ARTIFICIAL INTELLIGENCE-BASED APPROACH TO MODELLING OF PIPE ORGANS

The aim of the project was to develop a new Artificial Intelligence-based method to aid modeling of musical instruments and sound design. Despite significant advances in music technology, sound design and synthesis of complex musical instruments is still time consuming, error prone and requires expert understanding of the instrument attributes and significant expertise to produce high quality synthesised sounds to meet the needs of musicians and musical instrument builders. Artificial Intelligence (Al) offers an effective means of capturing this expertise and for handling the imprecision and uncertainty inherent in audio knowledge and data. This thesis presents new techniques to capture and exploit audio expertise, following extended knowledge elicitation with two renowned music technologist/audio experts, developed and embodied into an intelligent audio system. The Al combined with perceptual auditory modeling ba.sed techniques (ITU-R BS 1387) make a generic modeling framework providing a robust methodology for sound synthesis parameters optimisation with objective prediction of sound synthesis quality. The evaluation, carried out using typical pipe organ sounds, has shown that the intelligent audio system can automatically design sounds judged by the experts to be of very good quality, while significantly reducing the expert's work-load by up to a factor of three and need for extensive subjective tests. This research work, the first initiative to capture explicitly knowledge from audio experts for sound design, represents an important contribution for future design of electronic musical instruments based on perceptual sound quality will help to develop a new sound quality index for benchmarking sound synthesis techniques and serve as a research framework for modeling of a wide range of musical instruments.Musicom Lt

Learning EEG-based spectral-spatial patterns for attention level measurement

Abstract — In our every day life, our brain is constantly processing information and paying attention, reacting accordingly, to all sorts of sensory inputs (auditory, visual, etc.). In some cases, there is a need to accurately measure a person’s level of attention to monitor a sportsman performance, to detect Attention Deficit Hyperactivity Disorder (ADHD) in children, to evaluate the effectiveness of neuro-feedback treatment, etc. In this paper we propose a novel approach to extract, select and learn spectral-spatial patterns from electroencephalogram (EEG) recordings. Our approach improves over prior-art methods that was, typically, only concerned with power of specific EEG rhythms from few individual channels. In this new approach, spectral-spatial features from multichannel EEG are extracted by a two filtering stages: a filter-bank (FB) and common spatial patterns (CSP) filters. The most important features are selected by a Mutual Information (MI) based feature selection procedure and then classified using Fisher linear discriminant (FLD). The outcome is a measure of the attention level. An experimental study was conducted with 5 healthy young male subjects with their EEG recorded in various attention and non-attention conditions (opened eyes, closed eyes, reading, counting, relaxing, etc.). EEGs were used to train and evaluate the model using 4x4fold cross-validation procedure. Results indicate that the new proposed approach outperforms the prior-art methods and can achieve up to 89.4 % classification accuracy rate (with an average improvement of up to 16%). We demonstrate its application with a two-players attention-based racing car computer game. I

Bioprofiling over grid for early detection of dementia

Summarization: The primary aim of this paper is to present a new concept, bioprofiling over Grid, and to illustrate how Grid computing may be used to support individualisation of healthcare in future, with the aid of a new test bed, the BIOPATTERN Grid. The BIOPATTERN Grid is designed to facilitate seamless sharing of geographically distributed bioprofile databases and to support the analysis of bioprofiles to combat major diseases such as brain diseases and cancer within a major EU project, BIOPATTERN (www.biopattern.org). The main objectives in this paper are 1) to report the development of the BIOPATTERN Grid for biopattern analysis and bioprofiling in support of individualisation of healthcare; 2) to illustrate how the BIOPATTERN Grid could be used for biopattern analysis and bioprofiling for early detection of dementia. We present the architecture and general functionalities of BIOPATTERN Grid, and the development of a prototype test bed (including a Grid Portal and Grid services for early detection of dementia). We illustrate the concept of bioprofiling over Grid and discuss issues such as scalability in high through-put computing for biodata analysis associated with bioprofiling for dementia. Results show benefits in both high throughput computing in biodata analysis and for individualisation of healthcare using Grid computing which makes it possible to access geographically distributed patient's information for subject-specific data analysis for early detection of dementia.Παρουσιάστηκε στο: 1st international conference on Scalable information system