Chairs
Professor Torsten Dau, Technical University of Denmark, Denmark
Professor Torsten Dau, Technical University of Denmark, Denmark
Torsten Dau was born in Hannover, Germany. He received the diploma degree in Physics at the University of Göttingen, Germany in 1992, and the Dr. rer. nat. degree in Physics at the University of Oldenburg (Germany) in 1996. From 1997-2002, Dr Dau was assistant professor (C1) at the Physics Department in Oldenburg. From 1999-2000, he was visiting scientist at the Department of Biomedical Engineering (BME) at Boston University and the Research Laboratory of Electronics (RLE) at Massachussets Institute of Technology (MIT), USA. In 2003, he received the habilitation degree (Dr. rer. nat. habil.) in Applied Physics at the University of Oldenburg. Since 2003, he has been professor for Acoustics and Audiology, head of the Centre for Applied Hearing Research (CAHR) and the Centre of Excellence for Hearing and Speech Sciences (CHeSS) as well as head of the Hearing Systems Group at the Technical University of Denmark (DTU). His main research interests are in the fields of auditory signal processing and perception, technical and clinical audiology, auditory neuroscience, neural modelling, and technical applications of auditory models in hearing technology. Dr Dau has published numerous journal and conference papers, edited ten books and organised various international conferences. He is fellow of the Acoustical Society of America and has been associate editor for several international journals.
14:00-14:20
The transformation from auditory to linguistic representations across auditory cortex is rapid and attention-dependent
Professor Jonathan Simon, University of Maryland, USA
Abstract
Professor Simon shows that magnetoencephalography (MEG) responses to continuous speech can be used to directly study lexical as well as acoustic processing. Source localised MEG responses to passages from narrated stories were modelled as linear responses to multiple simultaneous predictor variables, reflecting both acoustic and linguistic properties of the stimuli. Lexical variables were modelled as an impulse at each phoneme, with values based on the phoneme cohort model, including cohort size, phoneme surprisal and cohort entropy.
Results indicate significant left-lateralised effects of phoneme surprisal and cohort entropy. The response to phoneme surprisal, peaking at ~115 ms, arose from auditory cortex, whereas the response reflecting cohort entropy, peaking at ~125 ms, was more ventral, covering the superior temporal sulcus. These short latencies suggest that acoustic information is rapidly used to constrain the word currently being heard. This difference in localisation and timing are consistent with two stages during lexical processing, with phoneme surprisal being a local measure of how informative each phoneme is, and cohort entropy reflecting the state of lexical activation via lexical competition. An additional left-lateralised response to word onsets peaked at ~105 ms.
The effect of selective attention was also investigated using a two speaker mixture, one attended and one ignored. Responses reflect the acoustic properties of both speakers, but reflect lexical processing only for the attended speech. While previous research has shown that responses to semantic properties of words in unattended speech are suppressed, these results indicate that even processing of word forms is restricted to attended speech.
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Professor Jonathan Simon, University of Maryland, USA
Professor Jonathan Simon, University of Maryland, USA
Jonathan Simon earned his PhD in physics from the University of California, Santa Barbara, and did postdoctoral research in theoretical general relativity (University of Wisconsin, Milwaukee, and University of Maryland, College Park) before embracing the field of neuroscience. He joined the University of Maryland's Electrical & Computer Engineering Department in 2001, Biology Department in 2002, and Institute for Systems Research in 2013. Simon is also co-director of the KIT-Maryland Magnetoencephalography Center, and of the Computational Sensorimotor Systems Laboratory. His research focuses on neural processing in the auditory system, especially neural computations that use the temporal character of a sound. He has most recently focused on how the brain represents complex sounds such as speech, and how the brain solves the 'Cocktail Party' problem, ie, how, in a crowded and noisy environment, we can hone in on a single auditory source (eg one speaker), while simultaneously suppressing the other interfering sounds.
14:40-15:00
Bottom-up auditory attention using complex soundscapes
Professor Mounya Elhilali, Johns Hopkins University, USA
Abstract
Recent explorations of task-driven (top-down) attention in the auditory modality draw a picture of a dynamic system where attentional feedback modulates sensory encoding of sounds in the brain to facilitate detection of events of interest and ultimately perception especially in complex soundscapes. Complementing these processes are mechanisms of bottom-up attention that are dictated by acoustic salience of the scene itself but still engage a form of attentional feedback. Often, studies of auditory salience have relied on simplified or well-controlled auditory scenes to shed light on acoustic attributes that drive the salience of sound events. Unfortunately, the use of constrained stimuli in addition to a lack of well-established benchmarks of salience judgments hampers the development of comprehensive theories of bottom-up auditory attention. Here, Professor Elhilali will explore auditory salience in complex and natural scenes. She will discuss insights from behavioural, neural and computational explorations of bottom-up attention and their implications for our current understanding of auditory attention in the brain.
