## 4 Results and Analysis
### Node-Based Analysis
#### 4.1.3 The Quality of Recording Data  
Reduction of reverberation. The acoustical attenuation of the package and the reverberation inside the package are the dominating interference of recording. To avoid the negative influence on the sensor signal, we firm the acoustic sensor towards the outside of the box through the hole, which allows the acoustic sensor to capture directly the arriving signal, as can be seen in Figure 5(d). For the same sound source, we used a device with the sensor in a box and another device with the sensor towards the outside of the box to record it. As shown in Figure 6(a), there is reverberation during the time of 3.8-5.8 s, on the contrary, there is no reverberation in Figure 6(b). Obviously, the reverberation is effectively reduced by placing the sensor towards the outside of the box.  
Endpoint detection. The background noise in the real environment are inevitably recorded by sensor nodes. To reduce the amount of data, we adopt the method of endpoint detection to choose useful sounds to be recognized. Referencing [23; 24], we perform an endpoint detection algorithm based on the sub-band selection spectral variance on the embedded processing board. As shown in Figure 8, an audio data recorded by the system is divided into seven segments and four of them (2,4,6,7) are background noise using traditional endpoint detection. Significantly, it is divided into three segments and all of them are useful sounds using the improved endpoint detection, which presents better robustness under different SNR conditions.  
_Compression with ADPCM._ Compression reduces the capacity but also the quality of audio, so we compare the waveforms and spectrograms before and after compression to explore the influence of compression on the sound signal. The Adaptive Differential Pulse Code Modulation (ADPCM) is a compression algorithm for 16 bits of sound data. It transforms 16-bit to 4-bit per sampling for the compression ratio of 1:4, but reduces the precision of the signal. In other words, the waveform of the signal will become blurred or blurr (spiculate) but without distortion after compression, as can be seen in Figure 8(a),(b). However, the compression mainly affects the frequency range of 16-22 K, which has a small impact on feature extraction and classification, as can be seen in Figure 8(c),(d). We will show the recognition accuracy in the next chapter.  
Figure 6: Data obtained from the sound level meter and the sensor nodes in the field test. (**a**) In the school road (low Signal to Noise Ratio (SNR)); (**b**) in the school gate (high SNR).  
Figure 8: The validity of endpoint detection used in the system.  
Figure 7: The spectrogram of the sound. (**a**) The spectrogram of the sound we recorded by the sensor in a box; (**b**) the spectrum of the sound we recorded by the sensor towards the outside of the box.  
Figure 9: The influence of compression with the adaptive differential pulse code modulation (ADPCM). (**a**) The waveform of car horn before compression; (**b**) the waveform of car horn after compression; (**c**) the spectrograms of car horn before compression; (**d**) the spectrograms of car horn after compression.