Automated Acoustic Identification of Bats
Abstract
This project developed a monitoring system to automatically and continuously monitor bats and birds for weeks to months by recording the vocalizations they produce. This project combined more than 10,000 sequences of species-known bat echolocation call recordings from 37 species in 30 states. High-resolution, full-spectrum data enabled an intelligent routine to automatically track call trends through noise and echoes and to extract and quantify subtle signal parameters, and enable the assessment of signal properties for quality control. The compiled known data supported the creation of an expert system to classify similarly parameterized unknown data. The expert classification of calls, and sequences of calls, uses an ensemble consensus of redundant hierarchical decision algorithms that reports a single species decision only when a result meets or exceeds an acceptance threshold at each decision step and satisfies redundant checks and signal assessments. Because of the greater number of bird species, their complexity, and variety of calls and songs, this project adopted an alternate approach to recognize target signals for bird signal recognition.
Document Details
- Document Type
- Technical Report
- Publication Date
- Oct 01, 2011
- Accession Number
- ADA600404
Entities
People
- Joseph M. Szewczak
- Michael L. Morrison
- Stuart Parsons
Organizations
- California State Polytechnic University, Humboldt