Populating the mix space : parametric methods for generating multitrack audio mixtures

The creation of multitrack mixes by audio engineers is a time-consuming activity and creating high-quality mixes requires a great deal of knowledge and experience. Previous studies on the perception of music mixes have been limited by the relatively small number of human-made mixes analysed. This paper describes a novel mix-space, a parameter space which contains all possible mixes using a finite set of tools, as well as methods for the parametric generation of artificial mixes in this space. Mixes that use track gain, panning and equalisation are considered. This allows statistical methods to be used in the study of music mixing practice, such as Monte Carlo simulations or population-based optimisation methods. Two applications are described: an investigation into the robustness and accuracy of tempo-estimation algorithms and an experiment to estimate distributions of spectral centroid values within sets of mixes. The potential for further work is also described

Interactive Evolutionary Algorithms for Image Enhancement and Creation

Image enhancement and creation, particularly for aesthetic purposes, are tasks for which the use of interactive evolutionary algorithms would seem to be well suited. Previous work has concentrated on the development of various aspects of the interactive evolutionary algorithms and their application to various image enhancement and creation problems. Robust evaluation of algorithmic design options in interactive evolutionary algorithms and the comparison of interactive evolutionary algorithms to alternative approaches to achieving the same goals is generally less well addressed. The work presented in this thesis is primarily concerned with different interactive evolutionary algorithms, search spaces, and operators for setting the input values required by image processing and image creation tasks. A secondary concern is determining when the use of the interactive evolutionary algorithm approach to image enhancement problems is warranted and how it compares with alternative approaches. Various interactive evolutionary algorithms were implemented and compared in a number of specifically devised experiments using tasks of varying complexity. A novel aspect of this thesis, with regards to other work in the study of interactive evolutionary algorithms, was that statistical analysis of the data gathered from the experiments was performed. This analysis demonstrated, contrary to popular assumption, that the choice of algorithm parameters, operators, search spaces, and even the underlying evolutionary algorithm has little effect on the quality of the resulting images or the time it takes to develop them. It was found that the interaction methods chosen when implementing the user interface of the interactive evolutionary algorithms had a greater influence on the performances of the algorithms

Learning algorithms for adaptive digital filtering

In this thesis, we consider the problem of parameter optimisation in adaptive digital filtering. Adaptive digital filtering can be accomplished using both Finite Impulse Response (FIR) filters and Infinite Impulse Response Filters (IIR) filters. Adaptive FIR filtering algorithms are well established. However, the potential computational advantages of IIR filters has led to an increase in research on adaptive IIR filtering algorithms. These algorithms are studied in detail in this thesis and the limitations of current adaptive IIR filtering algorithms are identified. New approaches to adaptive IIR filtering using intelligent learning algorithms are proposed. These include Stochastic Learning Automata, Evolutionary Algorithms and Annealing Algorithms. Each of these techniques are used for the filtering problem and simulation results are presented showing the performance of the algorithms for adaptive IIR filtering. The relative merits and demerits of the different schemes are discussed. Two practical applications of adaptive IIR filtering are simulated and results of using the new adaptive strategies are presented. Other than the new approaches used, two new hybrid schemes are proposed based on concepts from genetic algorithms and annealing. It is shown with the help of simulation studies, that these hybrid schemes provide a superior performance to the exclusive use of any one scheme

Genetic programming for adaptive digital signal processing

Abstract available: p. i-ii

A Study of Integrated UWB Antennas Optimised for Time Domain Performance

Antennas for impulse radio ultra-wideband based portable devices are required to be compact and able to transmit or receive waveforms with minimal distortion in order to support proximity ranging with a centimetre-scale precision. The first part of thesis characterises several pulse types for use in the generation of picosecond-scale signals in respect to the regulatory power and frequency standards while the principles of antenna transient transmission and reception are stated. The proximity effect of planar conductors on the performance of an ultra-wideband antenna is investigated in both spectral and temporal domain demonstrating the relationship between the antenna-reflector separation and the antenna performance. Balanced and unbalanced antennas are also investigated for integration into asset-tracking tag applications and are designed to operate in close proximity to PCB boards while meeting realistic dimensional constraints and acceptable time domain performances. Monopole antenna designs are reported with performances optimized for minimum pulse dispersion. Minimization of pulse dispersion effects in the antenna designs is achieved using pulses with optimal spectral fit to the UWB emission mask. The generation of these waveforms are reported for the first time. An antenna de-embedding method is reported enabling validation of the simulated fidelity factor of radiated patterns. Novel differentially-fed planar dipole and slot antennas are reported for direct IC output integration. Design objectives and optimisation are focused on bandwidth enhancement and pulse dispersion minimisation. Finally, time- and frequency-domain measurements are carried out using an approach based on the superposition principle

Spectrally and Energy Efficient Wireless Communications: Signal and System Design, Mathematical Modelling and Optimisation

