Building an acoustical-environment monitoring system

The purpose of this project is to design and develop an acoustical-environment monitoring system. The prototype of this system is able to monitor ambient noise level, determine the impulse response and reverberation time of a room, calculate the speech transmission index (STI), and act as a sound masking system. The method used to achieve these goals is to continually monitor the amplitude of incoming sound, while also radiating low-level maximum length sequence (MLS) sound signals. The MLS signal received by microphones is then cross-correlated with the output signal in order to determine the impulse response of the room. The STI is determined with knowledge of the ambient noise level, impulse response, and a user defined speech level. To justify continuously emitting pseudorandom noise, the system also provides a sound masking function by filtering the emitted MLS to match the characteristics of masking sound. In doing so, the device meets the demand for privacy in buildings that lack a Heating, Ventilation, and Air Conditioning (HVAC)system such as newly developed sustainable buildings. The prototype consists of a laptop, running MATLAB script, as well as the necessary peripherals such as microphones and loud speakers. A series of programs were written to monitor sound level, emit and filter MLS signals, measure the room response, calculate the STI,and determine the reverberation time for eight different octave bands. An A-weighting filter was also designed to adjust the measured ambient noise level in order to correspond to human hearing. Our group has begun to lay the foundation for the project; however, there are still improvements to be made. The prototype’s sound level measurement has been verified by a commercial sound level meter; however, due to time constraints, there has been a limited amount of testing for the impulse response, STI, and reverberation time measurements. It is recommended that future groups work to improve the timing of the impulse response function, perhaps by using a language or data-acquisition method that would allow for time-stamping the input and output signals, and run further testing. It is also recommended that future groups further test the quality of the sound masking system and rigorously determine the best MLS filter required for the task. It had also been found that MATLAB's built in function "xcorr" ran faster than the originally planned hadamard transform method [2] of cross-correlation. It is recommended that future groups look into this part of the code specifically. The hadamard transform method may have been necessary in the past, as [2] had been published in 1983 and assumes 1 microsecond per calculation, but there are possible alternatives including "xcorr" and "cconv" functions in MATLAB. It is suspected that further optimization of the code would result from examining this issue. Of course, any further optimization by future groups would be beneficial to the project as well.Science, Faculty ofPhysics and Astronomy, Department ofUnreviewedUndergraduat