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Kamarul is Master student of Cyber Informatic (CI) Program.

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An automatic metadata generation method for culture-based music
impressions.

Student Name : Kamarul Arif Bin Yusoff
Supervisor : Prof. Yasushi
Kiyoki
Student ID : 80825355
E-Mail : [email-masked]

Abstract

Communication plays an important role in this edge. Music is one of the
successful medium for people to communicate because of its universal
characteristic. It is necessary to generate metadata for music impressions in
order to communicate between cultures. Our system provides method to obtain
culture-based musical impression. The method consists of 5 steps: 1) Music
features extractions. 2) User impression collections. 3) User impression table
creation. 4) Semantic correlation calculation and 5) Obtaining the highest
ranking of semantic correlation. This method is able to realize the music
impression generation according to Malaysia, India and Japanese Gagaku music as
sample music. By this approach, an automatic generation for intercultural music
or other media impression can be realized in order to bridge the culture divide.

Keywords

1. Music Database 2. Music impression 3. Culture-based Music 4. Metadata
generation 5. Intercultural

Introduction

In general, communication can be defined as information transferring between
two parties. The process of transferring the information can be in various ways
such as speech, discussion, music, sign language, writing and touch. When the
internet becomes more popular in early 1980s, people get an easier tool to
communicate. As mention above, music is one of the best way to communicate
between people even though the communication dealing with infants [1]. It is
because some groups of people the might not have ability to speak using language
like children or people who cannot speak in common languages. "Music is, by
its very nature, essentially powerless to express anything at all, whether a
feeling, an attitude of mind, a psychological mood, a phenomenon of nature....
"(Stravinsky,1936) By using music, people might able to express their
feeling and sharing their thought by giving impression towards the music.[2]

The key technology for this system is using music sample to
represent various types of music. Since every people and individual define
each of music differently, it is very hard to make transformation matrix to
represent each individual perception towards certain type of music. In order to
overcome this problem, we collect sample music and each of the song will
assigned the suitable impression according to each person.

Our basic system concept is offer the generation of metadata for music
impression. This system is able to receive new music or a query and do certain
process. Basically, the process contains the music features extraction and
correlation calculation.

This system did musical feature extractions into six elements namely key,
tempo, pitch, rhythm, harmony and melody. After that, system calculates the
semantic correlation between query and among sample music and generates the
impression for the query

Related Work

There are several related studies about music impression and emotions. The
role of individuality in expressing music is discovered through the paper
[3][4]. Individual expression plays very important part to provide music emotion
recognition. Personal impression was shown as a good indicator and improved the
accuracy of emotion recognition. The individual or personal expression is very
important because each individual is exposed with different personality,
culture, demographic and music experience. Because of these differences, our
study is proposing the best method to extract the music impression.

To detect the music impression, it is necessary to choose the music elements.
The musical elements chosen can be a modulator in conveying emotion or mood.
Some studies analysis tempo as major elements to detect emotions [4]. However,
most research shows, more musical elements involve to be analysed to represent
particular music and to gain the accuracy of impression. [5][6][7].

The proliferation of musical data in the network is unavoidable and lead to
rapid growth in music retrieval research. Besides retrieving the musical data,
the access to musical impression gain interest to researchers. More method
introduced to deal with music and impression [8][9]. The idea is to generate
metadata for music and it can be access by dealing with impressions. Some engine
utilizes the text based query such as impression words and from the words, the
associated music are accessible. However, the query also can be done by
multimedia [10] such as audio files and picture files.

Method

1) Music features extractions.

2) User impression collections.

3) User impression table creation.

4) Semantic correlation calculation and

5) Obtaining the highest ranking of semantic correlation.

Implementation and Experimental studies

The implementation of this method we divided into two type of experiment.
First, Conjugate Test and second is Simple Test. In Conjugate Test, the songs
selections we collect are twenty three songs and the user participate in
answering questionnaires are twelve people. For Simple Test, the songs
selections only involve seven songs and the people who participate to answer the
questionnaire imply to seven people.

