Changing musical emotion : a computational rule system for modifying score and performance

When communicating emotion in music, composers and performers encode their expressive intentions through the control of basic musical features such as: pitch, loudness, timbre, mode, and articulation. The extent to which emotion can be controlled through the systematic manipulation of these features has not been fully examined. In this paper we present CMERS, a Computational Music Emotion Rule System for the control of perceived musical emotion that modifies features at the levels of score and performance in real-time. CMERS performance was evaluated in two rounds of perceptual testing. In experiment I, 20 participants continuously rated the perceived emotion of 15 music samples generated by CMERS. Three music works, each with five emotional variations were used (normal, happy, sad, angry, and tender). The intended emotion by CMERS was correctly identified 78% of the time, with significant shifts in valence and arousal also recorded, regardless of the works’ original emotion.

FPGA implementation of dual-microphone delay-and-sum beamforming for in-car speech enhancement and recognition

In an automotive environment, the performance of a speech recognition system is affected by environmental noise if the speech signal is acquired directly from a microphone. Speech enhancement techniques are therefore necessary to improve the speech recognition performance. In this paper, a field-programmable gate array (FPGA) implementation of dual-microphone delay-and-sum beamforming (DASB) for speech enhancement is presented. As the first step towards a cost-effective solution, the implementation described in this paper uses a relatively high-end FPGA device to facilitate the verification of various design strategies and parameters. Experimental results show that the proposed design can produce output waveforms close to those generated by a theoretical (floating-point) model with modest usage of FPGA resources. Speech recognition experiments are also conducted on enhanced in-car speech waveforms produced by the FPGA in order to compare recognition performance with the floating-point representation running on a PC.

The 10% rule. Maximising learning through collaborative game design

This paper presents a retrospective view of a game design practice that recently switched from the development of complex learning games to the development of simple authoring tools for students to design their own learning games for each other. We introduce how our ‘10% Rule’, a premise that only 10% of what is learnt during a game design process is ultimately appreciated by the player, became a major contributor to the evolving practice. We use this rule primarily as an analytical and illustrative tool to discuss the learning involved in designing and playing learning games rather than as a scientifically and empirically proven rule. The 10% rule was promoted by our experience as designers and allows us to explore the often overlooked and valuable learning processes involved in designing learning games and mobile games in particular. This discussion highlights that in designing mobile learning games, students are not only reflecting on their own learning processes through setting up structures for others to enquire and investigate, they are also engaging in high-levels of independent inquiry and critical analysis in authentic learning settings. We conclude the paper with a discussion of the importance of these types of learning processes and skills of enquiry in 21st Century learning.

GYAN: A methodology for rule extraction from artificial neural networks

Artificial neural network (ANN) learning methods provide a robust and non-linear approach to approximating the target function for many classification, regression and clustering problems. ANNs have demonstrated good predictive performance in a wide variety of practical problems. However, there are strong arguments as to why ANNs are not sufficient for the general representation of knowledge. The arguments are the poor comprehensibility of the learned ANN, and the inability to represent explanation structures. The overall objective of this thesis is to address these issues by: (1) explanation of the decision process in ANNs in the form of symbolic rules (predicate rules with variables); and (2) provision of explanatory capability by mapping the general conceptual knowledge that is learned by the neural networks into a knowledge base to be used in a rule-based reasoning system. A multi-stage methodology GYAN is developed and evaluated for the task of extracting knowledge from the trained ANNs. The extracted knowledge is represented in the form of restricted first-order logic rules, and subsequently allows user interaction by interfacing with a knowledge based reasoner. The performance of GYAN is demonstrated using a number of real world and artificial data sets. The empirical results demonstrate that: (1) an equivalent symbolic interpretation is derived describing the overall behaviour of the ANN with high accuracy and fidelity, and (2) a concise explanation is given (in terms of rules, facts and predicates activated in a reasoning episode) as to why a particular instance is being classified into a certain category.

Digital constitutionalism and the role of the rule of law in the governance of virtual communities

This project proposes a new conceptual framework for the regulation of social networks and virtual communities. By applying a model based upon the rule of law, this thesis addresses the growing tensions that revolve around the public use of private networks. This research examines the shortcomings of traditional contractual governance models and cyberlaw theory and provides a reconstituted approach that will allow public constitutional-type interests to be recognised in the interpretation and enforcement of contractual doctrine.

