Prediction and proactivity in real-time interactive music systems

We advocate for the use of predictive techniques in interactive computer music systems. We suggest that the inclusion of prediction can assist in the design of proactive rather than reactive computational performance partners. We summarize the significant role prediction plays in human musical decisions, and the only modest use of prediction in interactive music systems to date. After describing how we are working toward employing predictive processes in our own metacreation software we reflect on future extensions to these approaches.

Exploring the role of activity in genre

This paper provides an outline of genre as we currently know it, and examines the changes occurring as games become more complex. Recent research we've undertaken suggests that our perception of which games fall into which genre category is subjective and that genre hybridization continues to blur our understanding of these categories. Consequently, it is becoming increasingly difficult to categorise game play experience based on traditional genre classifications. We examine the use of videogame activities as a useful mechanism for supplementing our understanding of videogame genre. Through considering activity as a means of classifying game experiences we may obtain a much more nuanced understanding of how players engage with games within a particular genre and across genres.

The future of the music industry in an age of digital distribution

This is an invited public lecture. The talk will cover how the music industry has changed due to digital technologies. During the talk I will look at how the changing balance between live music, music licensing and recorded music. I will also discuss online music subscription services and whether they might be a future for music distribution in China and elsewhere in the world. It will also look at how music artists and composers are affected by this change.

Rough set based approach to text classification

Textual document set has become an important and rapidly growing information source in the web. Text classification is one of the crucial technologies for information organisation and management. Text classification has become more and more important and attracted wide attention of researchers from different research fields. In this paper, many feature selection methods, the implement algorithms and applications of text classification are introduced firstly. However, because there are much noise in the knowledge extracted by current data-mining techniques for text classification, it leads to much uncertainty in the process of text classification which is produced from both the knowledge extraction and knowledge usage, therefore, more innovative techniques and methods are needed to improve the performance of text classification. It has been a critical step with great challenge to further improve the process of knowledge extraction and effectively utilization of the extracted knowledge. Rough Set decision making approach is proposed to use Rough Set decision techniques to more precisely classify the textual documents which are difficult to separate by the classic text classification methods. The purpose of this paper is to give an overview of existing text classification technologies, to demonstrate the Rough Set concepts and the decision making approach based on Rough Set theory for building more reliable and effective text classification framework with higher precision, to set up an innovative evaluation metric named CEI which is very effective for the performance assessment of the similar research, and to propose a promising research direction for addressing the challenging problems in text classification, text mining and other relative fields.

Predicting fault-prone software modules with rank sum classification

The detection and correction of defects remains among the most time consuming and expensive aspects of software development. Extensive automated testing and code inspections may mitigate their effect, but some code fragments are necessarily more likely to be faulty than others, and automated identification of fault prone modules helps to focus testing and inspections, thus limiting wasted effort and potentially improving detection rates. However, software metrics data is often extremely noisy, with enormous imbalances in the size of the positive and negative classes. In this work, we present a new approach to predictive modelling of fault proneness in software modules, introducing a new feature representation to overcome some of these issues. This rank sum representation offers improved or at worst comparable performance to earlier approaches for standard data sets, and readily allows the user to choose an appropriate trade-off between precision and recall to optimise inspection effort to suit different testing environments. The method is evaluated using the NASA Metrics Data Program (MDP) data sets, and performance is compared with existing studies based on the Support Vector Machine (SVM) and Naïve Bayes (NB) Classifiers, and with our own comprehensive evaluation of these methods.

The Music Industry : Music in the Cloud, 2nd edition

The music industry is going through a period of immense change brought about in part by the digital revolution. What is the role of music in the age of computers and the Internet? How has the music industry been transformed by the economic and technological upheavals of recent years, and how is it likely to change in the future? This thoroughly revised and updated new edition provides an international overview of the music industry and its future prospects in the world of global entertainment. Patrik Wikström illuminates the workings of the music industry, and captures the dynamics at work in the production of musical culture between the transnational media conglomerates, the independent music companies and the public. New to this second edition are expanded sections on the structure of the music industry, online business models and the links between social media and music. Engaging and comprehensive, The Music Industry will be a must-read for students and scholars of media and communication studies, cultural studies, popular music, sociology and economics.

Digitization of the music industries in Australasia [Editorial]

The music business is one of the most international of all the cultural industries. Music, industry practices, and people travel easily across country borders and the major music companies are dominating national music markets across the globe. However, at the same time the music industries in different countries are very idiosyncratic. Music is an ingrained part of a country’s history, its culture and heritage. One aspect of this idiosyncrasy is related to how creatives, audiences and music organizations are affected by and is able to take advantage of the ongoing digitization of society...

Local inter-session variability modelling for object classification

Object classification is plagued by the issue of session variation. Session variation describes any variation that makes one instance of an object look different to another, for instance due to pose or illumination variation. Recent work in the challenging task of face verification has shown that session variability modelling provides a mechanism to overcome some of these limitations. However, for computer vision purposes, it has only been applied in the limited setting of face verification. In this paper we propose a local region based intersession variability (ISV) modelling approach, and apply it to challenging real-world data. We propose a region based session variability modelling approach so that local session variations can be modelled, termed Local ISV. We then demonstrate the efficacy of this technique on a challenging real-world fish image database which includes images taken underwater, providing significant real-world session variations. This Local ISV approach provides a relative performance improvement of, on average, 23% on the challenging MOBIO, Multi-PIE and SCface face databases. It also provides a relative performance improvement of 35% on our challenging fish image dataset.

Commercial successes in the music industry

Commercial success in the music industry is obviously related to one’s ability to use musical artisanship as a basis for generating profits and to accumulate substantial wealth. That may seem fairly straightforward, but commercial success is an elusive concept that is continuously negotiated within the industry to determine both what should be considered “success” as well as how it should be measured. This entry discusses commercial success in the popular music industry and strategies used to achieve it.

A typology of music distribution models

A typology of music distribution models is proposed consisting of the ownership model, the access model, and the context model. These models are not substitutes for each other and may co‐exist serving different market niches. The paper argues that increasingly the economic value created from recorded music is based on con‐text rather than on ownership. During this process, access‐based services temporarily generate economic value, but such services are destined to eventually become commoditised.

Music production in times of monopoly : the example of Sweden

For several decades now, Sweden has been successful in the worldwide popular music arena. This article explores how Sweden, as an integral part of the global music industry, has been able to cope with the changed market conditions brought about by regulatory changes and digital technologies. The article reflects on the virtualization of music distribution, the decline of the long‐play album and the ageing popular music audience.