5 research outputs found
System Identification with Applications in Speech Enhancement
As the increasing popularity of integrating hands-free telephony on mobile portable devices
and the rapid development of voice over internet protocol, identification of acoustic
systems has become desirable for compensating distortions introduced to speech signals
during transmission, and hence enhancing the speech quality. The objective of this research
is to develop system identification algorithms for speech enhancement applications
including network echo cancellation and speech dereverberation.
A supervised adaptive algorithm for sparse system identification is developed for
network echo cancellation. Based on the framework of selective-tap updating scheme
on the normalized least mean squares algorithm, the MMax and sparse partial update
tap-selection strategies are exploited in the frequency domain to achieve fast convergence
performance with low computational complexity. Through demonstrating how
the sparseness of the network impulse response varies in the transformed domain, the
multidelay filtering structure is incorporated to reduce the algorithmic delay.
Blind identification of SIMO acoustic systems for speech dereverberation in the
presence of common zeros is then investigated. First, the problem of common zeros is
defined and extended to include the presence of near-common zeros. Two clustering algorithms
are developed to quantify the number of these zeros so as to facilitate the study
of their effect on blind system identification and speech dereverberation. To mitigate such
effect, two algorithms are developed where the two-stage algorithm based on channel
decomposition identifies common and non-common zeros sequentially; and the forced
spectral diversity approach combines spectral shaping filters and channel undermodelling
for deriving a modified system that leads to an improved dereverberation performance.
Additionally, a solution to the scale factor ambiguity problem in subband-based blind system identification is developed, which motivates further research on subbandbased
dereverberation techniques. Comprehensive simulations and discussions demonstrate
the effectiveness of the aforementioned algorithms. A discussion on possible directions
of prospective research on system identification techniques concludes this thesis
Inferring Room Geometries
Determining the geometry of an acoustic enclosure using microphone arrays
has become an active area of research. Knowledge gained about the acoustic
environment, such as the location of reflectors, can be advantageous for
applications such as sound source localization, dereverberation and adaptive
echo cancellation by assisting in tracking environment changes and helping
the initialization of such algorithms.
A methodology to blindly infer the geometry of an acoustic enclosure by estimating
the location of reflective surfaces based on acoustic measurements
using an arbitrary array geometry is developed and analyzed. The starting
point of this work considers a geometric constraint, valid both in two
and three-dimensions, that converts time-of-arrival and time-difference-pf-arrival information into elliptical constraints about the location of reflectors.
Multiple constraints are combined to yield the line or plane parameters of
the reflectors by minimizing a specific cost function in the least-squares
sense. An iterative constrained least-squares estimator, along with a closed-form estimator, that performs optimally in a noise-free scenario, solve the
associated common tangent estimation problem that arises from the geometric
constraint. Additionally, a Hough transform based data fusion and
estimation technique, that considers acquisitions from multiple source positions,
refines the reflector localization even in adverse conditions.
An extension to the geometric inference framework, that includes the estimation
of the actual speed of sound to improve the accuracy under temperature
variations, is presented that also reduces the required prior information
needed such that only relative microphone positions in the array are
required for the localization of acoustic reflectors. Simulated and real-world
experiments demonstrate the feasibility of the proposed method.Open Acces