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Creating Dynamic Social Network Models from Sensor Data Tanzeem Choudhury Intel Research / Affiliate Faculty CSE Dieter Fox Henry Kautz CSE James Kitts Sociology
[object Object],[object Object],[object Object]
Social Network Analysis ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Social Networks ,[object Object]
Example Network Properties ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Applications ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
The Data Problem ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Social Network Analysis of Email ,[object Object]
Limits of E-Data ,[object Object],[object Object],[object Object],[object Object]
Research Goal ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Questions we want to investigate: ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Support ,[object Object],[object Object],[object Object],[object Object],[object Object]
Procedure ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Privacy ,[object Object],[object Object],[object Object],[object Object]
Data Collection ,[object Object],Real-time audio feature extraction audio features WiFi strength Coded Database code identifier
Data Collection ,[object Object],[object Object],[object Object],[object Object]
Older Procedure ,[object Object],[object Object],[object Object],[object Object]
Speech Detection ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Speech Production Fundamental frequency (F0/pitch) and formant frequencies (F1, F2 …) are the most important components for speech synthesis The source-filter Model vocal tract filter
Speech Production ,[object Object],[object Object],[object Object],[object Object],* 1. Donovan, R. (1996). Trainable Speech Synthesis. PhD Thesis. Cambridge University 2. O’Saughnessy, D. (1987). Speech Communication – Human and Machine, Addison-Wesley.
Goal: Reliably Detect Voiced Chunks in Audio Stream
Speech Features Computed ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Features used: Autocorrelation Autocorrelation of (a) un-voiced frame and (b) voiced frame. Voiced chunks have higher non-initial autocorrelation peak and fewer number of peaks (a) (b)
Features used: Spectral Entropy FFT magnitude of (a) un-voiced frame and (b) voiced frame. Voiced chunks have lower entropy than un-voiced chunks, because voiced chunks have more structure Spectral entropy: 3.74 Spectral entropy: 4.21
Features used: Energy Energy in voiced chunks is concentrated in the lower frequencies Higher order MEL cepstral coefficients contain pitch (F0) information. The lower order coefficients are NOT stored
Segmenting Speech Regions
Attributes Useful for Inferring Interaction ,[object Object],[object Object],[object Object],[object Object],[object Object]
Locations ,[object Object],[object Object],[object Object],[object Object]
Topological Location Map ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Social Network Model ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Non-instrumented Subjects ,[object Object],[object Object],[object Object],[object Object]
Disabling Sensor Units ,[object Object]
Access to the Data ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

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hsd-faculty-lunch-jan06

  • 1. Creating Dynamic Social Network Models from Sensor Data Tanzeem Choudhury Intel Research / Affiliate Faculty CSE Dieter Fox Henry Kautz CSE James Kitts Sociology
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  • 19. Speech Production Fundamental frequency (F0/pitch) and formant frequencies (F1, F2 …) are the most important components for speech synthesis The source-filter Model vocal tract filter
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  • 21. Goal: Reliably Detect Voiced Chunks in Audio Stream
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  • 23. Features used: Autocorrelation Autocorrelation of (a) un-voiced frame and (b) voiced frame. Voiced chunks have higher non-initial autocorrelation peak and fewer number of peaks (a) (b)
  • 24. Features used: Spectral Entropy FFT magnitude of (a) un-voiced frame and (b) voiced frame. Voiced chunks have lower entropy than un-voiced chunks, because voiced chunks have more structure Spectral entropy: 3.74 Spectral entropy: 4.21
  • 25. Features used: Energy Energy in voiced chunks is concentrated in the lower frequencies Higher order MEL cepstral coefficients contain pitch (F0) information. The lower order coefficients are NOT stored
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