# Speech Intelligibility Analysis and Approximation to Room Parameters through the Internet of Things
## 2 Materials and Methods  
To develop the aforementioned applications, we have used a WASN with 5 nodes based on the Raspberry Pi 3b (RPi) board, 4 for registration and one for emission, according to Figure 2. This platform is based on the Broadcom BCM2837 system on chip, which includes a 1.2 GHz quad-core ARM Cortex-A53 processor, with 512 KB of shared L2 cache, a VideoCore IV graphics processing unit (GPU) at 400 MHz clock frequency, with built-in WiFi (IEEE 802.11 b/g/n standard) and Bluetooth and 1 GB of LPDDR2 RAM at 900 MHz, with a micro-SD card memory. We installed in the measurement nodes an Andoer B01 LCIGY8U-USB condenser microphone, with omnidirectional reception pattern, sensitivity = \(-30\pm 3\) dB, SNR > 36 dB and linear frequency response between 20 Hz and 16 KHz. The set configuration allows each node to be prepared to automatically acquire a configurable audio time with 16 bits per sample (allowing a dynamic range of \(20\cdot log_{10}(2^{16})=96.33\) dB) at a sampling frequency of 44.1 kHz. The RPi have been placed in protective structures, each with 3.7 V and 3800 mAh batteries. Measurements have provided us with results of approximately 10 h, more than enough time to perform the analysis, since it is done in minutes. It is very useful to have batteries, since in the case of SII, we can place the nodes in different positions and calculate intelligibility values in more than 4 locations, due to the fact that measurements do not have to be simultaneous. Thus, the sound picked up by the microphone of each node is used to calculate independently the acoustic parameters of the room (focusing on the SII), so it is not necessary in this case to make any data fusion. Figure 3 shows the classroom prepared for measurement. As shown in Figure 2, the measurement system comprises the registration nodes and the broadcast nodes configured,  
Figure 2: Diagram of the IoT architecture for the room acoustics parameter measurement system. with a NTP server configured to have the same time in all the nodes, from a central computer, which acts as a server. The synchronizing order is sent to the player node (with loudspeaker) to start playing the excitation signal and to the recorder nodes to start recording at the same time. In the case of playback, it is possible to play both a frequency sweep that will allow the extraction of the IR of the room and subsequent calculation of different parameters, and different speech signals, which will allow the direct calculation of the SII at the position of each node. Once the recording is finished, the audios are sent to the central equipment which is in charge of storing them along with the positions of each node and making the pertinent computations. Audios from 10 s to 1 min duration are used for frequency sweeps, but excellent results have been obtained for SII analysis with 10-s audios and even with 5-s audios of constant speech. The signals used to extract the average impulse response are frequency sweeps from 20 to 20 KHz of 5 and 10 s duration. The human speech signals have been previously recorded in an anechoic environment and belong to 6 professors of the department, 3 male and 3 female in order to have a diverse set of voices. The software has been designed and tested initially with software simulations of classrooms and rooms of different geometries. Currently, the acquisition and calculation system is operational, and has been tested in different classrooms of the ETSE of the University of Valencia, taking advantage of their adaptation to the interpersonal distance required for teaching. The parameters evaluated are shown in Table 1. We can calculate only one (SII) from a speech signal alone. Therefore, we will focus the presentation of results on this parameter. The study of SII can give us a clear idea of how the position of the source, distance, geometry and classroom materials affect the intelligibility of the word.