Corey Kereliuk: "Improved Hidden Markov Model Partial Tracking Through Time-Frequency Analysis"
This talk discusses a modification to the combinatorial hidden Markov model developed by Depalle, Garcia, and Rodet for tracking partial frequency trajectories. We employ the Wigner-Ville distribution and Hough transform in order to (re)estimate the frequency and chirp rate of partials in each analysis frame. We estimate the initial phase and amplitude of each partial by minimizing the squared error in the time-domain. We then formulate a new scoring criterion for the hidden Markov model which makes the tracker more robust for non-stationary and noisy signals. We achieve good performance tracking crossing linear chirps and crossing FM signals in white noise as well as real instrument recordings.
Jason Hockman: "Automated Rhythmic Transformations of Musical Audio"
Time-scale transformations of audio signals have traditionally relied exclusively upon manipulations of tempo. This paper presents a novel technique for automatic mixing and synchronization between two musical signals. In this transformation, the original signal assumes the tempo, meter,
and rhythmic structure of the model signal, while the extracted downbeats and salient intra-measure infrastructure of the original are maintained.