66 research outputs found
Digital Filter Design Using Improved Artificial Bee Colony Algorithms
Digital filters are often used in digital signal processing applications. The design objective of a digital filter is to find the optimal set of filter coefficients, which satisfies the desired specifications of magnitude and group delay responses. Evolutionary algorithms are population-based meta-heuristic algorithms inspired by the biological behaviors of species. Compared to gradient-based optimization algorithms such as steepest descent and Newton’s like methods, these bio-inspired algorithms have the advantages of not getting stuck at local optima and being independent of the starting point in the solution space. The limitations of evolutionary algorithms include the presence of control parameters, problem specific tuning procedure, premature convergence and slower convergence rate. The artificial bee colony (ABC) algorithm is a swarm-based search meta-heuristic algorithm inspired by the foraging behaviors of honey bee colonies, with the benefit of a relatively fewer control parameters. In its original form, the ABC algorithm has certain limitations such as low convergence rate, and insufficient balance between exploration and exploitation in the search equations. In this dissertation, an ABC-AMR algorithm is proposed by incorporating an adaptive modification rate (AMR) into the original ABC algorithm to increase convergence rate by adjusting the balance between exploration and exploitation in the search equations through an adaptive determination of the number of parameters to be updated in every iteration. A constrained ABC-AMR algorithm is also developed for solving constrained optimization problems.There are many real-world problems requiring simultaneous optimizations of more than one conflicting objectives. Multiobjective (MO) optimization produces a set of feasible solutions called the Pareto front instead of a single optimum solution. For multiobjective optimization, if a decision maker’s preferences can be incorporated during the optimization process, the search process can be confined to the region of interest instead of searching the entire region. In this dissertation, two algorithms are developed for such incorporation. The first one is a reference-point-based MOABC algorithm in which a decision maker’s preferences are included in the optimization process as the reference point. The second one is a physical-programming-based MOABC algorithm in which physical programming is used for setting the region of interest of a decision maker. In this dissertation, the four developed algorithms are applied to solve digital filter design problems. The ABC-AMR algorithm is used to design Types 3 and 4 linear phase FIR differentiators, and the results are compared to those obtained by the original ABC algorithm, three improved ABC algorithms, and the Parks-McClellan algorithm. The constrained ABC-AMR algorithm is applied to the design of sparse Type 1 linear phase FIR filters of filter orders 60, 70 and 80, and the results are compared to three state-of-the-art design methods. The reference-point-based multiobjective ABC algorithm is used to design of asymmetric lowpass, highpass, bandpass and bandstop FIR filters, and the results are compared to those obtained by the preference-based multiobjective differential evolution algorithm. The physical-programming-based multiobjective ABC algorithm is used to design IIR lowpass, highpass and bandpass filters, and the results are compared to three state-of-the-art design methods. Based on the obtained design results, the four design algorithms are shown to be competitive as compared to the state-of-the-art design methods
Digital Filters and Signal Processing
Digital filters, together with signal processing, are being employed in the new technologies and information systems, and are implemented in different areas and applications. Digital filters and signal processing are used with no costs and they can be adapted to different cases with great flexibility and reliability. This book presents advanced developments in digital filters and signal process methods covering different cases studies. They present the main essence of the subject, with the principal approaches to the most recent mathematical models that are being employed worldwide
High-level power optimisation for Digital Signal Processing in Recon gurable Logic
This thesis is concerned with the optimisation of Digital Signal Processing (DSP) algorithm
implementations on recon gurable hardware via the selection of appropriate word-lengths
for the signals in these algorithms, in order to minimise system power consumption. Whilst
existing word-length optimisation work has concentrated on the minimisation of the area of
algorithm implementations, this work introduces the rst set of power consumption models
that can be evaluated quickly enough to be used within the search of the enormous design
space of multiple word-length optimisation problems. These models achieve their speed by
estimating both the power consumed within the arithmetic components of an algorithm
and the power in the routing wires that connect these components, using only a high-level
description of the algorithm itself. Trading o a small reduction in power model accuracy
for a large increase in speed is one of the major contributions of this thesis.
In addition to the work on power consumption modelling, this thesis also develops a
new technique for selecting the appropriate word-lengths for an algorithm implementation
in order to minimise its cost in terms of power (or some other metric for which models
are available). The method developed is able to provide tight lower and upper bounds on
the optimal cost that can be obtained for a particular word-length optimisation problem
and can, as a result, nd provably near-optimal solutions to word-length optimisation
problems without resorting to an NP-hard search of the design space.
Finally the costs of systems optimised via the proposed technique are compared to
those obtainable by word-length optimisation for minimisation of other metrics (such as
logic area) and the results compared, providing greater insight into the nature of wordlength
optimisation problems and the extent of the improvements obtainable by them
NATURAL ALGORITHMS IN DIGITAL FILTER DESIGN
Digital filters are an important part of Digital Signal Processing (DSP), which plays
vital roles within the modern world, but their design is a complex task requiring a great
deal of specialised knowledge. An analysis of this design process is presented, which
identifies opportunities for the application of optimisation.
