title: Automated detection of 50-kHz ultrasonic vocalizations using template matching in XBAT.
abstract: BACKGROUND
  Ultrasonic vocalizations (USVs) have been utilized to infer animals' affective states in multiple research paradigms including animal models of drug abuse, depression, fear or anxiety disorders, Parkinson's disease, and in studying neural substrates of reward processing. Currently, the analysis of USV data is performed manually, and thus is time consuming.
  
  
  METHODS
  The goal of the present study was to develop a method for automated USV recognition using a 'template detection' procedure for vocalizations in the 50-kHz range (35-80kHz). The detector is designed to run within XBAT, a MATLAB graphical user interface and extensible bioacoustics tool developed at Cornell University.
  
  
  RESULTS
  Results show that this method is capable of detecting >90% of emitted USVs and that time spent analyzing data by experimenters is greatly reduced.
  
  
  METHODS
  Currently, no viable and publicly available methods exist for the automated detection of USVs. The present method, in combination with the XBAT environment is ideal for the USV community as it allows others to (1) detect USVs within a user-friendly environment, (2) make improvements to the detector and disseminate and (3) develop new tools for analysis within the MATLAB environment.
  
  
  CONCLUSIONS
  The present detector provides an open-source, accurate method for the detection of 50-kHz USVs. Ongoing research will extend the current method for use in the 22-kHz frequency range of ultrasonic vocalizations. Moreover, collaborative efforts among USV researchers may enhance the capabilities of the current detector via changes to the templates and the development of new programs for analysis.
authors.affiliation: Department of Psychology, Rutgers University, 152 Frelinghuysen Road, Piscataway, NJ 08854, USA; Neural Networks Section, Integrative Neuroscience Research Branch, National Institute on Drug Abuse, 251 Bayview Boulevard, Baltimore, MD, USA. Electronic address: David.Barker@nih.gov.
authors.forename: David J
authors.initials: DJ
authors.lastname: Barker
authors.affiliation: Department of Psychology, Rutgers University, 152 Frelinghuysen Road, Piscataway, NJ 08854, USA.
authors.forename: Christopher
authors.initials: C
authors.lastname: Herrera
authors.affiliation: Department of Psychology, Rutgers University, 152 Frelinghuysen Road, Piscataway, NJ 08854, USA.
authors.forename: Mark O
authors.initials: MO
authors.lastname: West
mesh_terms: D022126:Access to Information; D000818:Animals; D003042:Cocaine / Q000008:administration & dosage; D018765:Dopamine Uptake Inhibitors / Q000008:administration & dosage; D008297:Male; D010363:Pattern Recognition, Automated / Q000379:methods*; D011336:Probability; D020318:Rats, Long-Evans; D015203:Reproducibility of Results; D012646:Self Administration; D059629:Signal-To-Noise Ratio; D012984:Software; D013018:Sound Spectrography / Q000379:methods; D013997:Time Factors; D014465:Ultrasonics; D014584:User-Computer Interface; D014828:Vocalization, Animal
chemical_list: D018765:Dopamine Uptake Inhibitors; D003042:Cocaine
keywords: Affect; Automatic detection; MATLAB; Open-source; Ultrasonic vocalizations