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Deep learning for music recommendation and generation

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If you have a question about this talk, please contact Michelle Sweering .

This is a joint talk with the Trinity College Science Society. Please note the unusual time.

The advent of deep learning has made it possible to extract high-level information from perceptual signals without having to specify manually and explicitly how to obtain it; instead, this can be learned from examples. This creates opportunities for automated content analysis of musical audio signals. In this talk, I will discuss how deep learning techniques can be used for audio-based music recommendation. I will also briefly discuss my ongoing work on music generation with WaveNet.

This talk is part of the Trinity Mathematical Society series.

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