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

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

[1]  Erica E. Ryherd,et al.  Speech intelligibility in hospitals. , 2012, The Journal of the Acoustical Society of America.

[2]  Marc Moonen,et al.  Distributed estimation and equalization of room acoustics in a wireless acoustic sensor network , 2012, 2012 Proceedings of the 20th European Signal Processing Conference (EUSIPCO).

[3]  Santiago Felici-Castell,et al.  Psychoacoustic Annoyance Implementation With Wireless Acoustic Sensor Networks for Monitoring in Smart Cities , 2020, IEEE Internet of Things Journal.

[4]  Angelo Farina,et al.  Simultaneous Measurement of Impulse Response and Distortion with a Swept-Sine Technique , 2000 .

[5]  S. Müller,et al.  Measuring impulse responses with digitally pre-emphasized pseudorandom noise derived from maximum-length sequences , 1995 .

[6]  M. Vorländer,et al.  Practical aspects of MLS measurements in building acoustics , 1997 .

[7]  Valtteri Hongisto,et al.  Experimental comparison between speech transmission index, rapid speech transmission index, and speech intelligibility index. , 2006, The Journal of the Acoustical Society of America.

[8]  Maximo Cobos,et al.  Enabling Real-Time Computation of Psycho-Acoustic Parameters in Acoustic Sensors Using Convolutional Neural Networks , 2020, IEEE Sensors Journal.

[9]  Janelle J. Harms,et al.  Distributed classification of acoustic targets in wireless audio-sensor networks , 2008, Comput. Networks.

[10]  Yu Hen Hu,et al.  Vehicle classification in distributed sensor networks , 2004, J. Parallel Distributed Comput..

[11]  Choi Ling Coriolanus Lam Improving the speech intelligibility in classrooms , 2010 .

[12]  M. Schroeder Integrated‐impulse method measuring sound decay without using impulses , 1979 .

[13]  R. McNeer,et al.  Factors Affecting Acoustics and Speech Intelligibility in the Operating Room: Size Matters , 2017, Anesthesia and analgesia.

[14]  Maximo Cobos,et al.  Cumulative-Sum-Based Localization of Sound Events in Low-Cost Wireless Acoustic Sensor Networks , 2014, IEEE/ACM Transactions on Audio, Speech, and Language Processing.

[15]  Guy-Bart Stan,et al.  Comparison of different impulse response measurement techniques , 2002 .