The Genetic Algorithm (GA) and Simulated Annealing are problem-independent
and increasingly popular optimisation techniques. They do not require detailed prior
knowledge of the nature of a problem, and are unaffected by a discontinuous search
space, unlike traditional methods such as calculus and hill-climbing.
Potential applications of these techniques to the filter design process are discussed,
and presented with practical results. Investigations into the design of Frequency Sampling
(FS) Finite Impulse Response (FIR) filters using a hybrid GA/hill-climber proved
especially successful, improving on published results. An analysis of the search space
for FS filters provided useful information on the performance of the optimisation technique.
The ability of the GA to trade off a filter's performance with respect to several design
criteria simultaneously, without intervention by the designer, is also investigated.
Methods of simplifying the design process by using this technique are presented, together
with an analysis of the difficulty of the non-linear FIR filter design problem from
a GA perspective. This gave an insight into the fundamental nature of the optimisation
problem, and also suggested future improvements.
The results gained from these investigations allowed the framework for a potential
'intelligent' filter design system to be proposed, in which embedded expert knowledge,
Artificial Intelligence techniques and traditional design methods work together. This
could deliver a single tool capable of designing a wide range of filters with minimal
human intervention, and of proposing solutions to incomplete problems. It could also
provide the basis for the development of tools for other areas of DSP system design
Intelligent Tools for Multitrack Frequency and Dynamics Processing
PhDThis research explores the possibility of reproducing mixing decisions of a skilled audio
engineer with minimal human interaction that can improve the overall listening experience of
musical mixtures, i.e., intelligent mixing. By producing a balanced mix automatically
musician and mixing engineering can focus on their creativity while the productivity of music
production is increased. We focus on the two essential aspects of such a system, frequency
and dynamics. This thesis presents an intelligent strategy for multitrack frequency and
dynamics processing that exploit the interdependence of input audio features, incorporates
best practices in audio engineering, and driven by perceptual models and subjective criteria.
The intelligent frequency processing research begins with a spectral characteristic analysis of
commercial recordings, where we discover a consistent leaning towards a target equalization
spectrum. A novel approach for automatically equalizing audio signals towards the observed
target spectrum is then described and evaluated. We proceed to dynamics processing, and
introduce an intelligent multitrack dynamic range compression algorithm, in which various
audio features are proposed and validated to better describe the transient nature and spectral
content of the signals. An experiment to investigate the human preference on dynamic
processing is described to inform our choices of parameter automations. To provide a
perceptual basis for the intelligent system, we evaluate existing perceptual models, and
propose several masking metrics to quantify the masking behaviour within the multitrack
mixture. Ultimately, we integrate previous research on auditory masking, frequency and
dynamics processing, into one intelligent system of mix optimization that replicates the
iterative process of human mixing. Within the system, we explore the relationship between
equalization and dynamics processing, and propose a general frequency and dynamics
processing framework. Various implementations of the intelligent system are explored and
evaluated objectively and subjectively through listening experiments.China Scholarship Council
Music Production Behaviour Modelling
The new millennium has seen an explosion of computational approaches to the study of music production, due in part to the decreasing cost of computation and the increase of digital music production techniques. The rise of digital recording equipment, MIDI, digital audio workstations (DAWs), and software plugins for audio effects led to the digital capture of various processes in music production. This discretization of traditionally analogue methods allowed for the development of intelligent music production, which uses machine learning to numerically characterize and automate portions of the music production process. One algorithm from the field referred to as ``reverse engineering a multitrack mix'' can recover the audio effects processing used to transform a multitrack recording into a mixdown in the absence of information about how the mixdown was achieved. This thesis improves on this method of reverse engineering a mix by leveraging recent advancements in machine learning for audio. Using the differentiable digital signal processing paradigm, greybox modules for gain, panning, equalisation, artificial reverberation, memoryless waveshaping distortion, and dynamic range compression are presented. These modules are then connected in a mixing chain and are optimized to learn the effects used in a given mixdown. Both objective and perceptual metrics are presented to measure the performance of these various modules in isolation and within a full mixing chain. Ultimately a fully differentiable mixing chain is presented that outperforms previously proposed methods to reverse engineer a mix. Directions for future work are proposed to improve characterization of multitrack mixing behaviours
Efficient algorithms for arbitrary sample rate conversion with application to wave field synthesis
Arbitrary sample rate conversion (ASRC) is used in many fields of digital signal processing to alter the sampling rate of discrete-time signals by arbitrary, potentially time-varying ratios.
