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Fig. 2. Features explored for vehicle detection (a) sound waveform with labels (b) short time energy (c) logarithmic energy in dB (d) smoothed logarithmic energy in dB (ii) Logarithmic energy (iii) Snoothed logarithmic energy

Figure 2 Features explored for vehicle detection (a) sound waveform with labels (b) short time energy (c) logarithmic energy in dB (d) smoothed logarithmic energy in dB (ii) Logarithmic energy (iii) Snoothed logarithmic energy

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The vehicle sound recordings were manually labelled for identification of broad categories of vehicles like light vehicles, medium vehicles and heavy vehicles as shown in Fig. 1(a). The light vehicles included two and three wheelers. The medium type vehicles included cars, SUVs and light trucks and the heavy vehicles included buses and trucks. Discernable incidents like horn sound were also labelled. The noise of wind or accidental movement of audio recorder was not labelled as an incident. Labelling was also done with the specific class of vehicles as hep, mep and Ip for heavy, medium, and light vehicles respectively as shown in Fig. 1(b). Horns were labelled as hop. Fig. 1. Visualisation of collected vehicle sound data using wavesurfer showing (a)& (b) broad category labels (c) sound wave form (d) energy contour in dB (e) wideband spectrogram.
The vehicle sound recordings were manually labelled for identification of broad categories of vehicles like light vehicles, medium vehicles and heavy vehicles as shown in Fig. 1(a). The light vehicles included two and three wheelers. The medium type vehicles included cars, SUVs and light trucks and the heavy vehicles included buses and trucks. Discernable incidents like horn sound were also labelled. The noise of wind or accidental movement of audio recorder was not labelled as an incident. Labelling was also done with the specific class of vehicles as hep, mep and Ip for heavy, medium, and light vehicles respectively as shown in Fig. 1(b). Horns were labelled as hop. Fig. 1. Visualisation of collected vehicle sound data using wavesurfer showing (a)& (b) broad category labels (c) sound wave form (d) energy contour in dB (e) wideband spectrogram.
Fig. 3. Methodology for vehicle detection (a) sound waveform (b) short time energy (c) logarithmic energy in dB (d) smoothed logarithmic energy in dB with peaks marked (e) Detected peaks corresponding to vehicles. 1.Start with inspecting every 10 ms window of energy contour in dB, find peak for each window, and inspect the values of neighboring samples (previous 40" sample and next 40" sample in our studies). 2.If both neighbouring point have very low value compared to the peak then identify the considered peak as the potential peak, otherwise discard the peak. 3.Now move to next 10 ms of energy contour and repeat the procedure. 4.Plot the identified potential vehicle peaks.
Fig. 3. Methodology for vehicle detection (a) sound waveform (b) short time energy (c) logarithmic energy in dB (d) smoothed logarithmic energy in dB with peaks marked (e) Detected peaks corresponding to vehicles. 1.Start with inspecting every 10 ms window of energy contour in dB, find peak for each window, and inspect the values of neighboring samples (previous 40" sample and next 40" sample in our studies). 2.If both neighbouring point have very low value compared to the peak then identify the considered peak as the potential peak, otherwise discard the peak. 3.Now move to next 10 ms of energy contour and repeat the procedure. 4.Plot the identified potential vehicle peaks.
Table 2. Confusion Matrix for Vehicle Classes (Data set- No.1)
Table 2. Confusion Matrix for Vehicle Classes (Data set- No.1)
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