The home assistant

Python to generate a customized Spotify playlist based on historic user listening data

Utilizing Information from the Spotify API to create data visualizations using D3.js and Plotly to analyze Spotify stream and track analysis.

DecadeTune is a machine learning project that classifies songs into decade groups by analyzing audio features and referencing popular hits. This project identifies the distinctive sound signatures that characterize music from various eras, from the 50s-60s to the 2010s-2020s.

A server for JAMS data

shazam like desktop app that you can select mp3 file and recognize the songs that matches the file and list them according to their similarity to the input file

graph audio features of Spotify tracks using the Spotify Web API

view a song's audio features 🎶📊

Analysis of the audio features from my weekly top tracks from Spotify using the library Spotipy to support the API extraction

This project is designed to analyze Spotify stream history metadata, extract audio features using the Spotify Web API, and generate a personalized playlist based on a trained neural network model.

Feature based synthesis in web audio

Spotify music analysis and a new method to recommend music!

Performed music data visualization using python-seaborn library and matplotlib library

R data package centered on Spotify’s Top 200 Most Streamed songs in the U.S. Includes audio features, rankings and artist-specific information for every song which reached the top 200 between January 1, 2017 and December 2019.

Analysing Miles Davis' Oeuvre with Spotify API

A Shiny App to Retrieve Daily Top 200 Spotify Charts with Audio Features in Real-Time

A simple music feature extractor for Deep Learning models

Audio input -> real-time analysis -> OSC output. Takes in real-time audio, does feature extraction using smart algorithms then sends out OSC to be used in other programs.

Estratto is a powerful and user-friendly Rust library designed for extracting rich audio features from digital audio signals.