With a budget of over 600K Euros and a pre-financing of 395K Euros, the project involves a partnership with the Digital Media Centre and the Audio Research Group at DIT, one of Europe’s leading audio Codec developers (DLLNI Ltd based in Bangor, Northern Ireland) and a specialist SME audio post-production company (Tamborine Productions Ltd based in London). Awarded in November 2009 through the Seventh Framework Programme of the European Union through the Support for Training and Career Development of Researchers (Marie Curie) and the Industry-Academia Partnerships and Pathways initiative, the project is coordinated and directed by Principal Investigators Dr Charlie Cullen (Digital Media Centre) and SFI Stokes Professor Jonathan Blackledge (Information and Communications Security Research Group), respectively. The broad aim of the research project is to use object-oriented audio to overcome many of the problems associated with channel-based coding and make it possible to transmit 3D audio scenes and objects over narrow bandwidths in a way that allows real user interactivity.

Auditory scene analysis (ASA) studies the interactions of concurrently presented sounds and their perception by the listener. The human listener splits and fuses physical sounds into logical groups known as streams, where multiple streams containing similar sounds (such as a choir) can often be perceived as a single stream. ASA describes stream grouping using psychological terms such as familiarity (a well known song is easier to recognize), similarity (a group of singing voices) and proximity (sources that are close in pitch, intensity or location).

In this example, an initial rhythmic juxtaposition of bass and strings defines two streams in bar one. At the same time, the interplay between these streams also groups them at another level relative to the bassoon melody. The introduction of a violin part with the same rhythm as the other stringed instruments creates a single string group in bar two. At the same time, the rhythm and pitch contour of the initial bassoon melody is repeated in the second bar by the basset horns.

Most audio coding algorithms adopt a channel-oriented approach (e.g. MPEG-1 and MPEG-2), where the final mixed signal in a given number of channels is processed for transmission. Channel coding is often lossy and destructive. For example, the relative volume balance between contrabass, strings and woodwind stream objects in Mozart’s Requiem will be defined by an engineer during mixing. If changes are made to the playout setup, or bandwidth is reduced to stream to a mobile device, the priorities in the original are lost in the adaptation to the new transmission conditions. Lossy compression is likely to have a worse effect on the woodwind than the strings or contrabass, and will severely compromise the final content.
Algorithms such as MPEG-4 and AudioBIFS can transmit a 3D scene comprised of audio objects to a wide range of playout setups, offering interactivity and capable of low bandwidth operation. AudioBIFS can mix, process and spatialise audio streams, while advanced AudioBIFS (AABIFS) also support descriptions of the acoustic properties of sound sources and environments, the source direction and room reverberation.

Security economics is the analysis of aggregate risks facing society and economy using rigorous analytical and empirical tools of economics. Policy makers may tend to take imperfect security decisions (e.g. regulations) based on a public perception of (in)security, with an impact to market structures. A singular focus on security or competitiveness would be too narrow and research under this area offers key insights that will contribute to balancing security and the overall policy objectives. Economic theory in particular can offer key insights, enabling governments to optimise their efforts to enhance security and growth. The expected impact of undertaking research in Security Economics is the provision to users of a decision support system providing them for insight into the pros and cons of specific security investments compared to a set of alternatives taking into account a wider societal context. This requires research projects that involve the exhaustive analysis of time series analysis that are best associated securing accurate economic forecasting.

The development of financial models that can provide increasingly accurate forecasts has been a fundamental requirement in economics for many years. For example, macroeconomic models are used in the preparation of Budget and Pre-Budget Report forecasts by most governments. Given the increased volatility in the financial markets, any approach that can provide more accurate forecasts are of interest to those working in the financial sector. Current models for market analysis are based on the Efficient Market Hypothesis EMH, which involves assumptions that are incompatible with real financial signals, including that market signals exhibit stationary Gaussian processes. The EMH is the basis for models such as the Black-Scholes equation.

The research question considered which underpins this project is whether a Fractal Market Hypothesis (FMH), based on the application of Fractional Dynamics can provide a more accurate representation of financial signals, in genera. Fractional Dynamics relies on models that include fractional Partial Differential Equations. This proposal is based on investigating solutions to a non-stationary fractional diffusion equation whose characteristics may be used to model financial signals with greater accuracy and physical significance than current models do. This approach may provide the basis for developing new macroeconomic forecasting measures that are both accurate and robust.