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Deep Learning based Automated Ultrasonic Vocalizations Scoring: DLAS

Award Information
Agency: Department of Defense
Branch: Defense Health Agency
Contract: W81XWH-17-C-0031
Agency Tracking Number: H16C-003-0055
Amount: $150,000.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: DHP16C-003
Solicitation Number: 2016.0
Timeline
Solicitation Year: 2017
Award Year: 2017
Award Start Date (Proposal Award Date): 2017-09-21
Award End Date (Contract End Date): 2018-04-28
Small Business Information
15400 Calhoun Drive
Rockville, MD 20855
United States
DUNS: 161911532
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Mr. Gang Mei
 Lead Research Scientist
 (301) 294-5257
 mgang@i-a-i.com
Business Contact
 Mr. Mark James
Phone: (301) 294-5221
Email: mjames@i-a-i.com
Research Institution
 University of Texas at Austin
 Christine Duvauchelle
 
110 Inner Campus Drive Array
Austin, TX 78705
United States

 (512) 471-1090
 Nonprofit college or university
Abstract

Ultrasonic vocalizations (USVs) provide an excellent behavioral measure that can be used to understand the effects of traumatic stressors on behavior and can be used to screen and identify therapeutic treatments. This provides a strong justification for the use of rodent USVs as a model for emotional processing in PTSD. Since there are no commercially available USV assessment software programs that reliably account for high variability in recording environments and signal-to-noise ratios across USV laboratories, manual analysis has remained the gold standard for accurate USV assessments.To address the critical need of an automated USV assessment program and the limitations of current solutions, Intelligent Automation, Inc. (IAI), along with our collaborator Prof. Christine Duvauchelle from the University of Texas at Austin (UT), proposes to develop a Deep Learning based Automated Ultrasonic Vocalizations Scoring program (DLAS). DLAS offers the key advantage that it is highly adaptive and flexible that it can be used for any user specified test environment and user defined set of USV classes, incorporated in DLAS profiles.

* Information listed above is at the time of submission. *

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