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### Browsing by Author "Moir, T.J."

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- ItemAnalysis of a non-minimum phase acoustic beamformer(Massey University, 2004) Moir, T.J.
Show more The two input Griffiths-Jim acoustic beamformer is analysed in the frequency domain using a Wiener type formulation. Unlike previous solutions the approach here is to look at the problem of non-minimum phase acoustic transfer functions which are encountered in many real filtering problems. The polynomial transfer function approach gives an elegant way of obtaining the frequency response of the beamformer and gives new insight to the problem in general.Show more - ItemAutomatic variance control and variance estimation loops(Massey University, 2000) Moir, T.J.
Show more A closed loop servo approach is applied to the problem of controlling and estimating variance in nonstationary signals. The new circuit closely resembles but is not the same as, automatic gain control (AGC) which is common in radio and other circuits. The closed loop nature of the solution to this problem makes this approach highly accurate and can be used recursively in real time.Show more - ItemAutomotive three-microphone voice activity detector and noise-canceller(Massey University, 2005) Qr, Z; Moir, T.J.
Show more This paper addresses issues in improving hands-free speech recognition performance in car environments. A three-microphone array has been used to form a beamformer with leastmean squares (LMS) to improve Signal to Noise Ratio (SNR). A three-microphone array has been paralleled to a Voice Activity Detection (VAD). The VAD uses time-delay estimation together with magnitude-squared coherence (MSC).Show more - ItemDiscrete-time variance tracking with application to speech processing(Massey University, 2001) Moir, T.J.
Show more Two new discrete-time algorithms are presented for tracking variance and reciprocal variance. The closed loop nature of the solutions to these problems makes this approach highly accurate and can be used recursively in real time. Since the Least-Mean Squares (LMS) method of parameter estimation requires an estimate of variance to compute the step size, this technique is well suited to applications such as speech processing and adaptive filtering.Show more - ItemA kepstrum approach to filtering, smoothing and prediction(Massey University, 2002) Moir, T.J.; Barrett, J.F.
Show more The kepstrum (or complex cepstrum) method is revisited and applied to the problem of spectral factorization where the spectrum is directly estimated from observations. The solution to this problem in turn leads to a new approach to optimal filtering, smoothing and prediction using the Wiener theory. Unlike previous approaches to adaptive and self-tuning filtering, the technique, when implemented, does not require a priori information on the type or order of the signal generating model. And unlike other approaches - with the exception of spectral subtraction - no state-space or polynomial model is necessary. In this first paper results are restricted to stationary signal and additive white noise.Show more - ItemKepstrum approach to real-time speech-enhancement methods using two microphones(Massey University, 2005) Jeong, J.; Moir, T.J.
Show more The objective of this paper is to provide improved real-time noise canceling performance by using kepstrum analysis. The method is applied to typically existing two-microphone approaches using modified adaptive noise canceling and speech beamforming methods. It will be shown that the kepstrum approach gives an improved effect for optimally enhancing a speech signal in the primary input when it is applied to the front-end of a beamformer or speech directivity system. As a result, enhanced performance in the form of an improved noise reduction ratio with highly reduced adaptive filter size can be achieved. Experiments according to 20cm broadside microphone configuration are implemented in real-time in a real environment, which is a typical indoor office with a moderate reverberation condition.Show more - ItemOn feed-through terms in the lms algorithm(Massey University, 2004) Moir, T.J.
Show more The well known least mean squares (LMS) algorithm is studied as a control system. When applied in a noise canceller a block diagram approach is used to show that the step size has two upper limits. One is the conventional limit beyond which instability results. The second limit shows that if the step size is chosen to be too large then feed-through terms consisting of signal times noise will result in an additive term at the noise canceller output. This second limit is smaller than the first and will cause distortion at the noise canceller output.Show more