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The Cocktail Party Problem
AcknowledgementThe authors thank Krishna Puvvada for interesting discussions. This project was supported by NIH grant 1R01AG036424.
Forward Model:
Attention Model:
Inverse Model:
Spk2(Female)
Spk2
Spk1
Attended
Spk2 Attended
MEG
Spk1
MEG
(Male)Spk1
x 10-5
0 125 250 375 500-6-4-202468
50t (ms)
M50TRF
M100TRF
Sink Source
50ft/Step
0
1
Our Contribution:1) Parsimonious use of covariates2) High temporal resolusion
~ Seconds3) Scalability
Existing Techniques:1) Full spectrotemporal features as
covariates2) Low temporal Resolution
~ Minutes
Objectives
Convex, but highly non-linear and coupled in time.Efficient solution: two nested EM algorithms
*
**
****
***
Time (s)10 20 30 40 500
0.5
1
10 dB
60
10 20 30 40 50 60−5
0
5
10 20 30 40 50 60−5
0
5
10 20 30 40 50 600
0.5
1
(x 10 )−3
(x 10 )−3
Time (s)
Spk2 Attended Spk2 Attended
MEGSpk2
MEGSpk1
10 20 30 40 500
0.5
1
0 dB−10 dB−20 dB
60
References
Speech Segregation: Identifying and tracking a target speaker, corrupted by acoustic interference 1 .
Neural Activity at the Cortical level:Strongly modulated by low-frequency temporal modula-tions (envelope) of the attended target speaker 2 .
Magnetic field map of the auditory MEG component (DSS) 3
1 Cherry, E. C. (1953). Some experiments on the recognition of speech, with one and with two ears. The Journal of the aoustical society of America, 25(5), 975-979.
2 Ding, N., & Simon, J. Z. (2012). Emergence of neural encoding of auditory objects while listening to competing speakers. Proceedings of the National Academy of Sciences, 109(29), 11854-11859.
3 de Cheveigne, A., & Simon, J. Z. (2008). Denoising based on spatial filtering. Journal of neuroscience methods, 171(2), 331-339.
Speech envelope
Temporal receptive field
NoiseAuditory MEG
attending to Spk2attending to Spk1
MAP Estimate: Maximize
»
»
»»
Outer EM iteration :
E-Step: Compute *
M-Step: Update and ** Inner EM iteration : E-Step: Compute
***
M-Step: Update ****
end end
MEGSpk2
MEGSpk1
Spk2 Attended
Spk2 AttendedMEGSpk2
MEGSpk1
Time(s) Time(s)
MEGSpk2
MEGSpk1
MEGSpk2
MEGSpk1
Spk1 Attended
10 20 30 40 50 60−0.02
0
0.02
10 20 30 40 50 60−0.02
0
0.02
10 20 30 40 500
0.5
1
10 20 30 40 500
0.5
1
60
60
10 20 30 40 50 60−0.02
0
0.02
10 20 30 40 50 60−0.02
0
0.02
10 20 30 40 500
0.5
1
10 20 30 40 500
0.5
1
60
60
Sbj 1Sbj 2Classifier
Sbj 1Sbj 2Classifier
10 20 30 40 50 60−0.02
0
0.02
10 20 30 40 50 60−0.02
0
0.02
10 20 30 40 500
0.5
1
10 20 30 40 500
0.5
1
60
60
10 20 30 40 50 60−0.02
0
0.02
10 20 30 40 50 60−0.02
0
0.02
10 20 30 40 500
0.5
1
10 20 30 40 500
0.5
1
60
60
Sbj 1Sbj 2Classifier
Sbj 1Sbj 2Classifier
1
0
1
0Spk1 Attended
Time(s) Time(s)
Simulation Resaults
von Mises Distribution
The Inverse Solution1
0
1
0Spk1 Attended Spk1 Attended
1
0
1
0
The Inverse Problem: Estimating , given the observed data from trials.
Spk1 Attended
A Sate-Space Model for Decoding Auditory Attentional Modulation from MEG in a Competing-Speaker EnvironmentSahar Akram1, 2, Jonathan Z. Simon1, 2, 3, Shihab Shamma1, 2, Behtash Babadi1, 2
1Department of Electrical and Computer Engineering, 2 Institute for Systems Reasearch, 3 Department of Biology, University of Maryland, College Park, MDsakram@umd.edu, jzsimon@umd.edu, sas@umd.edu, behtash@umd.edu
Application to Real Data1
0
Spk2 Attended
0 \pi/2 \pi0
0.5
1
1.5
2
2.5
3
κ = 0.5κ = 1κ = 2κ = 4κ = 8
θ(radian)
“von
Mis
es”
dens
ity
π/2 π
The Proposed Model
Decoupled in time with tractable non-linear operations
Task Task
Task Task
Task Task
Task
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