Distinguished Speaker Coming to Innovation Place

Michelle Thompson will be giving two presentations on June 17 at Innovation Place and the University of Saskatchewan.

Michelle Thompson has an MSEE in Information Theory, professional experience in Globalstar, founded the Open Research Institute, and has contributed within multiple amateur radio payload projects since 2008. Michelle also has experience with machine learning and embedded programming, and is a practicing amateur musician.

 

AN OPEN SOURCE PROTOCOL FOR AMATEUR RADIO SATELLITES

AMSAT stands for the Radio Amateur Satellite Corporation. After 50 years of steady success in launching and operating over 85 amateur radio satellites, AMSAT is taking a big step forward into microwave digital payloads at 5 and 10 GHz. Because of the increased complexity of digital microwave satellites, new radios must be developed for the ground. Phase 4 Ground is an all-volunteer open-source effort to bring a world-class DVB-protocol-based radio to life. Radios will range from do-it-yourself bespoke rigs to manufactured solutions. There are enormous and rewarding challenges and a wide variety of constraints and considerations.


This is a lunch and learn event, and lunch is included in the registration cost.

Visitor Parking is available at meters throughout Innovation PlaceParking Permits for the lots near the Atrium are available for $9 plus tax.  Contact Boian Berberov by 2019-06-12 to reserve a parking permit.


Date and Time: June 17, 2019  11:30am to 1:30pm
Location: The Atrium, 111 Research Drive, Candle / Span Room
Contact: Boian Berberov

Registration

  • Starts 06 June 2019 09:00 AM
  • Ends 12 June 2019 11:55 PM
  • Admission fee
  • Register Now

Agenda

  • 11:30am - Sign-in and Lunch
  • 12:00pm - Presentation and Questions

ALGORITHMIC MUSIC COMPOSITION

 

Algorithmic composition of music is a sweeping intersection between mathematics, information theory, aesthetics, and artistry. Attempts to artificially create composed music that passes human muster has a long history. Current efforts in algorithmic composition include machine learning, deep learning, Markov chains, and biomimicry. Artificially intelligent music composition is not yet fully realized, but algorithms that assist human composers exist and are here to stay. The choices we make in how to represent and model music is of critical importance.

Artificial intelligence is only as good as the input data.

  • What is it about music that makes it particularly challenging as a target of machine learning? Can music be treated simply as a time series?
  • What are the many areas of music practice that machine learning can address?
  • What might a computerized intelligent agent do for a busy composer?
  • Can and should machine learning archive the sound of particular artists, so that their mastery can be modeled and synthesized on demand for all time?
  • Are there hidden structures in sound that provide a mathematical theory of aesthetics?
  • What are the ethics and repercussions of replacing composers?

Visitor Parking is available at meters and lots near the Engineering Building.  Parking is administered by the University of Saskatchewan.  See this page for more information.


Date and Time: June 17, 2019  3:30pm - 5:00pm
Location: Room 2C40 Engineering Building, 57 Campus Drive (Click here for Map)
Contact: Boian Berberov

Registration

  • Starts 06 June 2019 09:00 AM
  • Ends 14 June 2019 11:55 PM
  • All times are Canada/Saskatchewan
  • No Admission Charge
  • Register Now

Agenda

  • 3:30pm - Sign-in
  • 3:40pm - Presentation and Questions