Use this URL to cite or link to this record in EThOS:
Title: Modelling of multi-channel audio signals
Author: Hicks, C. M.
Awarding Body: University of Cambridge
Current Institution: University of Cambridge
Date of Award: 1999
Availability of Full Text:
Full text unavailable from EThOS.
Please contact the current institution’s library for further details.
This dissertation is concerned with the mathematical modelling of musical and audio signals. The emphasis is on multi-channel signals where either more than one copy of a single original is available for analysis, or where the signal comprises two or more parts. The most common example of this latter class is stereo signals which comprise a left and a right signal to create an auditory illusion of space. Two models are analysed in which we have multiple observations of a single signal. Both are based on the well-known auto-regressive (AR) model which has previously been successfully deployed in many audio applications. The first of these is the Multiply-Observed AR Model in which a single AR signal is contaminated by a number of independent interference signals to give multiple noisy observations of the original. It is shown that the statistics of the noise sources can be determined given certain broad assumptions. The model is applied to the problem of broadband noise reduction of a 78 r.p.m. record, of which a number of copies are available. The second model is the Ensemble-AR Model in which an ensemble of excitation sources drive identical AR filters to give multiple observed signals. Methods for estimation of the AR parameters from the observed data are derived. The model is applied to the detection of impulsive noise in audio signals, and interpolation of the missing data. The E-AR model is demonstrated to be superior to a similar single-channel approach in both of these areas. There is such a variety of stereo signals in existence that a very general model is needed to encompass their whole spectrum. The Coupled-ARMA Model put forward here is based on the ARMA model, but generates a pair of interdependent signals. Its structure allows efficient estimation of its parameters, and various methods for this are examined. Interpolators for Coupled-ARMA signals are derived. For much multi-channel audio work it is necessary to ensure that the observed signals are accurately aligned with each other. Where multiple copies of a disc or tape are under examination this is a difficult problem, since even minute time offsets and speed fluctuations lead to effects such as time-varying comb-filtering when the signals are summed. We examine this problem in detail, and develop a robust scheme for resynchronising signals in a Bayesian statistical framework. Quantisation of audio signals has received much recent research effort. The final part of the dissertation presents a flexible model-based quantisation algorithm. The algorithm is demonstrated in the quantisation of narrow-band signals, and as a powerful enhancement to a simple linear prediction coding system.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available