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Professor Mounya Elhilali, Johns Hopkins University, USA
Professor Mounya Elhilali, Johns Hopkins University, USA
Mounya Elhilali received her PhD in Electrical and Computer Engineering from the University of Maryland, College Park in 2004. She is now associate professor of Electrical and Computer Engineering at John Hopkins University and a Charles Renn faculty scholar. She directs the Laboratory for Computational Audio Perception and is affiliated with the Center for Speech and Language Processing. Her research examines sound processing in noisy soundscapes and reverse engineering intelligent processing of sounds by brain networks with applications to speech technologies, audio systems and medical diagnosis. Dr Elhilali is the recipient of the National Science Foundation CAREER award and the Office of Naval Research Young Investigator award.
16:00-16:20
Speaker-independent auditory attention decoding without access to clean speech sources
Professor Nima Mesgarani, Columbia University, USA
Abstract
Speech perception in crowded acoustic environments is particularly challenging for hearing impaired listeners. Assistive hearing devices can suppress background noises that are sufficiently different from speech; however, they cannot lower interfering speakers without knowing the speaker on which the listener is focusing. One possible solution to determine the listener’s focus is auditory attention decoding in which the brainwaves of listeners are compared with sound sources in an acoustic scene to determine the attended source, which can then be amplified to facilitate hearing. In this talk, Professor Mesgarani addresses a major obstacle in actualising this system, which is the lack of access to clean sound sources in realistic situations where only mixed audio is available. He proposes a novel speech separation algorithm to automatically separate speakers in mixed audio without any need for prior training on the speakers. The separated speakers are compared to evoked neural responses in the auditory cortex of the listener to determine and amplify the attended speaker. These results show that auditory attention decoding with automatically separated speakers is as accurate and fast as using clean speech sounds. Moreover, Professor Mesgarani demonstrates that the proposed method significantly improves both the subjective and objective quality of the attended speaker. By combining the latest advances in speech processing technologies and brain-computer interfaces, this study addresses a major obstacle in actualisation of auditory attention decoding that can assist individuals with hearing impairment and reduce the listening effort for normal hearing subjects in adverse acoustic environments.
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Professor Nima Mesgarani, Columbia University, USA
Professor Nima Mesgarani, Columbia University, USA
Nima Mesgarani is an associate professor at Zuckerman Mind Brain Behavior Institute at Columbia University. He received his PhD from the University of Maryland. He was a postdoctoral scholar in Center for Language and Speech Processing at Johns Hopkins University, and the neurosurgery department of University of California San Francisco before joining Columbia in 2013. He was named a Pew Scholar for Innovative Biomedical Research in 2015 and received the National Science Foundation Early Career Award in 2016.
16:40-17:00
On the encoding and decoding of natural auditory stimulus processing using EEG
Professor Ed Lalor, University of Rochester, USA and Trinity College Dublin, Ireland
Abstract
Over the past few years there has been a surge in efforts to model neurophysiological responses to natural sounds. This has included a variety of methods for decoding brain signals to say something about how a person is engaging with and perceiving the auditory world. In this talk Professor Lalor will discuss recent efforts to improve these decoding approaches and broaden their utility. In particular, he will focus on three related factors: 1) how we represent the sound stimulus, 2) what features of the data we focus on, and 3) how we model the relationship between stimulus and response. Professor Lalor will present data from several recent studies in which he has used different stimulus representations, different EEG features and different modelling approaches in an attempt to lead to more useful decoding models and more interpretable encoding models of brain responses to sound.
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Professor Ed Lalor, University of Rochester, USA and Trinity College Dublin, Ireland
Professor Ed Lalor, University of Rochester, USA and Trinity College Dublin, Ireland
Ed Lalor received the BE in electronic engineering from University College Dublin in 1998 and the MSc in electrical engineering from the University of Southern California in 1999. After working as a silicon design engineer and a primary school teacher for children with learning difficulties, Ed joined MIT's Media Lab Europe, where he investigated brain-computer interfacing and attention from 2002-2005. This led to a PhD in biomedical engineering from UCD in 2006. Subsequently, he spent two years as a postdoctoral researcher at the Nathan Kline Institute in New York and two years at Trinity College Dublin as a postdoctoral fellow. Following a brief stint at University College London, he returned to TCD as an Assistant Professor in 2011, where he established a group investigating human sensation, perception and cognition. In 2016 he joined the Departments of Biomedical Engineering and Neuroscience at the University of Rochester as an Associate Professor.
17:20-17:40
General discussion