# 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

**Hypothesis 1**

**(H1).**

**Hypothesis 2**

**(H2).**

#### 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

**23**to

**27**. Then, within those subbands the overlap of both signal level distributions (ANE and RTN) are lower, being the ANE class the one that attains higher signal levels (e.g., its 2D-PDF maximum probabilities, represented in yellow color, are placed closer to the optimum decision threshold). Another subband to analyze is number

**2**, which presents a lower value than subbands

**1**and

**3**in the analysis of both ANE and RTN. Subband

**2**corresponds to a central frequency of 153 Hz, which is one of the most common frequency ranges of road traffic noise [42,43], and yet in this location the curve of the threshold adapts to the best possible discrimination between ANE and RTN following Equations (2) and (3).

**27**and higher frequencies. In addition, differences between ANE and RTN are more evident in the lower frequency band (e.g., Mel subbands

**5**and

**6**). Subband

**2**in the urban environment follows the same performance as in the suburban, and the threshold is set to a lower value than the neighbour subbands in order to minimize the probability of error.

#### 4.2.2. Subband Error Probability Calculation

**23**, being 0.1673. The error probability exhibits also a frequency region with a second local minimum value of 0.17, around subband

**26**, and another third local minimum around subband

**18**. This leads us to conclude that in the suburban environment, the most suitable frequency region to take into account in terms of separability can be found within the mid-frequency range.

**5**and

**6**, presenting the minimum value (${p}_{e}^{ANE>RTN}\left(x\right)=0.31$ in the 6th subband, see Equation (1) for more details). Moreover, subband

**1**also yields a third local minimum of the computed error probability. This leads us to conclude that in the urban environment, the low-frequency region is the most suitable to discriminate between ANE and RTN.

## 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 $N/2$ 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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**Figure 1.**Scheme of an hybrid WASN architecture using Hi-Cap master sensor nodes and Lo-Cap slave sensor nodes, with a distributed intelligence.

**Figure 2.**Block diagram of the acoustic signal processing within the Lo-Cap acoustic sensor. The upper part details the block diagram of the ANED Lo-Cap, with a binary label as an output, and the lower branch details the evaluation of the ${L}_{Aeq}$ of the measured acoustic signal.

**Figure 3.**Block diagram of the process conducted to select a frequency range with minimum probability of error of classification.

**Figure 4.**2D-PDF of the ANE (

**left**) and the RTN (

**right**) of the suburban dataset. The frequency subband index i corresponding to the MFS is labelled in the x-axis (the reader can find the corresponding frequency in Table 1), while the corresponding logarithmic signal level at each subband is depicted in the y-axis in dBs. The Figures colormap is blue for lower probabilities while tends to warm colors (with the maximum in red) for higher probabilities. The solid white line represents the optimum decision threshold for the one-band linear discriminant classifier at each frequency bin.

**Figure 5.**2D-PDF of the ANE (

**left**) and the RTN (

**right**) of the urban dataset. The frequency subband index i corresponding to the MFS is labelled in the x-axis (the reader can find the corresponding frequency in Table 1), while the corresponding logarithmic signal level at each subband is depicted in the y-axis in dBs. The Figures colormap is blue for lower probabilities while tends to warm colors (with the maximum in red) for higher probabilities. The solid white line draws the optimum decision threshold for the one-band linear discriminant classifier at each frequency bin.

**Table 1.**Relation between the $M=48$ subbands used from the Mel filter-bank parameterization and its central frequency in Hz.

# 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 | $N\xb7lo{g}_{2}\left(N\right)$ | $\frac{N}{2}\xb7lo{g}_{2}\left(N\right)$ | $\frac{3}{2}N\xb7lo{g}_{2}\left(N\right)$ |

Subband filtering | $C-1$ | C | $2\xb7C-1$ |

**Table 3.**Price comparison for the hardware platforms described and algorithm they can assume real-time.

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 |

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

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

**AMA Style**

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 Style**

Alsina-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