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MESSAGE FOLLOWS:
Reminder: this seminar will take place tomorrow afternoon, Friday May 2nd.
Also a note about the timing: the two talks will take 1h30 altogether,
with each speaker taking 45 minutes including questions. There will be a
brief break between speakers.
On 22/04/2014 15:38, Jordan Smith wrote:
> 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
>
