Researcher and engineer (and Hackbright mentor!) Steve Tjoa spoke at Hackbright Academy about music information retrieval in Python on Tuesday, October 21, 2014 at Hackbright Academy in San Francisco.
Watch the full Hackbright Academy tech talk here:
The material used for the tech talk, including the IPython notebooks, is available on GitHub.
Slides from the tech talk are available in PDF format here.
About the tech talk: Music information retrieval (MIR) is an interdisciplinary field bridging the domains of statistics, signal processing, machine learning, musicology, biology, and more. MIR algorithms allow a computer to make sense of audio data in order to bridge the semantic gap between high-level musical information — e.g. tempo, key, pitch, instrumentation, chord progression, genre, song structure — and low-level audio data.
In this talk, Steve surveys common research problems in MIR, including music fingerprinting, transcription, classification, and recommendation, and recently proposed solutions in the research literature. The talk contains both a high-level overview as well as concrete examples of implementing MIR algorithms in Python using NumPy, SciPy, and the IPython notebook.
About the speaker: Steve Tjoa is a researcher and engineer in the areas of signal processing and machine learning for music information retrieval (MIR). He currently works on the MIR team at Humtap. Before that, he worked on content-based audio recognition and recommendation as an NSF-sponsored postdoctoral fellow at iZotope and Imagine Research (acquired by iZotope). After earning a PhD in electrical engineering from the University of Maryland in 2011, Steve has been the co-instructor for the annual summer workshop on MIR at Stanford University. Follow him on Twitter at @stevetjoa.
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