Digital techniques

The goal of a speech enhancement algorithm is to remove noise and recover the original signal with as little distortion and residual noise as possible. Most successful real-time algorithms thereof have done in the frequency domain where the frequency amplitude of clean speech is estimated per short-time frame of the noisy signal. The state of-the-art short-time spectral amplitude estimator algorithms estimate the clean spectral amplitude in terms of the power spectral density (PSD) function of the noisy signal. The PSD has to be computed from a large ensemble of signal realizations. However, in practice, it may only be estimated from a finite-length sample of a single realization of the signal. Estimation errors introduced by these limitations deviate the solution from the optimal. Various spectral estimation techniques, many with added spectral smoothing, have been investigated for decades to reduce the estimation errors. These algorithms do not address significantly issue on quality of speech as perceived by a human. This dissertation presents analysis and techniques that offer spectral refinements toward speech enhancement. We present an analytical framework of the effect of spectral estimate variance on the performance of speech enhancement. We use the variance quality factor (VQF) as a quantitative measure of estimated spectra. We show that reducing the spectral estimator VQF reduces significantly the VQF of the enhanced speech. The Autoregressive Multitaper (ARMT) spectral estimate is proposed as a low VQF spectral estimator for use in speech enhancement algorithms. An innovative method of incorporating a speech production model using multiband excitation is also presented as a technique to emphasize the harmonic components of the glottal speech input.

Microelectromechanical systems (MEMS) accelerometers and gyroscopes are small scale sensors that measure changes in linear acceleration and rotational velocity, respectively. They are fabricated using electronic circuit techniques such as etching and deposition. MEMS motion sensors can be used in an Inertial Measurement Unit (IMU) that can be integrated with the Global Positioning System (GPS) to make a navigation system that is more accurate than each system alone. However, since MEMS-based IMUs are inherently noisy, we must overcome inaccuracies caused by the integration of random noise to find position. Accuracy can be increased by applying digital filters to the data before integration. Comparing the success of finite impulse response (FIR) filters and infinite impulse response (IIR) filters, we found that even though our highest order FIR filter yielded the most accurate position, it was limited by an offset bias in the accelerometer signal and a time delay in the determined position.