Detection of Anomalous Noise Events on Low-Capacity Acoustic Nodes for Dynamic Road Traffic Noise Mapping within an Hybrid WASN
Abstract
:1. Introduction
2. Related Work
2.1. Acoustic Event Detection in Urban Environments
2.2. Networks for Noise Monitoring
3. ANED Lo-Cap: An Anomalous Noise Event Detector for Low-Capacity Acoustic Sensors
3.1. General Description
3.2. Acoustic Signal Parameterization
3.3. Optimization of the ANED Lo-Cap Configuration
3.3.1. Computation of the Probability Error Function
3.3.2. Selection of Frequency Range
4. Experimental Section
4.1. Acoustic Database
4.2. 2D-PDF Subband Analysis and Selection
4.2.1. Computation of the PDFs for Both Scenarios
4.2.2. Subband Error Probability Calculation
5. Operations Cost Analysis of the ANED Lo-Cap
5.1. Audio Acquisition
5.2. Acoustic Signal Processing
- Windowing: In order to analyze short frames of audio, a window function should be applied to the input signal in order to reduce the spectral leakage due to higher frequencies. In this implementation, the hamming window is used [44]. The computational cost associated to any windowing process depends on the number of samples of the analyzed frame if the window function is computed and stored in advance. The ANED Lo-Cap proposal uses time frames 30 ms long, thus, if the sampling frequency is 48 kHz, the window will be 1440 samples long.
- FFT Computation: The FFT is one of the most popular algorithms that computes the DFT (Discrete Fourier Transform) of a sequence reducing its complexity by factorizing the DFT matrix. The most used algorithm is the Cooley-Turkey [41], that breaks the down the DFT of N points into smaller ones, typically dividing it in two pieces of at each step. The computational cost of the FFT may vary depending on N (the number of points of the FFT) and on the methodology of implementing the algorithm over a certain hardware platform and its optimization. In our case, the FFT shall be of minimum 1440 points and maybe of 2048 after adding zero-padding if the used algorithm requires a power-of-2 size.
- Sub-band Filtering: After the FFT is computed, a triangular-shaped filter is applied to a determined subband. The computational cost of obtaining each filtered subband depends on the number of coefficients (C) of the filter, which, in its turn, depends on the sampling frequency and the number of points of the FFT. The filter is used to obtain the energy of the subband, hence, the computational cost should consider the point-to-point multiplication of the vector and the filter and the posterior integration of the resulting vector. In order to reduce the computational cost, the filter may be designed in advance considering the sampling frequency and the number of points of the FFT. After that, only a product for each bin followed by a sum of all resulting outputs will be needed. The number of operations can be reduced if the filter is only employed in the concerning subbands and all other frequencies are omitted. In our case, two Mel subbands shall be implemented as it is the combination with a lower probability of error.
