Speech Intelligibility Analysis and Approximation to Room Parameters through the Internet of Things

Abstract

In recent years, Wireless Acoustic Sensor Networks (WASN) have been widely applied to different acoustic fields in outdoor and indoor environments. Most of these applications are oriented to locate or identify sources and measure specific features of the environment involved. In this paper, we study the application of a WASN for room acoustic measurements. To evaluate the acoustic characteristics, a set of Raspberry Pi 3 (RPi) has been used. One is used to play different acoustic signals and four are used to record at different points in the room simultaneously. The signals are sent wirelessly to a computer connected to a server, where using MATLAB we calculate both the impulse response (IR), and different acoustic parameters, such as the Speech Intelligibility Index (SII). In this way, the evaluation of room acoustic parameters with asynchronous IR measurements two different applications has been explored. Finally, the network features have been evaluated to assess the effectiveness of this system.

Real-Time Sound Source Localization for Low-Power IoT Devices Based on Multi-Stream CNN

“…These studies demonstrated that the SSL based on DNNs significantly improved the accuracy and robustness of the model compared with traditional signal processing-based methods. Furthermore, [12] used a CNN to analyze the speech intelligibility, which shows the suitability of a CNN for processing acoustic data. The microphone array-based sound localization method can be implemented using an analytic sound source localization (SSL) algorithm such as in [4][5][6][7][8][9].…”

“…These studies demonstrated that the SSL based on DNNs significantly improved the accuracy and robustness of the model compared with traditional signal processing-based methods. Furthermore, [12] used a CNN to analyze the speech intelligibility, which shows the suitability of a CNN for processing acoustic data.…”