東海大学紀要情報通信学部
Proc. Schl. ITE Tokai Univ.
vol.3,No2,2010,pp.31-36
2010-PaperPaper

Music Recommendation System using Heart Rates

Tamotsu NOJI *1 and Tadashi OGINO *2(Received on Oct.29, 2010 and accepted on Feb.10, 2011)

Keywords:

1. Introductionof the songs in these sites.
Some of such sites have a recommendation system. It

Most of the current music search systems use therecommends some pieces of music according to the user's

attributes of music, like title, singers, players and other staticsearch history and/or his music libraries. One of the mostinformation. Though these systems work generally well, they

famous systems is 'Genius' in Apple iTunes Store 1) . As Appleare not suitable to find music according to your dynamic

iTunes Store has a lot of user data, they can analyze music

conditions, such as you are happy, tired or sad etc. There are

preferences tendencies and recommends some set of music

no famous music search systems using the biologicalaccording to their data. Most of such recommendation

information of the user. We assume that there exists thesystems use the simple idea: 'If you like song A and most of

relation between the listener's biological conditions and hisother users who like song A like song B, you must like songthen favorite songs. As first step, we tried to use the heart

B'.
With this method, as the result is led by statistical analysis,

rates to find preferable music as the heart rates can be easily

an unnatural result may be brought in some cases.

measured using commercial equipment.
In this paper, we propose a new music recommendation

2.2 Ventricular Rate and Musicsystem using our assumption. We tried to confirm our idea byMusic has various influences on person's psychology. Ifquestionnaire and brain wave data.
Our goals are (1) to confirm the effectiveness of our idea, (2)you listen to the gloomy music, you will get tired. If you

to build the experimental system using our idea.

listen to the bright music, you will get happy 2) 3) . It is known
that, when the walking speed matches the tempo of music, the2. Related Works and Methodology

effects of pleasure and peace of mind can be obtained 4) .
In the research area of music therapy, it is known that

2.1 Music Recommendation Systempeople feel comfortable and relaxed when his heart rates andIn recent years, there are many music distribution internet

the tempo of the music he is listening synchronize 5) . Our

sites. Users can search their desired music using the attributes

research is to construct a new music recommendation system*1 Prof. of School of Information and Telecommunication
Engineering*2 Mitsubishi Electric Information Technology, Inc.

using the heart rates of the users and the tempo of the music.

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Music Recommendation System using Heart Rates− 32 −

Tamotsu NOJI・Tadashi OGINO

3.4 Music Search
Fig.4 is the start screen shot of the music search function.
The user is asked to input his heart rates (average or current)and his music preferences. Then the system searches thefavorite pieces of music and show the result list to the user.
Fig.5 is the screen shot of the search result.

Fig. 6

Trial listen screen

3.6 Recommendation Function
After the user selects the one piece from the search result
list, the system recommends other pieces of music withsimilar BPM (Fig. 7).
Fig. 4 Music search screen

Fig. 7 Recommendation screen
4. System Evaluation and Discussion
Fig. 5 Search result screen

4.1 Preliminary Experiment
Before we evaluate the proposed system, we started to

3.5 Trial Listencollect data to find the relation between people's heart rates
and his favorite music.

After the list of music is shown, the user can selectFor eight subjects, we examined his heart rates and his taste

anymusic piece from the search result list. The detail
information of the selected piece is displayed.

of the music. As we didn¶t gather a lot of data, assume thisThe user canwas one case study.

try to listen to that piece (Fig. 6).

The subjects of this experiment are men and women from theteens to 50's.

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Tamotsu NOJI・Tadashi OGINO− 35 −

Music Recommendation System using Heart Rates

5. Conclusion
In the commercial music distribution site, it is expected torecommend the user his preferable music. Our proposedsystem can be used to find user's favorite music even if it isnot famous.
Out system may not be suitable for the users who only
listen to his favorite artists. We are going to expand oursystem to use other information. Also we have a plan to apply
our system to the music therapy field.

Acknowledgment
We'd like to express my gratitude to everyone who helps
ourexperimentinInformation

andCommunications

Technology Division of Tokai University.

References
1) Christopher Mims, How iTunes Genius Really Works,[Online]. Available:
http://www.technologyreview.com/blog/guest/25267/
2) William Benzon, Music in mind and culture,
Kadokawa Shoten, 2005. (in Japanese)
3) Keito Fujiwara, Advanced Sound Therapy Treatment
Mozart, Magazine House, 1998. (in Japanese)4) Yoshiaki Fujiwara, Natural rhythm of playing rhythm
fluctuation, Hakusui sha, 1990. (in Japanese)5) Yasuji Murai, Basic Music Therapy, Ongaku no tomo,
1995. (in Japanese)6) (OL]DEHWK6FRWW³0XVLFDQG<RXU%RG\+RZ0XVLF
$IIHFWV8VDQG:K\0XVLF7KHUDS\3URPRWHV+HDOWK´[Online]. Available:
http://stress.about.com/od/tensiontamers/a/music_therapy.htm
7) iBPM Counter for iPod, [Online]. Available:
http://www.cdj-s.com/bpm/bpmcounter.html

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