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Papers by André Stuhlsatz

Transactions of the Institute of Measurement and Control, 2011

The concentration of organic acids in anaerobic digesters is one of the most critical parameters ... more The concentration of organic acids in anaerobic digesters is one of the most critical parameters for monitoring and advanced control of anaerobic digestion processes. Thus, a reliable online-measurement system is absolutely necessary. A novel approach to obtaining these measurements indirectly and online using UV/vis spectroscopic probes, in conjunction with powerful pattern recognition methods, is presented in this paper. An UV/vis spectroscopic probe from S::CAN is used in combination with a custom-built dilution system to monitor the absorption of fully fermented sludge at a spectrum from 200 to 750 nm. Advanced pattern recognition methods are then used to map the non-linear relationship between measured absorption spectra to laboratory measurements of organic acid concentrations. Linear discriminant analysis, generalized discriminant analysis (GerDA), support vector machines (SVM), relevance vector machines, random forest and neural networks are investigated for this purpose and their performance compared. To validate the approach, online measurements have been taken at a full-scale 1.3-MW industrial biogas plant. Results show that whereas some of the methods considered do not yield satisfactory results, accurate prediction of organic acid concentration ranges can be obtained with both GerDA and SVM-based classifiers, with classification rates in excess of 87% achieved on test data.

In this paper, we sketch a multi-sensor framework for estimating an object's pose. For this purpo... more In this paper, we sketch a multi-sensor framework for estimating an object's pose. For this purpose, we combine an inertial measurement unit, consisting of gyroscopes, accelerometers and magnetometers, with a visual pose estimation algorithm applied on images obtained from a low cost web cam. Using all measurements of the different sensors, we state the equations to model the various sensors and give an idea of how to fuse the different measurements by using the Extended Kalman filter.

Communications in Computer and Information Science, 2010

The concentration of organic acids in anaerobic digesters is one of the most critical parameters ... more The concentration of organic acids in anaerobic digesters is one of the most critical parameters for monitoring and advanced control of anaerobic digestion processes, making a reliable online-measurement system absolutely necessary. This paper introduces a novel approach to obtaining these measurements indirectly and online using UV/vis spectroscopic probes, in conjunction with powerful pattern recognition methods. An UV/vis spectroscopic probe from S::CAN is used in combination with a custom-built dilution system to monitor the absorption of fully fermented sludge at a spectrum from 200nm to 750nm. Advanced pattern recognition methods, like LDA, Generalized Discriminant Analysis (GerDA) and SVM, are then used to map the measured absorption spectra to laboratory measurements of organic acid concentrations. The validation of the approach at a full-scale 1.3MW industrial biogas plant shows that more than 87% of the measured organic acid concentrations can be detected correctly.

The optimization of full-scale biogas plant operation is of great importance to make biomass a co... more The optimization of full-scale biogas plant operation is of great importance to make biomass a competitive source of renewable energy. The implementation of innovative control and optimization algorithms, such as Nonlinear Model Predictive Control, requires an online estimation of operating states of biogas plants. This state estimation allows for optimal control and operating decisions according to the actual state of a plant. In this paper such a state estimator is developed using a calibrated simulation model of a full-scale biogas plant, which is based on the Anaerobic Digestion Model No.1. The use of advanced pattern recognition methods shows that model states can be predicted from basic online measurements such as biogas production, CH4 and CO2 content in the biogas, pH value and substrate feed volume of known substrates. The machine learning methods used are trained and evaluated using synthetic data created with the biogas plant model simulating over a wide range of possible...

Advances in Soft Computing, 2007

Summary. In this paper, we present a new implementable learning algorithm for the general nonline... more Summary. In this paper, we present a new implementable learning algorithm for the general nonlinear binary classification problem. The suggested algorithm abides the maximum margin philosophy, and learns a decision function from the set of all finite linear combinations of continuous dierentiable basis functions. This enables the use of a much more flexible function class than the one usually employed

2008 Seventh International Conference on Machine Learning and Applications, 2008

This paper presents a new implementable algorithm for solving the Lipschitz classifier that is a ... more This paper presents a new implementable algorithm for solving the Lipschitz classifier that is a generalization of the maximum margin concept from Hilbert to Banach spaces. In contrast to the Support Vector Machine approach, our algorithm is free to use any finite family of continuously differentiable functions which linearly compose the decision function. Nevertheless, robustness properties are maintained due to a maximizing margin. To obtain a useful algorithm, the inherent difficult problem is formulated in a convex Semi-infinite Program. Using this new formulation, we develop a duality result enabling us to solve the original problem iteratively as a finite sequence of constrained quadratic programming problems over a convex hull of matrices. We compare the performance of the Lipschitz classifier algorithm with state-of-the-art machine learning methodologies using a benchmark data set as well as a data set randomly generated from Gaussian mixtures.

