The strength of Timbre and why it can be an invaluable tool to a Moving Image Composer

The aim of this research is to demonstrate that timbre is one of the most effective tools found in a film composers tool box, and one that in modern film composition is used quite extensively. This research aims to build on prior research that provides an analytical understanding of sound, which demonstrate implications for timbre; including semiotic connotations, in sound design and conventional film based scores (Tagg, 2012 and Chion, 1994). Also to dissect how modern film composers, such as Hans Zimmer and Howard Shore, and sound designers, such as Richard Beggs, have used timbre in their soundtracks to offer a developing voice to conventional score based film soundtracks. In this thesis, I will also show how these insights have been applied in my own soundtracks. In the composition portfolio, I have explored how timbre might create semiotic connections with the image, in a similar way to the composers and sound designers this thesis discusses. I have taken 20 minutes from the film Shutter Island and re-written the accompanying music with timbre being the main tool of composition. Therefore other compositional materials have been made use of. Even though these materials - such as harmony, melody and rhythm - are used, they are always dictated by timbre. Timbre always comes first, and for example, the melodies have been shaped by the different timbres that they are sounded by. This has been done, to show what a composer might expect to find musically in their own film compositions, when viewing timbre as a key component in their tool box

A probabilistic classifier ensemble weighting scheme based on cross-validated accuracy estimates

Our hypothesis is that building ensembles of small sets of strong classifiers constructed with different learning algorithms is, on average, the best approach to classification for real world problems. We propose a simple mechanism for building small heterogeneous ensembles based on exponentially weighting the probability estimates of the base classifiers with an estimate of the accuracy formed through cross-validation on the train data. We demonstrate through extensive experimentation that, given the same small set of base classifiers, this method has measurable benefits over commonly used alternative weighting, selection or meta classifier approaches to heterogeneous ensembles. We also show how an ensemble of five well known, fast classifiers can produce an ensemble that is not significantly worse than large homogeneous ensembles and tuned individual classifiers on datasets from the UCI archive. We provide evidence that the performance of the Cross-validation Accuracy Weighted Probabilistic Ensemble (CAWPE) generalises to a completely separate set of datasets, the UCR time series classification archive, and we also demonstrate that our ensemble technique can significantly improve the state-of-the-art classifier for this problem domain. We investigate the performance in more detail, and find that the improvement is most marked in problems with smaller train sets. We perform a sensitivity analysis and an ablation study to demonstrate the robustness of the ensemble and the significant contribution of each design element of the classifier. We conclude that it is, on average, better to ensemble strong classifiers with a weighting scheme rather than perform extensive tuning and that CAWPE is a sensible starting point for combining classifiers