Next Article in Journal
Spectral Linewidth vs. Front Facet Reflectivity of 780 nm DFB Diode Lasers at High Optical Output Power
Next Article in Special Issue
Application of Aesthetic Principles to the Study of Consumer Preference Models for Vase Forms
Previous Article in Journal
A Novel Image Feature for the Remaining Useful Lifetime Prediction of Bearings Based on Continuous Wavelet Transform and Convolutional Neural Network
Previous Article in Special Issue
Study on Cleaning the Surface of Stainless Steel 316 Using Plasma Electrolysis Technology
Article Menu
Issue 7 (July) cover image

Export Article

Open AccessArticle
Appl. Sci. 2018, 8(7), 1103; https://doi.org/10.3390/app8071103

An Emotion-Aware Personalized Music Recommendation System Using a Convolutional Neural Networks Approach

1
Department of Electrical Engineering, College of Engineering, Chang Gung University, Kweishan, Taoyuan 33302, Taiwan
2
Department of Computer Science and Information Engineering, College of Engineering, Chang Gung University, Kweishan, Taoyuan 33302, Taiwan
3
Department of Otorhinolaryngology, Head and Neck Surgery, Chang Gung Memorial Hospital, Kweishan, Taoyuan 33375, Taiwan
4
Department of Electronic Engineering, Ming Chi University of Technology, Taishan Dist., Taipei 24301, Taiwan
This work was supported in part by the Ministry of Science and Technology, Taiwan, under Contract MOST 106-2221-E-182-065.
*
Author to whom correspondence should be addressed.
Received: 31 May 2018 / Revised: 25 June 2018 / Accepted: 6 July 2018 / Published: 8 July 2018
(This article belongs to the Special Issue Selected Papers from IEEE ICASI 2018)
Full-Text   |   PDF [3509 KB, uploaded 31 July 2018]   |  

Abstract

Recommending music based on a user’s music preference is a way to improve user listening experience. Finding the correlation between the user data (e.g., location, time of the day, music listening history, emotion, etc.) and the music is a challenging task. In this paper, we propose an emotion-aware personalized music recommendation system (EPMRS) to extract the correlation between the user data and the music. To achieve this correlation, we combine the outputs of two approaches: the deep convolutional neural networks (DCNN) approach and the weighted feature extraction (WFE) approach. The DCNN approach is used to extract the latent features from music data (e.g., audio signals and corresponding metadata) for classification. In the WFE approach, we generate the implicit user rating for music to extract the correlation between the user data and the music data. In the WFE approach, we use the term-frequency and inverse document frequency (TF-IDF) approach to generate the implicit user ratings for the music. Later, the EPMRS recommends songs to the user based on calculated implicit user rating for the music. We use the million songs dataset (MSD) to train the EPMRS. For performance comparison, we take the content similarity music recommendation system (CSMRS) as well as the personalized music recommendation system based on electroencephalography feedback (PMRSE) as the baseline systems. Experimental results show that the EPMRS produces better accuracy of music recommendations than the CSMRS and the PMRSE. Moreover, we build the Android and iOS APPs to get realistic data of user experience on the EPMRS. The collected feedback from anonymous users also show that the EPMRS sufficiently reflect their preference on music. View Full-Text
Keywords: convolutional neural networks; latent features; machine learning; music; user preference; weighted feature extraction convolutional neural networks; latent features; machine learning; music; user preference; weighted feature extraction
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Abdul, A.; Chen, J.; Liao, H.-Y.; Chang, S.-H. An Emotion-Aware Personalized Music Recommendation System Using a Convolutional Neural Networks Approach. Appl. Sci. 2018, 8, 1103.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Appl. Sci. EISSN 2076-3417 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top