EVENT    SANE 2017 - Speech and Audio in the Northeast

Date released: October 19, 2017


  •  EVENT    SANE 2017 - Speech and Audio in the Northeast
  • Date:

    Thursday, October 19, 2017

  • Location:

    Google, New York, NY

  • Description:

    SANE 2017, a one-day event gathering researchers and students in speech and audio from the Northeast of the American continent, was held on Thursday October 19, 2017 at Google, in New York, NY. It broke the attendance record for a SANE event, with 180 participants.

    It was a follow-up to SANE 2012, held at Mitsubishi Electric Research Labs (MERL), SANE 2013, held at Columbia University, SANE 2014, held at MIT CSAIL, SANE 2015, (already!) held at Google NY, and SANE 2016, held at MIT's McGovern Institute for Brain Research. Since the first edition, the audience has steadily grown, gathering over 100 researchers and students in recent editions.

    As in 2013 and 2015, this year's SANE took place in conjunction with the WASPAA workshop, held October 15-18 in upstate New York. Many WASPAA attendees (around 70!) also attended SANE.

    SANE 2017 featured invited talks by seven leading researchers from the Northeast and beyond: Sacha Krstulović (Audio Analytic), Yusuf Aytar (Google DeepMind), Florian Metze (CMU), Gunnar Evermann (Apple), Eric Humphrey (Spotify), Aaron Courville (University of Montreal), Aäron van den Oord (Google DeepMind). It also featured a live demo session with presentations by Jonathan Le Roux (MERL), Dan Ellis (Google), Arlo Faria (Remeeting), Tatsuya Komatsu (NEC), and a lively poster session with 26 posters.

    SANE 2017 was co-organized by Jonathan Le Roux (MERL), Dan Ellis (Google), Michael I. Mandel (CUNY), Hank Liao (Google), and John R. Hershey (MERL). SANE remained a free event thanks to generous sponsorship by Google and MERL.

    Slides and videos of the talks are available from the SANE workshop website.

  • MERL Contact:

    Jonathan Le Roux

  • External Link:

    https://saneworkshop.org/sane2017/

  • Research Areas:

    Artificial Intelligence, Machine Learning, Speech & Audio