This thesis investigates efficient algorithms for ASRC and proposes several improvements. First, closed-form descriptions for the modified Farrow structure and Lagrange interpolators are derived that are directly applicable to algorithm design and analysis. Second, efficient implementation structures for ASRC algorithms are investigated. Third, this thesis considers coefficient design methods that are optimal for a selectable error norm and optional design constraints.
Finally, the performance of different algorithms is compared for several performance metrics. This enables the selection of ASRC algorithms that meet the requirements of an application with minimal complexity.
Wave field synthesis (WFS), a high-quality spatial sound reproduction technique, is the main application considered in this work. For WFS, sophisticated ASRC algorithms improve the quality of moving sound sources. However, the improvements proposed in this thesis are not limited to WFS, but applicable to general-purpose ASRC problems.Verfahren zur unbeschränkten Abtastratenwandlung (arbitrary sample rate
conversion,ASRC) ermöglichen die Änderung der Abtastrate zeitdiskreter
Signale um beliebige, zeitvarianteVerhältnisse. ASRC wird in vielen
Anwendungen digitaler Signalverarbeitung eingesetzt.In dieser Arbeit wird
die Verwendung von ASRC-Verfahren in der Wellenfeldsynthese(WFS), einem
Verfahren zur hochqualitativen, räumlich korrekten Audio-Wiedergabe,
untersucht.Durch ASRC-Algorithmen kann die Wiedergabequalität bewegter
Schallquellenin WFS deutlich verbessert werden. Durch die hohe Zahl der in
einem WFS-Wiedergabesystembenötigten simultanen ASRC-Operationen ist eine
direkte Anwendung hochwertigerAlgorithmen jedoch meist nicht möglich.Zur
Lösung dieses Problems werden verschiedene Beiträge vorgestellt. Die
Komplexitätder WFS-Signalverarbeitung wird durch eine geeignete
Partitionierung der ASRC-Algorithmensignifikant reduziert, welche eine
effiziente Wiederverwendung von Zwischenergebnissenermöglicht. Dies
erlaubt den Einsatz hochqualitativer Algorithmen zur Abtastratenwandlungmit
einer Komplexität, die mit der Anwendung einfacher konventioneller
ASRCAlgorithmenvergleichbar ist. Dieses Partitionierungsschema stellt
jedoch auch zusätzlicheAnforderungen an ASRC-Algorithmen und erfordert
Abwägungen zwischen Performance-Maßen wie der algorithmischen
Komplexität, Speicherbedarf oder -bandbreite.Zur Verbesserung von
Algorithmen und Implementierungsstrukturen fĂĽr ASRC werdenverschiedene
MaĂźnahmen vorgeschlagen. Zum Einen werden geschlossene,
analytischeBeschreibungen fĂĽr den kontinuierlichen Frequenzgang
verschiedener Klassen von ASRCStruktureneingefĂĽhrt. Insbesondere fĂĽr
Lagrange-Interpolatoren, die modifizierte Farrow-Struktur sowie
Kombinationen aus Ăśberabtastung und zeitkontinuierlichen
Resampling-Funktionen werden kompakte Darstellungen hergeleitet, die sowohl
Aufschluss ĂĽber dasVerhalten dieser Filter geben als auch eine direkte
Verwendung in Design-Methoden ermöglichen.Einen zweiten Schwerpunkt bildet
das Koeffizientendesign fĂĽr diese Strukturen, insbesonderezum optimalen
Entwurf bezüglich einer gewählten Fehlernorm und optionaler
Entwurfsbedingungenund -restriktionen. Im Gegensatz zu bisherigen Ansätzen
werden solcheoptimalen Entwurfsmethoden auch fĂĽr mehrstufige
ASRC-Strukturen, welche ganzzahligeĂśberabtastung mit zeitkontinuierlichen
Resampling-Funktionen verbinden, vorgestellt.FĂĽr diese Klasse von
Strukturen wird eine Reihe angepasster Resampling-Funktionen
vorgeschlagen,welche in Verbindung mit den entwickelten optimalen
Entwurfsmethoden signifikanteQualitätssteigerungen ermöglichen.Die
Vielzahl von ASRC-Strukturen sowie deren Design-Parameter bildet eine
Hauptschwierigkeitbei der Auswahl eines fĂĽr eine gegebene Anwendung
geeigneten Verfahrens.Evaluation und Performance-Vergleiche bilden daher
einen dritten Schwerpunkt. Dazu wirdzum Einen der Einfluss verschiedener
Entwurfsparameter auf die erzielbare Qualität vonASRC-Algorithmen
untersucht. Zum Anderen wird der benötigte Aufwand bezüglich
verschiedenerPerformance-Metriken in Abhängigkeit von Design-Qualität
dargestellt.Auf diese Weise sind die Ergebnisse dieser Arbeit nicht auf WFS
beschränkt, sondernsind in einer Vielzahl von Anwendungen unbeschränkter
Abtastratenwandlung nutzbar
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