Cough sound discrimination in noisy and reverberant environments using microphone arrays

Cough sound discriminator algorithms are capable of distinguishing between dry and wet cough types. The performance of such algorithms, however, is affected by noise and reverberation which might exist in patients' environments. In this thesis, the performance of the previously developed cough sound discriminator in a noisy and reverberant room is quantitatively measured using Linear Separation Score. Experiments revealed a significant decrease in the performance of the cough sound discriminator in the presence of noise and reverberation using a single microphone for the cough sound acquisition. In order to improve this performance, a microphone array structure which included a maximum of 7 microphones was designed with a delay-and-sum beamformer. Experiments showed a significant improvement in the performance of the cough sound discriminator using a microphone array in noisy and reverberant environments. Finally, a Graphical User Interface was developed in order to visualize the beampattem emitted by the microphone array structure.