The role of the rule of law in virtual communities

There is a severe tendency in cyberlaw theory to delegitimize state intervention in the governance of virtual communities. Much of the existing theory makes one of two fundamental flawed assumptions: that communities will always be best governed without the intervention of the state; or that the territorial state can best encourage the development of communities by creating enforceable property rights and allowing the market to resolve any disputes. These assumptions do not ascribe sufficient weight to the value-laden support that the territorial state always provides to private governance regimes, the inefficiencies that will tend to limit the development utopian communities, and the continued role of the territorial state in limiting autonomy in accordance with communal values. In order to overcome these deterministic assumptions, this article provides a framework based upon the values of the rule of law through which to conceptualise the legitimacy of the private exercise of power in virtual communities. The rule of law provides a constitutional discourse that assists in considering appropriate limits on the exercise of private power. I argue that the private contractual framework that is used to govern relations in virtual communities ought to be informed by the values of the rule of law in order to more appropriately address the governance tensions that permeate these spaces. These values suggest three main limits to the exercise of private power: that governance is limited by community rules and that the scope of autonomy is limited by the substantive values of the territorial state; that private contractual rules should be general, equal, and certain; and that, most importantly, internal norms be predicated upon the consent of participants.

Estimation of parameters for macroparasite population evolution using approximate Bayesian computation

We estimate the parameters of a stochastic process model for a macroparasite population within a host using approximate Bayesian computation (ABC). The immunity of the host is an unobserved model variable and only mature macroparasites at sacrifice of the host are counted. With very limited data, process rates are inferred reasonably precisely. Modeling involves a three variable Markov process for which the observed data likelihood is computationally intractable. ABC methods are particularly useful when the likelihood is analytically or computationally intractable. The ABC algorithm we present is based on sequential Monte Carlo, is adaptive in nature, and overcomes some drawbacks of previous approaches to ABC. The algorithm is validated on a test example involving simulated data from an autologistic model before being used to infer parameters of the Markov process model for experimental data. The fitted model explains the observed extra-binomial variation in terms of a zero-one immunity variable, which has a short-lived presence in the host.

A Two-stage information filtering based on rough decision rule and pattern mining

Information Overload and Mismatch are two fundamental problems affecting the effectiveness of information filtering systems. Even though both term-based and patternbased approaches have been proposed to address the problems of overload and mismatch, neither of these approaches alone can provide a satisfactory solution to address these problems. This paper presents a novel two-stage information filtering model which combines the merits of term-based and pattern-based approaches to effectively filter sheer volume of information. In particular, the first filtering stage is supported by a novel rough analysis model which efficiently removes a large number of irrelevant documents, thereby addressing the overload problem. The second filtering stage is empowered by a semantically rich pattern taxonomy mining model which effectively fetches incoming documents according to the specific information needs of a user, thereby addressing the mismatch problem. The experimental results based on the RCV1 corpus show that the proposed twostage filtering model significantly outperforms the both termbased and pattern-based information filtering models.

Approximate Bayesian computation using indirect inference

We present a novel approach for developing summary statistics for use in approximate Bayesian computation (ABC) algorithms by using indirect inference. ABC methods are useful for posterior inference in the presence of an intractable likelihood function. In the indirect inference approach to ABC the parameters of an auxiliary model fitted to the data become the summary statistics. Although applicable to any ABC technique, we embed this approach within a sequential Monte Carlo algorithm that is completely adaptive and requires very little tuning. This methodological development was motivated by an application involving data on macroparasite population evolution modelled by a trivariate stochastic process for which there is no tractable likelihood function. The auxiliary model here is based on a beta–binomial distribution. The main objective of the analysis is to determine which parameters of the stochastic model are estimable from the observed data on mature parasite worms.

Noise robust voice activity detection using features extracted from the time-domain autocorrelation function

This paper presents a method of voice activity detection (VAD) for high noise scenarios, using a noise robust voiced speech detection feature. The developed method is based on the fusion of two systems. The first system utilises the maximum peak of the normalised time-domain autocorrelation function (MaxPeak). The second zone system uses a novel combination of cross-correlation and zero-crossing rate of the normalised autocorrelation to approximate a measure of signal pitch and periodicity (CrossCorr) that is hypothesised to be noise robust. The score outputs by the two systems are then merged using weighted sum fusion to create the proposed autocorrelation zero-crossing rate (AZR) VAD. Accuracy of AZR was compared to state of the art and standardised VAD methods and was shown to outperform the best performing system with an average relative improvement of 24.8% in half-total error rate (HTER) on the QUT-NOISE-TIMIT database created using real recordings from high-noise environments.

Legal ethics in a post-Westphalian world : building the international rule of law and otehr tasks

The emergence of strong sovereign states after the Treaty of Westphalia turned two of the most cosmopolitan professions (law and arms) into two of the least cosmopolitan. Sovereign states determined the content of the law within their borders – including which, if any, ecclesiastical law was to be applied; what form of economic regulation was adopted; and what, if any, international law applied. Similarly, states sought to ensure that all military force was at their disposal in national armies. The erosion of sovereignty in a post-Westphalian world may significantly reverse these processes. The erosion of sovereignty is likely to have profound consequences for the legal profession and the ethics of how, and for what ends, it is practised. Lawyers have played a major role in the civilization of sovereign states through the articulation and institutionalisation of key governance values – starting with the rule of law. An increasingly global profession must take on similar tasks. The same could be said of the military. This essay will review the concept of an international rule of law and its relationship to domestic conceptions and outline the task of building the international rule of law and the role that lawyers can and should play in it.