** The Music and Science list is managed by the Institute of Musical Research (www.music.sas.ac.uk) as a bulletin board and discussion forum for researchers working at the shared boundaries of science and music. **
MESSAGE FOLLOWS:
Dear all,
On Friday, May 2nd at 2:00pm, Johanna Devaney and Michael Mandel, of
Ohio State University, will present two seminars back-to-back, entitled
"Analyzing recorded vocal performances" and "Strong models for
understanding sounds in mixtures", respectively, in ENG 2.09 (the
Engineering building) at Queen Mary University of London, Mile End Road,
London E1 4NS. Details of the talks follow.
Information on how to access the school can be found at
http://www.eecs.qmul.ac.uk/about/campus-map.php. If you are coming from
outside Queen Mary, please let me know, so that I can provide detailed
directions and make sure no-one is stuck outside the doors. If you wish
to be added to / removed from our mailing list as an individual
recipient, please send me an email and I'll be happy to do so.
**
Speaker 1: Johanna Devaney
Title: Analyzing Recorded Vocal Performances
Abstract:
A musical performance can convey both the musicians’ interpretation of
the written musical score as well as emphasize, or even manipulate, the
emotional content of the music through small variations in timing,
dynamics, tuning, and timbre. This talk presents my work on score-guided
automatic musical performance analysis, as well as my investigations
into vocal intonation practices. The score-audio alignment algorithm I
developed to estimate note locations makes use of a hybrid DTW-HMM
multi-pass approach that is able to capture onset and offset
asynchronies between simultaneously notated chords in polyphonic music.
My work on vocal intonation practices has examined both solo and
ensemble singing, with a particular focus on the role of musical
training, the presence and/or type of accompaniment, and the
organization of musical materials on intonation.
Bio:
Johanna Devaney is an assistant professor of music theory and cognition
at The Ohio State University. Her research applies a range of
interdisciplinary approaches to the study of musical performance,
motivated by a desire to understand how performers mediate listeners’
experience of music. Her work on extracting and analyzing performance
data, with a particular focus on intonation in the singing voice,
integrates the fields of music theory, music perception and cognition,
signal processing, and machine learning. She has released a number of
the tools she has developed in the open-source Automatic Music
Performance and Comparison Toolkit (www.ampact.org). Johanna completed
her PhD at the Schulich School of Music of McGill University. She also
holds an M.Phil. degree from Columbia University, as well as an MA from
York University in Toronto. Before working at Ohio State, she was a
postdoctoral scholar at the Center for New Music and Audio Technologies
(CNMAT) at the University of California, Berkeley.
**
Speaker 2: Michael Mandel
Title: Strong models for understanding sounds in mixtures
Abstract:
Human abilities to understand sounds in mixtures, for example, speech in
noise, far outstrip current automatic approaches, despite recent
technological breakthroughs. This talk presents two projects that use
strong models of speech to begin to close this gap and discusses their
implications for musical applications. The first project investigates
the human ability to understand speech in noise using a new data-driven
paradigm. By formulating intelligibility prediction as a classification
problem, the model is able to learn the important spectro-temporal
features of speech utterances from the results of listening test using
real speech. It is also able to successfully generalize to new
recordings of the same and similar words. The second project aims to
reconstruct damaged or obscured speech similarly to the way humans
might, by using a strong prior model. In this case, the prior model is a
full large vocabulary continuous speech recognizer. Posed as an
optimization problem, this system finds the latent clean speech features
that minimize a combination of the distance to the reliable regions of
the noisy observation and the negative log likelihood under the
recognizer. It reduces both speech recognition errors and the distance
between the estimated speech and the original clean speech.
Bio:
Michael I Mandel earned his BSc in Computer Science from the
Massachusetts Institute of Technology in 2004 and his MS and PhD with
distinction in Electrical Engineering from Columbia University in 2006
and 2010 as a Fu Foundation School of Engineering and Applied Sciences
Presidential Scholar. From 2009 to 2010 he was an FQRNT Postdoctoral
Research Fellow in the Machine Learning laboratory at the Université de
Montréal. From 2010 to 2012 he was an Algorithm Developer at Audience
Inc, a company that has shipped over 350 million noise suppression chips
for cell phones. He is currently a Research Scientist in Computer
Science and Engineering at the Ohio State University where he recently
received an Outstanding Undergraduate Research Mentor award. His
research applies signal processing and machine learning to computational
audition problems including source separation, robust speech
recognition, and music classification and tagging.
Other upcoming C4DM Seminars:
Richard Foss (Rhodes University), Thursday 1 May 2014, 2:00pm ("The
delights and dilemmas associated with sending audio over networks")
Matt McVicar (AIST Japan), Monday 12 May 2014, 3:30pm ("Towards the
automatic transcription of lyrics from audio")
Paul Weir (Aardvark Swift Recruitment, Audio director of Soho
Productions), Wednesday 21 May 2014, 3:00pm
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