29 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
Sparseness-controlled adaptive algorithms for supervised and unsupervised system identification
In single-channel hands-free telephony, the acoustic coupling between the loudspeaker and
the microphone can be strong and this generates echoes that can degrade user experience.
Therefore, effective acoustic echo cancellation (AEC) is necessary to maintain a stable
system and hence improve the perceived voice quality of a call. Traditionally, adaptive
filters have been deployed in acoustic echo cancellers to estimate the acoustic impulse
responses (AIRs) using adaptive algorithms. The performances of a range of well-known
algorithms are studied in the context of both AEC and network echo cancellation (NEC).
It presents insights into their tracking performances under both time-invariant and time-varying
system conditions.
In the context of AEC, the level of sparseness in AIRs can vary greatly in a mobile
environment. When the response is strongly sparse, convergence of conventional
approaches is poor. Drawing on techniques originally developed for NEC, a class of time-domain
and a frequency-domain AEC algorithms are proposed that can not only work
well in both sparse and dispersive circumstances, but also adapt dynamically to the level
of sparseness using a new sparseness-controlled approach.
As it will be shown later that the early part of the acoustic echo path is sparse
while the late reverberant part of the acoustic path is dispersive, a novel approach to
an adaptive filter structure that consists of two time-domain partition blocks is proposed
such that different adaptive algorithms can be used for each part. By properly controlling
the mixing parameter for the partitioned blocks separately, where the block lengths are
controlled adaptively, the proposed partitioned block algorithm works well in both sparse
and dispersive time-varying circumstances.
A new insight into an analysis on the tracking performance of improved proportionate
NLMS (IPNLMS) is presented by deriving the expression for the mean-square error.
By employing the framework for both sparse and dispersive time-varying echo paths, this
work validates the analytic results in practical simulations for AEC.
The time-domain second-order statistic based blind SIMO identification algorithms,
which exploit the cross relation method, are investigated and then a technique with proportionate
step-size control for both sparse and dispersive system identification is also
developed
In Car Audio
This chapter presents implementations of advanced in Car Audio Applications. The system is composed by three main different applications regarding the In Car listening and communication experience. Starting from a high level description of the algorithms, several implementations on different levels of hardware abstraction are presented, along with empirical results on both the design process undergone and the performance results achieved
GPU Implementation of multichannel adaptive algorithms for local active noise control
© 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksMultichannel active noise control (ANC) systems are commonly based on adaptive signal processing algorithms that require high computational capacity, which constrains their practical implementation. Graphics Processing Units (GPUs) are well known for their potential for highly parallel data processing. Therefore, GPUs seem to be a suitable platform for multichannel scenarios. However, efficient use of parallel computation in the adaptive filtering context is not straightforward due to the feedback loops. This paper compares two GPU implementations of a multichannel feedforward local ANC system working as a real-time prototype. Both GPU implementations are based on the filtered-x Least Mean Square algorithms; one is based on the conventional filtered-x scheme and the other is based on the modified filtered-x scheme. Details regarding the parallelization of the algorithms are given. Finally, experimental results are presented to compare the performance of both multichannel ANC GPU implementations. The results show the usefulness of many-core devices for developing versatile, scalable, and low-cost multichannel ANC systems.This work was supported by the European Union ERDF and Spanish Government under Project TEC2012-38142-C04, and Generalitat Valenciana under Project PROMETEO/2009/013. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Thushara D. Abhayapala.Lorente Giner, J.; Ferrer Contreras, M.; Diego Antón, MD.; Gonzalez, A. (2014). GPU Implementation of multichannel adaptive algorithms for local active noise control. IEEE Transactions on Audio, Speech and Language Processing. 22(11):1624-1635. https://doi.org/10.1109/TASLP.2014.2344852S16241635221
Transform domain filtering in incremental and diffusion strategies over distributed networks
We analyse incremental and diffusion co-operative schemes in which nodes share information to some neighbour nodes in order to estimate desired parameter of interest locally in the presence of noise. Each node works as an adaptive filter and having its own learning ability. In incremental co-operative fashion a node takes information from previous node and after local estimation the information is sent to next node whereas in diffusion the input is taken from various nodes so that after each iteration the behaviour of distributed network is observed. We employ LMS structure for updating the observations. The convergence performance and computational complexity of LMS-filter is very important consideration for the point of view of speed boost and cost reduction. The convergence performance of a filter depends on eigenvalue spread of covariance matrix of input data or in other words inversely proportional to the eigenvalue spread of the input data. If input data is de-correlated the eigenvalue spread is less and if input data is correlated the eigenvalue spread is more. Transform domain filter has data de-correlation properties of transforms like DCT & DFT. The data de-correlation by the unitary transforms is depends on the orthogonal property of individual transform. Hence we get improved convergence performance by applying transform domain to input data followed by power normalization of input data. If the input data is fully de-correlated the covariance matrix of input data is proportional to the identity matrix