Integrating musicological knowledge into a probabilistic framework for chord and key extraction

In this contribution a formerly developed probabilistic framework for the simultaneous detection of chords and keys in polyphonic audio is further extended and validated. The system behaviour is controlled by a small set of carefully defined free parameters. This has permitted us to conduct an experimental study which sheds a new light on the importance of musicological knowledge in the context of chord extraction. Some of the obtained results are at least surprising and, to our knowledge, never reported as such before

Group Compactifications and Moduli Spaces

We give a summary of joint work with Michael Thaddeus that realizes toroidal compactifcations of split reductive groups as moduli spaces of framed bundles on chains of rational curves. We include an extension of this work that covers Artin stacks with good moduli spaces. We discuss, for complex groups, the symplectic counterpart of these compactifications, and conclude with some open problems about the moduli problem concerned.Comment: To appear in proceedings of "International Conference on Analytic and Algebraic Geometry

Modeling musicological information as trigrams in a system for simultaneous chord and local key extraction

In this paper, we discuss the introduction of a trigram musicological model in a simultaneous chord and local key extraction system. By enlarging the context of the musicological model, we hoped to achieve a higher accuracy that could justify the associated higher complexity and computational load of the search for the optimal solution. Experiments on multiple data sets have demonstrated that the trigram model has indeed a larger predictive power (a lower perplexity). This raised predictive power resulted in an improvement in the key extraction capabilities, but no improvement in chord extraction when compared to a system with a bigram musicological model

New trends in data mining.

Trends; Data; Data mining;

Country corruption analysis with self organizing maps and support vector machines.

During recent years, the empirical research on corruption has grown considerably. Possible links between government corruption and terrorism have attracted an increasing interest in this research field. Most of the existing literature discusses the topic from a socio-economical perspective and only few studies tackle this research field from a data mining point of view. In this paper, we apply data mining techniques onto a cross-country database linking macro-economical variables to perceived levels of corruption. In the first part, self organizing maps are applied to study the interconnections between these variables. Afterwards, support vector machines are trained on part of the data and used to forecast corruption for other countries. Large deviations for specific countries between these models' predictions and the actual values can prove useful for further research. Finally, projection of the forecasts onto a self organizing map allows a detailed comparison between the different models' behavior.cross-country;

Forecasting Sovereign Default risk with Merton’s Model

Merton's structural model for sovereigns is proven to be useful to analyze the default risk of a country. We are the first to investigate how fast CDS spreads react to changes in model inputs and outputs. CDS spread changes strongly correlate with exchange rate returns, which are an input to the model. But CDS spread changes on average react with a delay to changes in model outputs such as the distance to default, the default probability and model spreads. Hence contingency claim analysis for sovereigns provides useful predictions for CDS spreads

Improving the key extraction performance of a simultaneous local key and chord estimation system

In this paper, significant improvements of a previously developed key and chord extraction system are proposed. The major improvement is the introduction of a separate acoustic model, designed to verify local key hypotheses. The conducted experimental evaluation shows that the presented system improves the state of the art in local key estimation. Our experimental study further demonstrates that the chord estimation performance is already quite robust, whereas the key estimation performance still happens to be sensitive to a number of factors. In particular, we present figures that illustrate the significant impact of the embedded musicological model and the duration of the processed excerpt on the key estimation accuracy