BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//pretalx//cfp.scipy.org//2023//speaker//DALEAX
BEGIN:VTIMEZONE
TZID:America/Chicago
BEGIN:DAYLIGHT
DTSTART:20220712T000000
TZNAME:CDT
TZOFFSETFROM:-0500
TZOFFSETTO:-0500
END:DAYLIGHT
BEGIN:STANDARD
DTSTART:20221106T020000
RDATE:20231105T020000
TZNAME:CST
TZOFFSETFROM:-0500
TZOFFSETTO:-0600
END:STANDARD
BEGIN:DAYLIGHT
DTSTART:20230312T030000
RDATE:20240310T030000
TZNAME:CDT
TZOFFSETFROM:-0600
TZOFFSETTO:-0500
END:DAYLIGHT
END:VTIMEZONE
BEGIN:VEVENT
SUMMARY:vak: a neural network framework for researchers studying animal ac
 oustic communication - Yarden Cohen\, David Nicholson
DTSTART;TZID=America/Chicago:20230712T104500
DTEND;TZID=America/Chicago:20230712T111500
DTSTAMP:20261007T032739Z
UID:pretalx-2023-333PY7@cfp.scipy.org
DESCRIPTION:Research on animal acoustic communication is being revolutioni
 zed by deep learning. In this talk we present vak\, a framework that allow
 s researchers in this area to easily benchmark deep neural network models 
 and apply them to their own data. We'll demonstrate how research groups ar
 e using vak through examples with TweetyNet\, a model that automates annot
 ation of birdsong by segmenting spectrograms. Then we'll show how adopting
  Lightning as a backend in version 1.0 has allowed us to incorporate more 
 models and features\, building on the foundation we put in place with help
  from the scientific Python stack.
LOCATION:Grand Salon C
URL:https://cfp.scipy.org/2023/talk/333PY7/
END:VEVENT
END:VCALENDAR