Bat Echolocation Scan Pattern Reconstruction using Convolutional Sparse Coding
- Resource Type
- Conference
- Authors
- Baeyens, Rens; Verreycken, Erik; Steckel, Jan; Daems, Walter
- Source
- 2024 IEEE Applied Sensing Conference (APSCON) Applied Sensing Conference (APSCON), 2024 IEEE. :1-4 Jan, 2024
- Subject
- Engineering Profession
General Topics for Engineers
Convolutional codes
Convolution
Acoustics
Encoding
Robustness
Microphone arrays
Sensors
Acoustic monitoring
Compressive sensing
Dictionary learning
Echolocation
Sparsity
- Language
Compressive sensing enables the detection and allocation of sparse signals at a sub-Nyquist sampling rate. For this reason, it is particularly interesting in the case of high sample rate applications. Monitoring bat echolocation signals using an ultrasonic microphone array is a high sample rate application. To evaluate the methods proposed in this work, Nyquist-compliant sample data is undersampled to simulate compressive sensing (CS) and reconstructed using convolutional sparse coding with a dictionary set trained on a bat’s echolocation calls. This paper evaluates the robustness of the proposed method for extracting key acoustic properties from bat echolocation signals. It compares these properties to those extracted with a Nyquist-compliant dataset that serves as a ground truth reference.