This thesis explores engineering studies and designs aiming to meeting the requirements of enhancing capacity and energy efficiency for next generation communication networks. Challenges of spectrum scarcity and energy constraints are addressed and new technologies are proposed, analytically investigated and examined. The thesis commences by reviewing studies on spectrally and energy-efficient techniques, with a special focus on non-orthogonal multicarrier modulation, particularly spectrally efficient frequency division multiplexing (SEFDM). Rigorous theoretical and mathematical modelling studies of SEFDM are presented. Moreover, to address the potential application of SEFDM under the 5th generation new radio (5G NR) heterogeneous numerologies, simulation-based studies of SEFDM coexisting with orthogonal frequency division multiplexing (OFDM) are conducted. New signal formats and corresponding transceiver structure are designed, using a Hilbert transform filter pair for shaping pulses. Detailed modelling and numerical investigations show that the proposed signal doubles spectral efficiency without performance degradation, with studies of two signal formats; uncoded narrow-band internet of things (NB-IoT) signals and unframed turbo coded multi-carrier signals. The thesis also considers using constellation shaping techniques and SEFDM for capacity enhancement in 5G system. Probabilistic shaping for SEFDM is proposed and modelled to show both transmission energy reduction and bandwidth saving with advantageous flexibility for data rate adaptation. Expanding on constellation shaping to improve performance further, a comparative study of multidimensional modulation techniques is carried out. A four-dimensional signal, with better noise immunity is investigated, for which metaheuristic optimisation algorithms are studied, developed, and conducted to optimise bit-to-symbol mapping. Finally, a specially designed machine learning technique for signal and system design in physical layer communications is proposed, utilising the application of autoencoder-based end-to-end learning. Multidimensional signal modulation with multidimensional constellation shaping is proposed and optimised by using machine learning techniques, demonstrating significant improvement in spectral and energy efficiencies

Video Processing Acceleration using Reconfigurable Logic and Graphics Processors

A vexing question is `which architecture will prevail as the core feature of the next state of the art video processing system?' This thesis examines the substitutive and collaborative use of the two alternatives of the reconfigurable logic and graphics processor architectures. A structured approach to executing architecture comparison is presented - this includes a proposed `Three Axes of Algorithm Characterisation' scheme and a formulation of perfor- mance drivers. The approach is an appealing platform for clearly defining the problem, assumptions and results of a comparison. In this work it is used to resolve the advanta- geous factors of the graphics processor and reconfigurable logic for video processing, and the conditions determining which one is superior. The comparison results prompt the exploration of the customisable options for the graphics processor architecture. To clearly define the architectural design space, the graphics processor is first identifed as part of a wider scope of homogeneous multi-processing element (HoMPE) architectures. A novel exploration tool is described which is suited to the investigation of the customisable op- tions of HoMPE architectures. The tool adopts a systematic exploration approach and a high-level parameterisable system model, and is used to explore pre- and post-fabrication customisable options for the graphics processor. A positive result of the exploration is the proposal of a reconfigurable engine for data access (REDA) to optimise graphics processor performance for video processing-specific memory access patterns. REDA demonstrates the viability of the use of reconfigurable logic as collaborative `glue logic' in the graphics processor architecture

Evaluation and modelling of perceived audio quality in popular music, towards intelligent music production

This thesis addresses three fundamental questions: What is mixing? What makes a high-quality mix? How can high-quality mixes be automatically generated? While these may seem essential to the very foundations of intelligent music production, this thesis argues that they have not been sufficiently addressed in previous studies. An important contribution is the questioning of previously-held definitions of a 'mix'. Experiments were conducted in which participants used traditional mixing interfaces to create mixes using gain, panning and equalisation. The data was analysed in a novel 'mix-space', 'panning-space' and 'tone-space' in order to determine if there is a consensus in how these tools are used. Methods were developed to create mixes by populating the mix-space according to parametric models. These mixes were characterised by signal features, the distributions of which suggest tolerance bounds for automated mixing systems. This was complemented by a study of real-world music mixes, containing hundreds of mixes each for ten songs, collected from on-line communities. Mixes were shown to vary along four dimensions: loudness/dynamics, brightness, bass and stereo width. The variations between individual mix engineers were also studied, indicating a small effect of the mix engineer on mix preference ratings (eta2 = 0.021). Perceptual audio evaluation revealed that listeners appreciate 'quality' in a variety of ways, depending on the circumstances. In commercially-released music, 'quality' was related to the loudness/dynamic dimension. In mixes, 'quality' is highly correlated with 'preference'. To create mixes which maximised perceived quality, a novel semi-automatic mixing system was developed using evolutionary computation, wherein a population of mixes, generated in the mix-space, is guided by the subjective evaluations of the listener. This system was evaluated by a panel of users, who used it to create their ideal mixes, rather than the technically-correct mixes which previous systems strived for. It is hoped that this thesis encourages the community to pursue subjectively motivated methods when designing systems for music-mixing