| Paper: | MLSP-L2.6 | ||
| Session: | Blind Source Separation | ||
| Time: | Friday, May 21, 14:40 - 15:00 | ||
| Presentation: | Lecture | ||
| Topic: | Machine Learning for Signal Processing: Blind Signal Separation and Independent Component Analysis | ||
| Title: | UNSUPERVISED LEARNING OF SPARSE AND SHIFT-INVARIANT DECOMPOSITIONS OF POLYPHONIC MUSIC | ||
| Authors: | Thomas Blumensath; Queen Mary, University of London | ||
| Mike Davies; Queen Mary, University of London | |||
| Abstract: | Many time-series in engineering arise from a sparse mixture of individual components. Sparse coding can be used to decompose such signals into a set of functions. Most sparse coding algorithms divide the signal into blocks. The functions learned from those blocks are, however, not independent of the temporal alignment of the blocks. We present a fast algorithm for sparse coding that does not depend on the block location. To reduce the dimensionality of the problem, a subspace selection step is used during signal decomposition. Due to this reduction an Iterative Reweighted Least Squares method can be used for the constrained optimisation. We demonstrate the algorithm's abilities by learning functions from a polyphonic piano recording. The found functions represent individual notes and a sparse signal decomposition leads to a transcription of the piano signal. | ||
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