Colloquium

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Revision as of 11:31, 14 January 2021 by Bnerness (Talk | contribs) (Winter Quarter (2021))

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@5:30pm in the Classroom on Wednesdays!

The CCRMA Colloquium is a weekly gathering of CCRMA students, faculty, staff, and guests. It is an opportunity for members of the CCRMA community and invited speakers to share the work that they are doing in the fields of Computer Music, Audio Signal Processing and Music Information Retrieval, Psychoacoustics, and related fields. The colloquium typically happens every Wednesday during the academic year from 5:30 - 7:00pm and meets in the CCRMA Classroom, Knoll 217, unless otherwise noted.

The colloquium team for 2020-2021 is:
Barbara Nerness - bnerness@ccrma.stanford.edu
Kunwoo Kim - kunwoo@ccrma.stanford.edu
Mike Mulshine - mrmulshine@ccrma.stanford.edu
Camille Noufi - cnoufi@ccrma.stanford.edu

  • Note: the colloquium will not be held every Wednesday this year (20-21), please keep an eye on the notification e-mails for the dates.

Winter Quarter (2021)

  • 1/13: Canceled
  • 1/20: Canceled (due to inauguration)
  • 1/27:
  • 2/03:
  • 2/10:
  • 2/17:
  • 2/24:
  • 3/03:
  • 3/10:
  • 3/17:
  • (Winter Quarter, Date TBD) Rapid-Fire Talks - sign up here via your CCRMA login.
    • Speaker 1: Kunwoo Kim
    • Speaker 2: Elena Georgieva
    • Speaker 3: Noah Fram
    • Speaker 4: Camille Noufi
    • Speaker 5: Barbara Nerness
    • Speaker 6:
    • Speaker 7:
    • Speaker 8:
    • Speaker 9:
    • Speaker 10:
    • Speaker 11:
    • Speaker 12:
    • Speaker 13:
    • Speaker 14:
    • Speaker 15:

Spring Quarter (2021)

Schedule TBD. Dates will be posted here as soon as they are planned.


Past - Autumn Quarter (2020)

In person colloquiua will not be held for the 2020 Autumn Quarter. All events will be held remotely.

  • 9/16 New Student Introductions
    • Speaker 1: Lloyd May
    • Speaker 2: Andrew Zhu
    • Speaker 3: Kathleen Yuan
    • Speaker 4: Marise van Zyl
    • Speaker 5: Hannah Choi
    • Speaker 6: Joss Saltzman
    • Speaker 7: Champ Darabundit
    • Speaker 8: Clara Allison
    • Speaker 9: David Braun
    • Speaker 10: Austin Zambito-Valente
  • 9/23 Faculty/Staff Introductions
    • Speaker 1: Jonathan Berger
    • Speaker 2: Ge Wang
    • Speaker 3: Takako Fujioka
    • Speaker 4: Seán O Dalaigh (new DMA)
    • Speaker 5: Eleanor Selfridge-Field
    • Speaker 6: Craig Stuart Sapp
    • Speaker 7: Blair Kaneshiro
  • 9/30 Faculty/Staff Introductions
    • Speaker 1: Patricia Alessandrini (via video)
    • Speaker 2: Julius Smith
    • Speaker 3: Marina Bosi
    • Speaker 4: Nando (aka Fernando Lopez-Lezcano)
    • Speaker 5: Stephanie Sherriff
    • Speaker 6: Constantin Basica
    • Speaker 7: Matt Wright
    • Speaker 8: Chris Chafe
  • 10/7 - Break
  • 10/14 - Town Hall
  • 10/21 - Adjunct Faculty Talks
    • Speaker 1: Malcolm Slaney
    • Speaker 2: Poppy Crum
    • Speaker 3: Paul Demarinis
    • Speaker 4: Jonathan Abel
    • Speaker 5: Doug James
  • 11/4 - Break
  • 11/18 - Mona Shahnavaz

ABSTRACT & BIO: Mona is an enthusiastic musician, whose focus and passion has been to share the joy of music with others. In 2018, a successful outcome of her innovative music program designed for senior citizens was the turning point for her to decide to change the course of learning piano in a less complex route. Her engineering background helped her to start working on the idea that bridges the gap between music and technology.

The approach to fingering in music has always been and still is one of the major elements of success for keyboard players. Correct fingering assists the performer in delivering a better technical and musical performance. This research presents the best technique to generate fingering for any sequence of music notes. Dynamic programming and mathematics are major parts of this paper, they work alongside rules set by pianists to calculate the most practical fingerings for any musical passage.

The ultimate goal is to facilitate the process of playing the piano using an AR platform. This is helpful for scaling music instructors and allows for efficient teaching. Through solving this problem, virtual instructions would be more productive and impactful. Success of this research applied in the AR field can be applied to robotic tasks in educational programs, video games, and medical fields.

  • 11/25 - THANKSGIVING WEEK - Break