Joint ECE/CSCVR Seminar*: Topic: Spatial Bioacoustics: Soundscape Analysis with a Co-located Microphone Array
When: Friday,
October 14, 2022
2:00 PM
-
4:00 PM
Where: > See description for location
Cost: Free
Description: Topic: Spatial Bioacoustics: Soundscape Analysis with a Co-located Microphone Array
Speaker: Irina Tolkova, Harvard University, Applied Math PhD Program
Location: Textiles 105
ZOOM Teleconference:
https://umassd.zoom.us/j/98538770144?pwd=dGZDc1VYdSt2V3RLa0Y4aTg2cjVLdz09
Abstract: The integration of passive acoustic sensors with machine learning enables large-scale, low-cost, non-invasive monitoring of vocal animals. A remaining challenge to the successful deployment of such monitoring systems is in maintaining high classification accuracy across complex real-world soundscapes composed of overlapping calls and variable noise patterns. In this talk, we present an algorithmic pipeline for using a terrestrial co-located microphone array to (1) estimate acoustic direction-of-arrival (DoA), (2) distinguish individual sound sources in the environment, and (3) approximate their spectrograms and time-domain signals. First, we evaluate the performance of multiple DoA estimation algorithms as well as multiple approaches for source separation. Next, we demonstrate the analysis pipeline for recordings collected at wildlife refuges during the dawn chorus in late spring, when birds are most vocally active. We significantly improve species-level performance metrics by applying source separation to the recordings prior to classification with the BirdNET network. This approach opens possibilities for additional
spatiotemporal analysis of soundscapes, including the ability to visualize movement and perform
directional filtering.
Biography: Irina Tolkova is completing a PhD in applied mathematics at Harvard University, with a particular interest in acoustic monitoring for wildlife conservation efforts. Her primary research focus is on utilizing sound directionality to improve automated species-level classification. In addition, she has worked on a range of projects spanning robotics, machine learning, signal processing, and quantitative ecology. Previously, Irina received bachelor's degrees in applied math and computer science from the University of Washington, Seattle. More details on Irina's work and hobbies can be found at https://avokloti.github.io/.
The Seminar is open to the public free of charge.
*For further information, please contact Dr. John Buck at 508.999.9237, or by via email at jbuck@umassd.edu.
Speaker: Irina Tolkova, Harvard University, Applied Math PhD Program
Location: Textiles 105
ZOOM Teleconference:
https://umassd.zoom.us/j/98538770144?pwd=dGZDc1VYdSt2V3RLa0Y4aTg2cjVLdz09
Abstract: The integration of passive acoustic sensors with machine learning enables large-scale, low-cost, non-invasive monitoring of vocal animals. A remaining challenge to the successful deployment of such monitoring systems is in maintaining high classification accuracy across complex real-world soundscapes composed of overlapping calls and variable noise patterns. In this talk, we present an algorithmic pipeline for using a terrestrial co-located microphone array to (1) estimate acoustic direction-of-arrival (DoA), (2) distinguish individual sound sources in the environment, and (3) approximate their spectrograms and time-domain signals. First, we evaluate the performance of multiple DoA estimation algorithms as well as multiple approaches for source separation. Next, we demonstrate the analysis pipeline for recordings collected at wildlife refuges during the dawn chorus in late spring, when birds are most vocally active. We significantly improve species-level performance metrics by applying source separation to the recordings prior to classification with the BirdNET network. This approach opens possibilities for additional
spatiotemporal analysis of soundscapes, including the ability to visualize movement and perform
directional filtering.
Biography: Irina Tolkova is completing a PhD in applied mathematics at Harvard University, with a particular interest in acoustic monitoring for wildlife conservation efforts. Her primary research focus is on utilizing sound directionality to improve automated species-level classification. In addition, she has worked on a range of projects spanning robotics, machine learning, signal processing, and quantitative ecology. Previously, Irina received bachelor's degrees in applied math and computer science from the University of Washington, Seattle. More details on Irina's work and hobbies can be found at https://avokloti.github.io/.
The Seminar is open to the public free of charge.
*For further information, please contact Dr. John Buck at 508.999.9237, or by via email at jbuck@umassd.edu.
Contact:
ECE: Electrical & Computer Engineering Department 508.999.9164 http://www.umassd.edu/engineering/ece/
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