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Enhanced WAAVES+: A Fast and Accurate Automated USV Scoring Program

Award Information
Agency: Department of Defense
Branch: Defense Health Program
Contract: W81XWH-17-C-0032
Agency Tracking Number: H16C-003-0011
Amount: $149,999.00
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): 2017-09-15
Award End Date (Contract End Date): 2018-04-14
Small Business Information
2750 Indian Ripple Road, Dayton, OH, 45440
DUNS: 130020209
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Mr. Kristopher Aber
 Team Leader
 (937) 320-1877
 aberkk@crgrp.com
Business Contact
 Ms. Chrysa Theodore
Phone: (937) 320-1877
Email: theodorecm@crgrp.com
Research Institution
 The University of Texas at Austin
 Courtney Frazier Swaney
 101 E. 27th Street, Stop A9000
Array
Austin, TX, 78712
 (512) 471-6424
 Nonprofit college or university
Abstract
Cornerstone Research Group (CRG) and the University of Texas (UT) will team to develop a fast and accurate automated analysis program for USV scoring. This automated tool will enable greater research efficiency and throughput allowing greater strides in developing treatments for post-traumatic stress disorder through rodent-based research. Building off prior work by UT on a first generation auto-scoring algorithm (WAAVES), CRG and UT will develop an improved program which addresses various shortcomings with the original. Instead of the narrowly constructed scoring ruleset used the original WAAVES program, CRG will develop an automated analysis code based on machine learning which will not only improve speed and accuracy but also broaden the capabilities of the USV scoring program to include more call types, additional animals including but not limited to mice, and support various recording environments. This will be accomplished by incorporating improved waveform analysis techniques; developing ab initio identification, grouping, and classification algorithms; and providing statistical feedback on the accuracy of the automatically scored calls.UT will provide input on the manual scoring rules needed for an expanded library of calls and provide feedback on the accuracy of the enhanced algorithm by comparing to hand scored data.

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

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