Conjugate Test

In this test we used 23 music from Malaysia/Indonesia and 23 music from
India as sample music. We calculate the semantic correlation all song with the
4 query music. And from that process we obtain teh highest ranking for each of
query in order to get the music impression

Simple Test

In this test we used Japanese Gagaku music as a sample music. However the
number of music is less than 10 which is only 7 music. We used same method to
obtain the music impressions. The idea is to see whether number of sample
music will effect the result of producing music impressions.

Applications

I built an web application to calculate the correlation between query
features and sample music features. For each of test stated as above, and as for
example, we get the result as follow:

Conjugate Test

Simple Test

Conclusion

We believed that communication plays and important role nowadays because of
the rapid enhancement of technologies. One of the best medium to communicate is
music and from it we can express thought and impression. Our objective in this
research is to generate the musical impression based on different culture so
that different culture can communicate each other through music
media.

The idea of music communication not only for social aspect but it
can be in economic side. In music industries for example, the communication
using music makes the industries easily to exchange market of musical outcome or
entertainment product.

This research produced an easy method to achieve
metadata generation of culture-based music impression. The idea to make the
method less complex is due to reducing the transmission or congestions of the
traffic inside of the communication edge. This research successfully realized
the metadata generation method for culture-based music impression automatically.

Reference

[1] Benjaman, S., gMusic as a Tool in Communications Research.h Nordic
Journal of Music Therapy, Vol.7(1), pp 40-49, 1998.

[2] Mark, M.T.,
Flora, V.G., gMusical Expression of Moodstates.h Psychology of Music, Vol.19,
No. 2, pp.99-109, 1991.

[3] Yang, Y.H., Su, Y.F., Lin, Y.C., Chen, H.H.,
Yang, Y.H., Su, Y.F., Lin, Y.C.,&Chen, gMusic emotion recognition: role of
individuality.h ACM SIGMM Int. Workshop on Human-centered Multimedia 2007,
pp.13-21,2007.

[4] Rosa. G, Cesar.A.C., gIntegrating Emotions and
Knowledge in Aesthetics Designs Using Cultural Profiles.h,
ISBN-978-3-540-73288-4, 2007.

[5] Hevner, K., gExpression in music: a
discussion of experimental studies and theories,h Psychological Review, Vol.42,
pp.246-268,1935.

[6] Hevner, K., gExperimental studies of elements of
expression in music,h American Journal of Psychology, Vol.48, pp.186-204,1936.

[7] Hevner, K., gThe affective value of pitch and tempo in music,h
American Journal of Psychology, Vol.49, pp.621-630,1937.

[8] Asako, I.,
Yasushi, K., gAn Automatic Metadata Generation Method for Music
Retrieval-by-Impression Dealing with Impression-Transition,h In Proceedings of
the 7th IASTED International Conference on Internet and Multimedia System and
Applications. 281-288, 2003.

[9] Asako, I., Yasushi, K., gA Kansei
Metadata Generation Method for Music Data Dealing with Dramatic
Interpretation,h Information Modeling and Knowledge Bases, IOS Press, 2004.

[10] Sahori, I., Shuichi, K., Asako, I., Yasushi, K., gA Music
Retrieval System Supporting Intuitive Visualization by the Color Sense of
Tonalityh. In Proceedings of the 24th IASTED International Conference on
Applied Informatics, pp.153-159. 2006.

[11] Taahashi, K., Yasushi, K.,
gFundamental framework for media data retrieval systems using media-lexico
transformation operator ? in the case of musical MIDI data,h Information
Modelling and Knowledge bases XII, IOS Press, 2001.

[12] Perfessor Bill
Edwards, gYour MIDI recordings are spread over three or more tracks and
channels, which makes it difficult to play them on an outboard device or piano
that only plays one at a time. Why do you do this?h, 5 September 2009,

[13] David Miles Huber, gThe MIDI
manual: a practical guide to MIDI in the project studioh, Focal Press, 1999, pp
4.

[14] C.L.Krumhansl. gCognitive foundations of musical pitch.h
Oxford@University Press, 1990.

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