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Build a personalized Music Recommendation System using Spotify API and Python. The system uses content-based and hybrid filtering to suggest songs based on user preferences, enhancing the music discovery experience.
Code for Direct-Reconst proposed in Multi-Task Learning Approaches to Leveraging Music Source Separation In Music Similarity Representation Learning Based On Individual Instrument Sounds
Code for Cascade-FT proposed in Multi-Task Learning Approaches to Leveraging Music Source Separation In Music Similarity Representation Learning Based On Individual Instrument Sounds
This repository contains a web application that integrates with a music recommendation system, which leverages a dataset of 3,415 audio files, each lasting thirty seconds, utilising a Locality-Sensitive Hashing (LSH) implementation to determine rhythmic similarity, as part of an assignment for the Fundamental of Big Data Analytics (DS2004) course.
Sistema de recomendação de música baseado em Databricks e API do Spotify, com etapas de importação de dados, análise exploratória e clusterização para agrupar músicas semelhantes