Joonas Kauppinen

I am a music and statistical data analysis professional. I have studied statistics at the University of Tampere, Finland, where I have got doctoral level training. I am an analytic and creative thinker, eager to develop and learn new technologies.

Core skills and research interests:


Joonas Kauppinen. Self-similarity matrix modeling using the nonnegative matrix factorization. In Antti Niemistö, editor, Digest of TISE Seminar 2013, volume 12 of TISE Publications, pages 21–26, Tampere, Finland, 2013. Tampere Doctoral Programme in Information Science and Engineering.

Joonas Kauppinen. The R Student Companion by Brian Dennis. International Statistical Review, 81(2):309, 2013. (doi:10.1111/insr.12020_3)

Joonas Kauppinen, Anssi Klapuri, and Tuomas Virtanen. Music self-similarity modeling using augmented nonnegative matrix factorization of block and stripe patterns. In Proc. IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, New Paltz, NY, 2013. (PDF, 214322 bytes) (doi:10.1109/WASPAA.2013.6701855)

Self-similarity matrices have been widely used to analyze the sectional form of music signals, e.g. enabling the detection of parts such as verse and chorus in popular music. Two main types of structures often appear in self-similarity matrices: rectangular blocks of high similarity and diagonal stripes off the main diagonal that represent recurrent sequences. In this paper, we introduce a novel method to model both the block and stripe-like structures in self-similarity matrices and to pull them apart from each other. The model is an extension of the nonnegative matrix factorization, for which we present multiplicative update rules based on the generalized Kullback–Leibler divergence. The modeling power of the proposed method is illustrated with examples, and we demonstrate its application to the detection of sectional boundaries in music.

Joonas Kauppinen. Content-based data mining of musical scores. In Antti Niemistö, editor, Digest of TISE Seminar 2012, volume 11 of TISE Publications, pages 74–78, Tampere, Finland, 2012. Tampere Doctoral Programme in Information Science and Engineering.

Joonas Kauppinen. Music Data Mining edited by Tao Li, Mitsunori Ogihara, George Tzanetakis. International Statistical Review, 80(1):189–190, 2012. (doi:10.1111/j.1751-5823.2012.00179_13.x)

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