2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2011

Deep Neural Networks (DNNs) denote multilayer artificial neural networks with more than one hidde... more Deep Neural Networks (DNNs) denote multilayer artificial neural networks with more than one hidden layer and millions of free parameters. We propose a Generalized Discriminant Analysis (GerDA) based on DNNs to learn discriminative features of low dimension optimized with respect to a fast classification from a large set of acoustic features for emotion recognition. On nine frequently used emotional speech corpora, we compare the performance of GerDA features and their subsequent linear classification with previously reported benchmarks obtained using the same set of acoustic features classified by Support Vector Machines (SVMs). Our results impressively show that low-dimensional GerDA features capture hidden information from the acoustic features leading to a significantly raised unweighted average recall and considerably raised weighted average recall.

IEEE Transactions on Neural Networks and Learning Systems, 2012

We present an approach to feature extraction that is a generalization of the classical linear dis... more We present an approach to feature extraction that is a generalization of the classical linear discriminant analysis (LDA) on the basis of deep neural networks (DNNs). As for LDA, discriminative features generated from independent Gaussian class conditionals are assumed. This modeling has the advantages that the intrinsic dimensionality of the feature space is bounded by the number of classes and that the optimal discriminant function is linear. Unfortunately, linear transformations are insufficient to extract optimal discriminative features from arbitrarily distributed raw measurements. The generalized discriminant analysis (GerDA) proposed in this paper uses nonlinear transformations that are learnt by DNNs in a semisupervised fashion. We show that the feature extraction based on our approach displays excellent performance on real-world recognition and detection tasks, such as handwritten digit recognition and face detection. In a series of experiments, we evaluate GerDA features with respect to dimensionality reduction, visualization, classification, and detection. Moreover, we show that GerDA DNNs can preprocess truly high-dimensional input data to low-dimensional representations that facilitate accurate predictions even if simple linear predictors or measures of similarity are used.

The 2010 International Joint Conference on Neural Networks (IJCNN), 2010

We propose a framework for optimizing Deep Neural Networks (DNN) with the objective of learning l... more We propose a framework for optimizing Deep Neural Networks (DNN) with the objective of learning lowdimensional discriminative features from high-dimensional complex patterns.

2010 20th International Conference on Pattern Recognition, 2010

Constructing a recognition system based on raw measurements for different objects usually require... more Constructing a recognition system based on raw measurements for different objects usually requires expert knowledge of domain specific data preprocessing, feature extraction, and classifier design. We seek to simplify this process in a way that can be applied without any knowledge about the data domain and the specific properties of different classification algorithms. That is, a recognition system should be simple to construct and simple to operate in practical applications. For this, we have developed a nonlinear feature extractor for high-dimensional complex patterns, using Deep Neural Networks (DNN). Trained partly supervised and unsupervised, the DNN effectively implements a nonlinear discriminant analysis based on a Fisher criterion in a feature space of very low dimensions. Our experiments show that the automatically extracted features work very well with simple linear discriminants, while the recognition rates improve only minimally if more sophisticated classification algorithms like Support Vector Machines (SVM) are used instead.

Water Science & Technology, 2012

In this paper, we introduce an approach to improve the recognition performance of a Hidden Markov... more In this paper, we introduce an approach to improve the recognition performance of a Hidden Markov Model (HMM) based monophone recognizer using Support Vector Machines (SVMs). We developed and examined a method for re-scoring the HMM recognizer hypotheses by SVMs in a phoneme recognition framework. Compared to a stand-alone HMM system, an improvement of 9.2% was reached on the TIMIT

As the recognition of emotion from speech has matured to a degree where it becomes applicable in ... more As the recognition of emotion from speech has matured to a degree where it becomes applicable in real-life settings, it is time for a realistic view on obtainable performances. Most studies tend to overestimation in this respect: acted data is often used rather than spontaneous data, results are reported on pre-selected prototypical data, and true speaker disjunctive partitioning is still less common than simple cross-validation. Even speaker disjunctive evaluation can give only little insight into the generalization ability of today's emotion recognition engines since training and test data used for system development usually tend to be similar as far as recording conditions, noise overlay, language, and types of emotions are concerned. A considerably more realistic impression can be gathered by inter-set evaluation: we therefore show results employing six standard databases in a cross-corpora evaluation experiment which could also be helpful to learn about chances to add resources for training and overcome the typical sparseness in the field. To better cope with the observed high variances, different types of normalization are investigated. 1.8 k individual evaluations in total indicate the crucial performance inferiority of inter-to intra-corpus testing.

