Data-Dependent Feature Extraction Method Based on Non-Negative Matrix Factorization for Weakly Supervised Domestic Sound Event Detection
Abstract
:1. Introduction
2. Background
2.1. Problem Description
2.2. Non-Negative Matrix Factorization
3. Proposed System
3.1. Strategy for the Frequency Basis Learning
3.2. Iterative and Non-Iterative Feature Extraction Methods
3.3. Classifier
3.4. Post-Processing
4. Evaluation
4.1. Evaluation Settings
4.2. Comparison of Various Features
4.3. Effect of the Training Data on the Frequency Basis Learning
4.4. Thresholding Singular Values for Calculating the Pseudo-Inverse Matrix
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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w/o Event-Wise Post-Processing | w/ Event-Wise Post-Processing | |||
---|---|---|---|---|
F1-Score [%] (Micro) | F1-Score [%] (Macro) | F1-Score [%] (Micro) | F1-Score [%] (Macro) | |
NMF(iterative) | 35.06 | 31.58 | 40.12 | 39.23 |
NMF (non-iterative) | 34.87 | 30.16 | 40.02 | 38.45 |
MelSpec | 34.41 | 32.31 | 40.41 | 39.72 |
Log-Mel | 30.27 | 29.88 | 35.11 | 36.60 |
GAM | 32.15 | 33.09 | 37.23 | 39.81 |
CQT | 32.25 | 28.76 | 37.28 | 35.36 |
Cornell et al. [46] | - | - | (with own post-processing) | |
42.48 | 39.56 |
Electric Shaver | Speech | Dishes | Cat | Running Water | Dog | Frying | Blender | Alarm Bell | Vacuum Cleaner | |
---|---|---|---|---|---|---|---|---|---|---|
ine NMF (iterative) | 32.9 | 46.8 | 18.0 | 39.7 | 22.4 | 21.5 | 28.9 | 27.5 | 36.3 | 41.7 |
ine NMF (non-iterative) | 24.1 | 43.9 | 21.9 | 39.5 | 25.7 | 22.1 | 22.8 | 22.1 | 39.6 | 39.9 |
ine MelSpec | 35.7 | 45.0 | 18.0 | 39.1 | 30.9 | 17.4 | 24.0 | 32.0 | 32.0 | 49.0 |
ine Log-Mel | 38.3 | 37.3 | 14.2 | 36.7 | 27.4 | 13.7 | 23.5 | 23.0 | 35.8 | 49.0 |
ine GAM | 29.5 | 34.2 | 24.6 | 41.7 | 28.3 | 19.7 | 31.4 | 33.9 | 39.4 | 48.2 |
ine CQT | 34.6 | 46.7 | 18.4 | 35.9 | 21.7 | 16.5 | 9.1 | 24.8 | 24.1 | 55.6 |
NMF (Iterative) | NMF (Non-Iterative) | |||
---|---|---|---|---|
F1-Score [%] (Micro) | F1-Score [%] (Macro) | F1-Score [%] (Micro) | F1-Score [%] (Macro) | |
STR | 40.12 | 39.23 | 40.02 | 38.45 |
WEAK(U) | 40.02 | 38.89 | 38.98 | 37.65 |
WEAK | 41.53 | 38.28 | 39.01 | 37.76 |
STR + WEAK(U) | 38.91 | 37.50 | 38.39 | 38.79 |
STR + WEAK | 38.51 | 37.39 | 39.97 | 38.40 |
w/o Event-Wise Post-Processing | w/ Event-Wise Post-Processing | |||
---|---|---|---|---|
F1-Score [%] (Micro) | F1-Score [%] (Macro) | F1-Score [%] (Micro) | F1-Score [%] (Macro) | |
27.26 | 24.56 | 35.16 | 34.24 | |
33.59 | 29.95 | 39.59 | 37.94 | |
34.87 | 30.16 | 40.02 | 38.45 | |
32.52 | 28.35 | 38.51 | 36.23 | |
21.45 | 10.88 | 26.45 | 17.39 |
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Lee, S.; Kim, M.; Shin, S.; Park, S.; Jeong, Y. Data-Dependent Feature Extraction Method Based on Non-Negative Matrix Factorization for Weakly Supervised Domestic Sound Event Detection. Appl. Sci. 2021, 11, 1040. https://doi.org/10.3390/app11031040
Lee S, Kim M, Shin S, Park S, Jeong Y. Data-Dependent Feature Extraction Method Based on Non-Negative Matrix Factorization for Weakly Supervised Domestic Sound Event Detection. Applied Sciences. 2021; 11(3):1040. https://doi.org/10.3390/app11031040
Chicago/Turabian StyleLee, Seokjin, Minhan Kim, Seunghyeon Shin, Sooyoung Park, and Youngho Jeong. 2021. "Data-Dependent Feature Extraction Method Based on Non-Negative Matrix Factorization for Weakly Supervised Domestic Sound Event Detection" Applied Sciences 11, no. 3: 1040. https://doi.org/10.3390/app11031040
APA StyleLee, S., Kim, M., Shin, S., Park, S., & Jeong, Y. (2021). Data-Dependent Feature Extraction Method Based on Non-Negative Matrix Factorization for Weakly Supervised Domestic Sound Event Detection. Applied Sciences, 11(3), 1040. https://doi.org/10.3390/app11031040