bot

musicbrainz-bot-1

musicbrainz-data

MusicBrainz Java data bindings using Hibernate

musicbrainz-elasticsearch

Index music album from the MusicBrainz open music encyclopedia into Elasticsearch

musicbrainz-packer-template

A Packer Template to Create a Musicbrainz Server EC2 AMI

musicbrainz-to-aws

A few scripts to help quick generation of a musicbrainz vm in AWS EC2

musicbrainz-track-parsers

A collection of scripts for used to output music tag information in a format suitable for insertion into MusicBrainz' Track Parser

musicbrainz_automatcher

musicbrainz_automatcher matches artists/tracks names to MusicBrainz intelligently

musiccabinet

Artist radio, genre radio, related artists and more. Based on last.fm knowledge and your music.

musicdb_builder

music database builder and crawler using python django with lxml and requests

musicgenie

a instant music download app

musicgenreclassification

Classify music genre from a 10 second sound stream using a Neural Network.

musician_recom

A tentative development project: reocomendation engine providing user suggestions

musicindicator

Music indicator for Android. Easy to use. 🎧 ✨

musicmachine

The Music Machine uses semantic web technologies to profile users based on their music interests and offer suggestions on songs and artists based on their current interests.

musicmila

MusicMila is a music streaming web application that plays music tailored to your mood, daily activities and location of the tweets. Much more than radio, it's a tool to create auto playlists for every occasion, with the ability to tag and share music with everyone you know

musicmood.me

Open source mood detection with Node.js and Python

musicrecommend

powered by netease web api, can get the recommend music every day.

musicrecommendation

Using decision tree to recommend music

musicrecommender

Music Recommendation using Python + Spark

musicrecommender-1

lastFM data used for recommendation of new interprets.

musicrecommendersystem

A recommender system that will recommend new musical artists to a user based on their listening history

musicrepair

:musical_score: Fixes music metadata and adds album art.

musicstreamer

A Music Player for android that renders beautiful DNA(Visualization) of the currently playing music.

musicsuggestor

Song suggester is an innovative application which will learn the behaviour of the listener while playing songs. The application will use finger printing methodology of the Echo Nest (An open source database having records of over 30 million songs) to identify songs in the application. In this Project there will be a music player with various options which will play mp3 files from hard disk of the client or user which helps to refer sources from the directory of the system and to play the song through the system’s output. The system will analyse the sample of the file it is playing and produces the code string using the code generation tool made available by echo nest. This code string will be sent to the web server where the details of all the identified music will be saved. The web server is maintained to collect all the music identified by the users of this application, which will have a user account in this web server. This web server will have the API which is required for the precise identification of the song from Echonest. Beyond that, the web server will analyse the behaviour of the user’s listening habit and to suggest songs accordingly. The Song Suggestion part will have algorithms for learning the user’s listening habits through identification of songs and the suggestions of songs according to the user’s selection of classifications like most popular, user specific favourites etc. The website will use the web server’s database to analyse a user’s taste and help them to create suggestions for groups, friends and discussions. It will also help the user to analyse the top hits and new trends in music. The proposed system will help the user to get the most appropriate song he likes, information about new releases and artists. The user can search for suggested songs in the internet; it recommends songs according to the criteria of the user and provides opportunities to learn the user’s listening habits.