34 results for “topic:song-recommender”
UC Berkeley team's submission for RecSys Challenge 2018
Implementation of music genre classification, audio-to-vec, song recommender, and music search in mxnet
No description provided.
In this project, I create a recommendation algorithm to give song recommendations on Spotify.
This is Content based music recommendation system. I have used both audio features and lyrics (text based) features for recommending most similar songs to a given query song.
Web app for custom Spotify playlists based on track audio properties such as danceability, energy, tempo & your personal Spotify listening analysis
My first end-to-end project, using Web Scraping with BeautifulSoup and Spotify API. Clustering songs based on its audio features.
A simple song recommendation engine using K-Nearest Neighbors with the Spotify Music Dataset
A Prolog song recommender
Song Recommendation using K-NN.
This is Content based music recommendation system. I have used both audio features and lyrics (text based) features for recommending most similar songs to a given query song.
Spotify Playlist Generator
SpotifySongRecommender is a C++ project for analyzing and recommending songs. It includes features like recommending songs and artists based on musical genres, and generating popularity rankings by artist or genre.
A website which suggest songs on the basis of the recognized facial emotion.
Sangeet.com made use of Spotify API to get songs recommendations and provide link so that you can enjoy more fresh songs. Made using html, css, node, express with help of Postman
Machine Learning Algorithms using GraphLab
A mac-based song analyzer application built to classify and play songs based on your mood.
The best trvia app for music lovers, boasting the ability to retrive songs realtime based on the users spotify taste profile! Allows multiple users to log in or even select songs based on your favourite artists.
A comprehensive song recommender system. Features include scraping Billboard Hot 100, processing the Million Song Subset, clustering with KMeans/DBScan, and a Streamlit app for generating music recommendations.
I analysed a dataset obtained from last.fm to recommend the next songs a user is likely to hear. Used NearestNeighbor algorithm for predictive analysis.
Plays a random song from the Spotify catalog
Song recommender system using Turicreate library to recommend songs.
API written in python for SongRadar
A locally-run song recommendation tool that converts song attributes into vectors to find acoustically similar matches across a quarter billion tracks
No description provided.
Recommending songs with personalization and based on simple popularity-based recommender.
Following is a song recommender NN model in google colab using turiCreate developed and used by apple for research. It aims to predict likeable songs based on their history and data of liked ones.
This project demonstrates a collection of Data Science techniques using R. These include Data Analysis, Data Cleaning, Data Visualization, Support Vector Machines, Euclidean Distance, and K-Means Clustering.
Song recommender built end-to-end combining BeautifulSoap4 and Spotify API
Web Portal for the [RUMusic](https://github.com/vraj152/RUMusic)