Comparison of classical and sequential design of experiments in note onset detection

Design of experiments is an established approach to parameter optimization of industrial processes. In many computer applications however it is usual to optimize the parameters via genetic algorithms. The main idea of this work is to apply design of experiment’s techniques to the optimization of computer processes. The major problem here is finding a compromise between model validity and costs, which increase with the number of experiments. The second relevant problem is choosing an appropriate model, which describes the relationship between parameters and target values. One of the recent approaches here is model combination, which can be used in sequential designs in order to improve automatic prediction of the next trial point. In this paper a musical note onset detection algorithm will be optimized using sequential parameter optimization with model combination. It will be shown that parameter optimization via design of experiments leads to better values of the target variable than usual parameter optimization via grid search or genetic optimization algorithms. Furthermore, the results of this application study reveal, whether the combination of many models brings improvements in finding the optimal parameter setting

Comparison of parameter optimization techniques for a music tone onset detection algorithm

Design of experiments is an established approach to parameter optimization for industrial processes. In many computer applications, however, it is usual to optimize the parameters via genetic algorithms or, recently, via sequential parameter optimization techniques. The main idea of this work is to analyse and compare parameter optimization approaches which are usually applied in industry with those applied for computer optimization tasks using the example of a tone onset detection algorithm. The optimal algorithm parameter setting is sought in order to get the best onset detection accuracy. We vary in our work essential options of the parameter optimization strategies like size and constitution of the initial designs in order to assess their in uence on the evaluation results. Furthermore we test how the instrumentation and the tempo of music pieces affect the optimal parameter setting of the onset detection algorithm

Weakly-Supervised Temporal Localization via Occurrence Count Learning

We propose a novel model for temporal detection and localization which allows the training of deep neural networks using only counts of event occurrences as training labels. This powerful weakly-supervised framework alleviates the burden of the imprecise and time-consuming process of annotating event locations in temporal data. Unlike existing methods, in which localization is explicitly achieved by design, our model learns localization implicitly as a byproduct of learning to count instances. This unique feature is a direct consequence of the model's theoretical properties. We validate the effectiveness of our approach in a number of experiments (drum hit and piano onset detection in audio, digit detection in images) and demonstrate performance comparable to that of fully-supervised state-of-the-art methods, despite much weaker training requirements.Comment: Accepted at ICML 201

Time efficient optimization of instance based problems with application to tone onset detection

A time efficient optimization technique for instance based problems is proposed, where for each parameter setting the target function has to be evaluated on a large set of problem instances. Computational time is reduced by beginning with a performance estimation based on the evaluation of a representative subset of instances. Subsequently, only promising settings are evaluated on the whole data set. As application a comprehensive music onset detection algorithm is introduced where several numerical and categorical algorithm parameters are optimized simultaneously. Here, problem instances are music pieces of a data base. Sequential model based optimization is an appropriate technique to solve this optimization problem. The proposed optimization strategy is compared to the usual model based approach with respect to the goodness measure for tone onset detection. The performance of the proposed method appears to be competitive with the usual one while saving more than 84% of instance evaluation time on average. One other aspect is a comparison of two strategies for handling categorical parameters in Kriging based optimization

Ellipticity loss analysis for tangent moduli deduced from a large strain elastic–plastic self-consistent model

In order to investigate the impact of microstructures and deformation mechanisms on the ductility of materials, the criterion first proposed by Rice is applied to elastic–plastic tangent moduli derived from a large strain micromechanical model combined with a self-consistent scale-transition technique. This approach takes into account several microstructural aspects for polycrystalline aggregates: initial and induced textures, dislocation densities as well as softening mechanisms such that the behavior during complex loading paths can be accurately described. In order to significantly reduce the computing time, a new method drawn from viscoplastic formulations is introduced so that the slip system activity can be efficiently determined. The different aspects of the single crystal hardening (self and latent hardening, dislocation storage and annihilation, mean free path, etc.) are taken into account both by the introduction of dislocation densities per slip system as internal variables and the corresponding evolution equations. Comparisons are made with experimental results for single and dual-phase steels involving linear and complex loading paths. Rice’s criterion is then coupled and applied to this constitutive model in order to determine the ellipticity loss of the polycrystalline tangent modulus. This criterion, which does not need any additional “fitting” parameter, is used to build Ellipticity Limit Diagrams (ELDs).ArcelorMittal Researc

Model based optimization of music onset detection

In this paper a comprehensive online music onset detection algorithm is introduced where - in contrast to many other relevant publications - 14 important algorithm parameters are optimized simultaneously. For solving the optimization problem we derive an extensive tool for iterative model based optimization. In each iteration, a very time consuming evaluation has to be per- formed on a large music data base. To speed up this procedure, the expected performance of each newly proposed setting is estimated in a pretest on a representative part of the data so that just very promising points are evaluated on all data. We compare different variants of the classical and the fast optimization strategies with respect to the F-values of their best identified parameter settings. The performance of the fast approach appears to be competitive with the classical one while saving more than 80% of music piece evaluations on average. Our best found parameter settings, both for online and offline onset detection, are mainly in accordance with the usual choices in the state- of-the art literature concerning, e.g., the spectral flux detection function or preferences for window length and overlap. However, we also found unexpected results. For example, the adaptive whitening pre-processing step showed no effect

A multivariate approach for onset detection using supervised classification

In this paper we introduce a new onset detection approach which incorporates a supervised classification model for estimating the tone onset probability in signal frames. In contrast to the most classical strategies where only one detection function can be applied for signal feature extraction, the classification model can be fitted on a large feature set. This is meaningful since, depending on the music characteristics, some detection functions can be more advantageous that the others. Although the idea of the considering of many detection functions is not new in the literature, these functions are, so far, treated in a univariate way by, e.g., building of weighted sums. This probably lies on the difficulties of the direct transfer of the classification ideas to the onset detection task. The goodness measure of onset detection is namely based on the comparison of two time vectors while by the classification such a measure is derived from the framewise matches of predicted and true labels. In this work we first construct { based on several resent publications { a comprehensive univariate onset detection algorithm which depends on many free settable parameters. Then, the new multivariate approach also depending on many free parameters is introduced. The parameters of the both onset detection strategies are optimized for online and offline cases by utilizing an appropriate validation technique. The main funding is that the multivariate strategy outperforms the univariate one significantly regarding the F-measure. Furthermore, the multivariate approach seems to be especially beneficial in online case since it requires only the halve of the future signal information comparing to the best setting of the univariate onset detection