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Poster
Online Learning of Time-Frequency Patterns
- Citation Author(s):
- Submitted by:
- JOSE RUIZ-MUNOZ
- Last updated:
- 1 March 2017 - 3:59pm
- Document Type:
- Poster
- Document Year:
- 2017
- Event:
- Presenters:
- Jose F. Ruiz-Munoz
- Paper Code:
- 2826
- Categories:
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We present an online method to learn recurring time-frequency patterns from spectrograms. Our method relies on a convolutive decomposition that estimates sequences of spectra into time-frequency patterns and their corresponding activation signals. This method processes one spectrogram at a time such that in comparison with a batch method, the computational cost is reduced proportionally to the number of considered spectrograms. We use a first-order stochastic gradient descent and show that a monotonically decreasing learning-rate works appropriately. Furthermore, we suggest a framework to classify spectrograms based on the estimated set of time-frequency patterns. Results, on a set of synthetically generated spectrograms and a real-world dataset, show that our method finds meaningful time-frequency patterns and that it is suitable to handle a large amount of data.
poster.pdf
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