Kyogu Lee

We describe an acoustic chord transcription system that uses symbolic data to train hidden Markov models and gives best-of-class frame-level recognition results. We avoid the extremely laborious task of human annotation of chord names and boundaries-which must be done to provide machine learning models with ground truth-by performing automatic harmony analysis on symbolic music files. In parallel, we synthesize audio from the same symbolic files and extract acoustic feature vectors which are in

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We reconfirmed that the TSR/STA method is an effective modality to objectify VPT. In addition, the potential role of the TSR/STA method in the objective evaluation of treatment outcomes in patients with VPT was proven. Further studies incorporating a larger sample size and more refined recording techniques are warranted.

This extended abstract describes in detail a submission to the task on Audio Cover Song in the Music Information Retrieval eXchange in 2006. The system uses as feature set a chord sequence identified by an HMM trained with audiofrom-symbolic data, and computes a distance between two chord sequence pair using the Dynamic Time Warping algorithm to find the minimum alignment cost. The rational behind the system is that cover songs largely preserve harmonic content even if they vary in other musical

A novel approach for obtaining labeled training data is presented to directly estimate the model parameters in a supervised learning algorithm for automatic chord recognition from the raw audio. To this end, harmonic analysis is first performed on symbolic data to generate label files. In paral-lel, we synthesize audio data from the same symbolic data, which are then provided to a machine learning algorithm along with label files to estimate model parameters. Experimental results show higher per

Quantitative evaluation of piano performance is of interests in many fields, including music education and computational performance rendering. Previous studies utilized features extracted from audio or musical instrument digital interface (MIDI) files but did not address the difference between hands (DBH), which might be an important aspect of high-quality performance. Therefore, we investigated DBH as an important factor determining performance proficiency. To this end, 34 experts and 34 amate

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Recently unsupervised learning algorithms have been successfully used to represent data in many of machine recognition tasks. In particular, sparse feature learning algorithms have shown that they can not only discover meaningful structures from raw data but also outperform many hand-engineered features. In this paper, we apply the sparse feature learning approach to acoustic scene classification. We use a sparse restricted Boltzmann machine to capture manyfold local acoustic structures from aud

Dance and music often co-occur as evidenced when viewing choreographed dances or singers moving while performing. This study investigated how the viewing of dance motions shapes sound perception. Previous research has shown that dance reflects the temporal structure of its accompanying music, communicating musical meter (i.e. a hierarchical organization of beats) via coordinated movement patterns that indicate where strong and weak beats occur. Experiments here investigated the effects of dance