5.3. Commercial Board Comparison
6. Discussion
6.1. Classification Accuracy of ANED Lo-Cap vs. ANED Hi-Cap
6.2. ANED Lo-Cap and Network Homogeneity
6.3. Real-Time Implementation in a Low-Cost Platform
7. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
Abbreviations
ADC | Analog to Digital Converter |
AED | Acoustic Event Detection |
ARM | Advanced RISC Machine |
ANE | Anomalous Noise Event |
ANED | Anomalous Noise Event Detection |
CNOSSOS | Common Noise Assessment Methods |
CPU | Central Processing Unit |
DCT | Discrete Cosine Transform |
DFT | Discrete Fourier Transform |
DYNAMAP | DYNamic Acoustic MAPping |
EC | European Commission |
END | Environmental Noise Directive |
EU | European Union |
FFT | Fast Fourier Transform |
FPGA | Field-Programmable Gate Array |
GPU | Graphics Processing Unit |
HMM | Hidden Markov Models |
IIR | Infinite Impulse Response |
LDD | Low Level Descriptors |
MFCC | Mel-Frequency Cepstral Coefficients |
MFS | Mel Frequency Subband |
MSPS | Mega Samples per Second |
OCC | One-Class Classifier |
PWP | Perceptual Wavelet Packets |
RTN | Road Traffic Noise |
SNR | Signal to Noise Ratio |
SVM | Support Vector Machine |
UGMM | Universal GMM |
WASN | Wireless Acoustic Sensor Network |
ZCR | Zero Crossing Rate |
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# of Subband | Freq. (Hz) | # of Subband | Freq. (Hz) | # of Subband | Freq. (Hz) | # of Subband | Freq. (Hz) |
---|---|---|---|---|---|---|---|
1 | 86.7 | 13 | 886.7 | 25 | 2484.6 | 37 | 7231.5 |
2 | 153.3 | 14 | 953.3 | 26 | 2715.9 | 38 | 7904.8 |
3 | 220 | 15 | 1020 | 27 | 2968.8 | 39 | 8640.9 |
4 | 286.7 | 16 | 1115 | 28 | 3242.2 | 40 | 9445.4 |
5 | 353.3 | 17 | 1218.8 | 29 | 3547.4 | 41 | 10,324.9 |
6 | 420 | 18 | 1332.3 | 30 | 3877.7 | 42 | 11,286.3 |
7 | 486.7 | 19 | 1456.3 | 31 | 4238.7 | 43 | 12,337.2 |
8 | 553.3 | 20 | 1591.9 | 32 | 4633.4 | 44 | 13,485.9 |
9 | 620 | 21 | 1740.2 | 33 | 5064.9 | 45 | 14,741.6 |
10 | 686.7 | 22 | 1902.2 | 34 | 5536.5 | 46 | 16,114.3 |
11 | 753.3 | 23 | 2079.3 | 35 | 6052 | 47 | 17,614.7 |
12 | 820 | 24 | 2272.9 | 36 | 6615.5 | 48 | 19,254.8 |
Additions | Multiplications | Floating Point Operations | |
---|---|---|---|
Windowing | 0 | N | N |
FFT | |||
Subband filtering | C |
Hardware Platform | Base Price | Supported ANED Version |
---|---|---|
Arduino Uno R3 | from $20 | None |
Raspberry Pi Model A+ | from $20 | ANED Lo-Cap |
NXP Semiconductor FRDM-K66F Freedom Board | from $69 | ANED Lo-Cap & Hi-Cap |
Arty S7: Spartan-7 FPGA | from $109 | ANED Lo-Cap & Hi-Cap |
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Alsina-Pagès, R.M.; Alías, F.; Socoró, J.C.; Orga, F. Detection of Anomalous Noise Events on Low-Capacity Acoustic Nodes for Dynamic Road Traffic Noise Mapping within an Hybrid WASN. Sensors 2018, 18, 1272. https://doi.org/10.3390/s18041272
Alsina-Pagès RM, Alías F, Socoró JC, Orga F. Detection of Anomalous Noise Events on Low-Capacity Acoustic Nodes for Dynamic Road Traffic Noise Mapping within an Hybrid WASN. Sensors. 2018; 18(4):1272. https://doi.org/10.3390/s18041272
Chicago/Turabian StyleAlsina-Pagès, Rosa Ma, Francesc Alías, Joan Claudi Socoró, and Ferran Orga. 2018. "Detection of Anomalous Noise Events on Low-Capacity Acoustic Nodes for Dynamic Road Traffic Noise Mapping within an Hybrid WASN" Sensors 18, no. 4: 1272. https://doi.org/10.3390/s18041272
APA StyleAlsina-Pagès, R. M., Alías, F., Socoró, J. C., & Orga, F. (2018). Detection of Anomalous Noise Events on Low-Capacity Acoustic Nodes for Dynamic Road Traffic Noise Mapping within an Hybrid WASN. Sensors, 18(4), 1272. https://doi.org/10.3390/s18041272