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.

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