Improving an Acoustic Vehicle Detector Using an Iterative Self-Supervision Procedure
Abstract
:1. Introduction
1.1. Context
1.2. Problem Statement, Challenges, and Solution Overview
1.3. Related Work
1.4. Outline
2. Materials and Methods
2.1. Measurements and Signature Identification
2.2. Reference Classifier and Canonical Supervised Learning
2.3. Semi-/Self-Supervised Learning
3. Results and Discussion
3.1. Self-Learning Validation
3.1.1. Recycle Method
3.1.2. Relabel Method
3.2. Extension to Unseen Data
3.2.1. Reference Classifier Training Dataset Retention
3.2.2. Pseudo-Labeled Background
3.2.3. Sensor Recalibration
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Date | Sensor | ||
---|---|---|---|
Training Data | Friday | 15 November 2019 | A |
Friday | 20 December 2019 | B | |
Thursday | 14 November 2019 | A | |
Friday | 3 January 2020 | B | |
Testing Data | Tuesday | 17 December 2019 | B |
Audio File | Pseudo-Label Source | ||
---|---|---|---|
Date (Sensor) | Recycle | Relabel | |
Dataset 1 | 15 November 2019 (A) | Reference Classifier | Reference Classifier |
Dataset 2 | 15 November 2019 (A) | Reference Classifier | Classifier 1 |
20 December 2019 (B) | Classifier 1 | ||
Dataset 3 | 15 November 2019 (A) | Reference Classifier | Classifier 2 |
20 December 2019 (B) | Classifier 1 | ||
14 November 2019 (A) | Classifier 2 | ||
Dataset 4 | 15 November 2019 (A) | Reference Classifier | Classifier 3 |
20 December 2019 (B) | Classifier 1 | ||
14 November 2019 (A) | Classifier 2 | ||
3 January 2020 (B) | Classifier 3 |
Probability of Overriding Trained Classifier Output | ||||||||
---|---|---|---|---|---|---|---|---|
0.00 | 0.05 | 0.10 | 0.15 | 0.20 | 0.25 | 0.50 | ||
AUROC | Dataset 1 | 0.6676 | 0.6422 | 0.6308 | 0.6002 | 0.5891 | 0.5559 | 0.4912 |
Dataset 2 | 0.7430 | 0.6602 | 0.5940 | 0.5574 | 0.4788 | 0.4844 | 0.3660 | |
Dataset 3 | 0.8447 | 0.8005 | 0.7120 | 0.7420 | 0.5961 | 0.6178 | 0.4843 | |
Dataset 4 | 0.8983 | 0.8789 | 0.8023 | 0.8185 | 0.7007 | 0.7304 | 0.5579 | |
Total Samples | Dataset 1 | 152 | 456 | 458 | 434 | 450 | 440 | 460 |
Dataset 2 | 590 | 672 | 816 | 880 | 824 | 822 | 898 | |
Dataset 3 | 906 | 1018 | 1.174 | 1218 | 1200 | 1194 | 1222 | |
Dataset 4 | 1160 | 1368 | 1508 | 1500 | 1472 | 1544 | 1500 |
Probability of Overriding Trained Classifier Output | ||||||||
---|---|---|---|---|---|---|---|---|
0.00 | 0.05 | 0.10 | 0.15 | 0.20 | 0.25 | 0.50 | ||
AUROC | Dataset 1 | 0.6676 | 0.6422 | 0.6308 | 0.6002 | 0.5891 | 0.5559 | 0.4912 |
Dataset 2 | 0.5893 | 0.7412 | 0.5489 | 0.7285 | 0.5163 | 0.5533 | 0.3581 | |
Dataset 3 | 0.7877 | 0.8267 | 0.8060 | 0.8611 | 0.8096 | 0.7848 | 0.4151 | |
Dataset 4 | 0.8815 | 0.8856 | 0.9165 | 0.9038 | 0.8669 | 0.8781 | 0.4257 | |
Total Samples | Dataset 1 | 152 | 456 | 458 | 434 | 450 | 440 | 460 |
Dataset 2 | 544 | 546 | 776 | 892 | 758 | 738 | 812 | |
Dataset 3 | 742 | 558 | 620 | 798 | 730 | 700 | 798 | |
Dataset 4 | 988 | 858 | 856 | 946 | 962 | 996 | 984 |
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Phathanapirom, B.; Hite, J.; Dayman, K.; Chichester, D.; Johnson, J. Improving an Acoustic Vehicle Detector Using an Iterative Self-Supervision Procedure. Data 2023, 8, 64. https://doi.org/10.3390/data8040064
Phathanapirom B, Hite J, Dayman K, Chichester D, Johnson J. Improving an Acoustic Vehicle Detector Using an Iterative Self-Supervision Procedure. Data. 2023; 8(4):64. https://doi.org/10.3390/data8040064
Chicago/Turabian StylePhathanapirom, Birdy, Jason Hite, Kenneth Dayman, David Chichester, and Jared Johnson. 2023. "Improving an Acoustic Vehicle Detector Using an Iterative Self-Supervision Procedure" Data 8, no. 4: 64. https://doi.org/10.3390/data8040064