Thesis Defense Schedule
PLEASE JOIN US AS THE FOLLOWING CANDIDATES PRESENT THEIR CULMINATING WORK.
Friday, June 7
Chair: Dr. Kaibao Nie
Candidate: Master of Science in Electrical Engineering
11:00 A.M.; DISC 464
Intelligent background sound event detection and classification based on WOLA spectral analysis in hearing devices
Audio signals from real-life hearing devices typically contain background noises. The purpose of this thesis is to build a system model which can automatically separate background noise from noisy speech, and then classify background sound into predefined event categories. This thesis proposed to use weighted overlap-add algorithm (WOLA) for feature extraction and feed-forward neural network for sound event detection. In this approach, an energy signal trough detection algorithm is used to separate out speech gaps which primarily contain background noise. To further analyze the noise signal’s spectrum, the WOLA algorithm is used to extract spectral features by transforming a fraction of time domain signal into frequency domain data represented in 22 channels. Moreover, a feed-forward neural network with one hidden layer is used to recognize each event’s diverse spectral feature pattern. It then produces classification decisions based on confidence values. Recordings of 11 realistic background noise scenes (cafe, station, hallway …), mixed with human speech at Signal to Noise Ratio (SNR) of 5 dB, are used for training. The neural network will learn the mapping between spectral feature characteristics and sound event categories. After training, the neural network classifier is evaluated by measuring the accuracy of event classification. The overall detection accuracy has achieved 96%, while the event ‘hallway’ has the lowest detection rate at 85%. This detection algorithm also has the ability for improving noise reduction in hearing devices by applying distinct compensation gains, which will attenuate the noise dominated frequency bands for each particular predefined event. In our preliminary evaluation experiment, the application of gain patterns has been proven to be effective in reducing background noise. Its combinational usage with instant gain pattern would produce improved results with noticeably attenuated noise and smooth spectral cues in the processed audio output.
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