Development of a Real Time Hearing Enhancement Algorithm for Crowded Social Environments
Brian Wang · TSpace (University of Toronto) · 2014
A novel hearing enhancement algorithm was developed particularly for real time processing within crowded social environments. Binaural microphone array steering provides initial estimation of sound source location. Parallel noise gating is then applied to reduce crowd noise. This system is adaptive to environmental changes due to an adaptive noise bank, noise identification and selection utilizes Amplitude Modulation pitch tracking as well as pitch and formant continuity checks. The algorithm has been verified through both signal processing metrics as well as a formal listening experiment consisting of 10 subjects. It can achieve up to 10 dB Signal to Noise Ratio improvement on a near-zero dB SNR sound segment containing a target speaker immersed in crowd noise. Signal distortions as well as digital artifacts are significantly reduced with our method, improving target speech intelligibility. The listening experiment demonstrates a 39% accuracy increase in target speech recognition over the original noised segment, and a 31% accuracy increase over conventional binary masking noise reduction method. The algorithm has a computational speed that allows for real time processing.