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A Machine Learning based Music Retrieval and Recommendation System

Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016)

DOI:10.63317/2fxuwvqqv6r8

Abstract

In this paper, we present a music retrieval and recommendation system using machine learning techniques. We propose a query by humming system for music retrieval that uses deep neural networks for note transcription and a note-based retrieval system for retrieving the correct song from the database. We evaluate our query by humming system using the standard MIREX QBSH dataset. We also propose a similar artist recommendation system which recommends similar artists based on acoustic features of the artists' music, online text descriptions of the artists and social media data. We use supervised machine learning techniques over all our features and compare our recommendation results to those produced by a popular similar artist recommendation website.

Details

Paper ID
lrec2016-main-312
Pages
pp. 1970-1977
BibKey
mostafa-etal-2016-machine
Editor
N/A
Publisher
European Language Resources Association (ELRA)
ISSN
2522-2686
ISBN
978-2-9517408-9-1
Conference
Tenth International Conference on Language Resources and Evaluation
Location
Portorož, Slovenia
Date
23 May 2016 28 May 2016

Authors

  • NM

    Naziba Mostafa

  • YW

    Yan Wan

  • UA

    Unnayan Amitabh

  • PF

    Pascale Fung

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