# Dynamic Matching of Reconstruction and Anti-Aliasing Filters in Adaptive Active Noise Control
## 4 Experimental Validation  
### Experimental Setup  
A laboratory experimental system was set up in an anechoic chamber whose inside space had dimensions of 6.54 m \(\times\) 4.84 m \(\times\) 4.74 m. The free-field radius of the anechoic chamber was 1.1 m, and the background noise was 12.1 dBA, as shown in Figure 4. In the anechoic chamber, a loudspeaker serving as the primary sound source, a reference microphone, an error microphone, a monitoring microphone, and an active headrest were arranged. Figure 5 shows a flowchart of the experimental system. The embedded real-time processor for ANC was mainly composed of an XC7K325T FPGA produced by the Xilinx Company (San Jose, CA, USA) and a TMS320C6678 digital signal processor (DSP) produced by the TI company (Dallas, TX, USA). The FPGA was responsible for data acquisition and interface management, while the DSP performed algorithm calculations. The core processor of the embedded real-time processor for monitoring was an XC7Z020 system on chip (SOC). The type of analog-to-digital converter (ADC) used was AD7606B, and the digital-to-analog converter (DAC) used was LTC2755-16; the quantization bits of both the ADC and DAC chips were 16 bits. The conditioning circuits provided a constant current source of 4 mA for the microphones, the type of microphones was YG-201, and the type of speaker was Swan-B4N. The power amplifiers used were CA8000C models produced by Harman (Stamford, CT, USA), and the DSP debugger was TL-XDS560V2. All of the ANC signal-processing tasks were implemented with embedded real-time processors, and the computer was only used to control the working status of the DSP and to receive the network data from the monitoring microphone. The monitoring microphone, which was in close proximity to the error microphone, was only used to monitor the effect of noise reduction and did not participate in the control process. The embedded real-time processor for monitoring received the monitored microphone data from the ADC and then sent data to the computer through a network cable. In the experimental process, the high sampling rate was set to 40 kHz, and the low sampling rate was set to 5 kHz. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference** & **Secondary Sources** & **Error Microphones** & **Single-Rate Method** & **Bai’s Method** & **Dynamic Matching Method** \\ \hline
1 & 1 & 1 & 100\% & 112\% & 127\% \\
1 & 2 & 1 & 200\% & 216\% & 236\% \\
2 & 2 & 1 & 400\% & 420\% & 444\% \\
2 & 2 & 2 & 600\% & 624\% & 654\% \\
4 & 4 & 2 & 2400\% & 2440\% & 2490\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Average number of multiplication operations compared with the single-rate method. This experiment aimed to test the effect of the proposed method in improving the noise reduction performance by changing the direction of incoming waves from primary sound sources with different spectral structures. For convenience when comparing the noise reduction performance of the proposed method and other methods, the primary sound source was arranged at multiple test locations during the experiment, and the top view of the experimental system's layout is shown in Figure 6.  
The location of the error microphone was taken as the origin; the direction pointing towards the reference microphone was the y+ direction; the direction perpendicular to the y-axis and pointing to the right was the x+ direction. The reference microphone was mounted at the \((0,100)\) coordinates; \(d_{1}\) and \(d_{2}\) denote the distances from the primary sound source to the reference microphone and to the error microphone, respectively; and \(\theta\) denotes the angle between the straight line connecting the error microphone with the primary sound source and the y-axis. The noise reduction effect produced by the active headrest at the position of the monitoring microphone was measured for different locations and input signals of the primary sound source.