Abstract
In previous work, we have proposed the automatic sensitive word score detection system for a user dependent music retrieval system. However, the user dependent method causes a lot of burdens to the user because the system requires a lot of data for adapting it to each user. Hence, in this paper, we propose an automatic sensitive word score detection method for a user independent music retrieval system and evaluate the proposed method using 225 music data. Experimental results show that 87.5% of music patterns succeeded in detection of sensitive word score in the case that the difference between estimated and evaluated score is 1 (Error 1 rate). Moreover, we conduct subjective evaluation experiments to evaluate the proposed method as a utility method. From this experiment, it is observed that the user satisfaction level of the proposed method is higher than random selection impression detection.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Sugihara, T., Morimoto, K., Kurokawa, T.: m-RIK: Music Retrieval System Specialized in Individual Kansei Characteristic. The Journal of Information Processing Society 46(7), 1560–1570 (2005) (in Japanese)
Ikezoe, T., Kajikawa, Y., Nomura, Y.: Music Database Retrieval System with Sensitivity Words Using Music Sensitivity Space. The Journal of Information Processing Society 42(12), 3201–3212 (2001) (in Japanese)
Otsuka, L., Kajikawa, Y., Nomura, Y.: A Study on a Music Retrieval System for PCM Data by Sensitivity Words. In: Proc. of DEWS 2003, 8-p-05 (2003) (in Japanese)
Hirae, R., Nishi, T.: Mood Classification of Music Audio Signals. The Journal of the Acoustical Society of Japan 64(10), 607–615 (2008) (in Japanese)
Tanoue, N., Nishi, T.: Sensibility Classification of Music Audio Signals Using Time-Frequency Image Pattern. In: Proc. of ASJ 2009 (2009) (in Japanese)
Miyoshi, M., Oyama, T., Choge, H.K., Karungaru, S.G., Tsuge, S., Fukumi, M.: Music Classification using Sensitive Words. In: Proc. of NCSP 2010, pp. 211–214 (2010)
Tzanetakis, G., Essl, G., Cook, P.: Automatic Genre Classification Of Audio Signals. In: Proc. of ISMIR 2001 (2001)
Goto, M.: A Real-time Music Scene Description System: A Chorus-Section Detecting Method. In: MUS 2002, vol. 2002(100), pp. 27–34 (2002) (in Japanese)
Goto, M., Hashiguchi, H., Nishimura, T., Oka, R.: RWC Music Database: Database of Copyright-cleared Musical Pieces and Instrument Sounds for Research Purposes. The Journal of Information Processing Society 45(3), 728–738 (2004) (in Japanese)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Miyoshi, M., Tsuge, S., Choge, H.K., Oyama, T., Ito, M., Fukumi, M. (2010). Music Impression Detection Method for User Independent Music Retrieval System. In: Setchi, R., Jordanov, I., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based and Intelligent Information and Engineering Systems. KES 2010. Lecture Notes in Computer Science(), vol 6278. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15393-8_68
Download citation
DOI: https://doi.org/10.1007/978-3-642-15393-8_68
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15392-1
Online ISBN: 978-3-642-15393-8
eBook Packages: Computer ScienceComputer Science (R0)