Transactions of the Institute of Measurement and Control, 2011

The concentration of organic acids in anaerobic digesters is one of the most critical parameters ... more The concentration of organic acids in anaerobic digesters is one of the most critical parameters for monitoring and advanced control of anaerobic digestion processes. Thus, a reliable online-measurement system is absolutely necessary. A novel approach to obtaining these measurements indirectly and online using UV/vis spectroscopic probes, in conjunction with powerful pattern recognition methods, is presented in this paper. An UV/vis spectroscopic probe from S::CAN is used in combination with a custom-built dilution system to monitor the absorption of fully fermented sludge at a spectrum from 200 to 750 nm. Advanced pattern recognition methods are then used to map the non-linear relationship between measured absorption spectra to laboratory measurements of organic acid concentrations. Linear discriminant analysis, generalized discriminant analysis (GerDA), support vector machines (SVM), relevance vector machines, random forest and neural networks are investigated for this purpose and their performance compared. To validate the approach, online measurements have been taken at a full-scale 1.3-MW industrial biogas plant. Results show that whereas some of the methods considered do not yield satisfactory results, accurate prediction of organic acid concentration ranges can be obtained with both GerDA and SVM-based classifiers, with classification rates in excess of 87% achieved on test data.

In this paper, we sketch a multi-sensor framework for estimating an object's pose. For this purpo... more In this paper, we sketch a multi-sensor framework for estimating an object's pose. For this purpose, we combine an inertial measurement unit, consisting of gyroscopes, accelerometers and magnetometers, with a visual pose estimation algorithm applied on images obtained from a low cost web cam. Using all measurements of the different sensors, we state the equations to model the various sensors and give an idea of how to fuse the different measurements by using the Extended Kalman filter.

Communications in Computer and Information Science, 2010

The concentration of organic acids in anaerobic digesters is one of the most critical parameters ... more The concentration of organic acids in anaerobic digesters is one of the most critical parameters for monitoring and advanced control of anaerobic digestion processes, making a reliable online-measurement system absolutely necessary. This paper introduces a novel approach to obtaining these measurements indirectly and online using UV/vis spectroscopic probes, in conjunction with powerful pattern recognition methods. An UV/vis spectroscopic probe from S::CAN is used in combination with a custom-built dilution system to monitor the absorption of fully fermented sludge at a spectrum from 200nm to 750nm. Advanced pattern recognition methods, like LDA, Generalized Discriminant Analysis (GerDA) and SVM, are then used to map the measured absorption spectra to laboratory measurements of organic acid concentrations. The validation of the approach at a full-scale 1.3MW industrial biogas plant shows that more than 87% of the measured organic acid concentrations can be detected correctly.

The optimization of full-scale biogas plant operation is of great importance to make biomass a co... more The optimization of full-scale biogas plant operation is of great importance to make biomass a competitive source of renewable energy. The implementation of innovative control and optimization algorithms, such as Nonlinear Model Predictive Control, requires an online estimation of operating states of biogas plants. This state estimation allows for optimal control and operating decisions according to the actual state of a plant. In this paper such a state estimator is developed using a calibrated simulation model of a full-scale biogas plant, which is based on the Anaerobic Digestion Model No.1. The use of advanced pattern recognition methods shows that model states can be predicted from basic online measurements such as biogas production, CH4 and CO2 content in the biogas, pH value and substrate feed volume of known substrates. The machine learning methods used are trained and evaluated using synthetic data created with the biogas plant model simulating over a wide range of possible...

Advances in Soft Computing, 2007

Summary. In this paper, we present a new implementable learning algorithm for the general nonline... more Summary. In this paper, we present a new implementable learning algorithm for the general nonlinear binary classification problem. The suggested algorithm abides the maximum margin philosophy, and learns a decision function from the set of all finite linear combinations of continuous dierentiable basis functions. This enables the use of a much more flexible function class than the one usually employed

2008 Seventh International Conference on Machine Learning and Applications, 2008

This paper presents a new implementable algorithm for solving the Lipschitz classifier that is a ... more This paper presents a new implementable algorithm for solving the Lipschitz classifier that is a generalization of the maximum margin concept from Hilbert to Banach spaces. In contrast to the Support Vector Machine approach, our algorithm is free to use any finite family of continuously differentiable functions which linearly compose the decision function. Nevertheless, robustness properties are maintained due to a maximizing margin. To obtain a useful algorithm, the inherent difficult problem is formulated in a convex Semi-infinite Program. Using this new formulation, we develop a duality result enabling us to solve the original problem iteratively as a finite sequence of constrained quadratic programming problems over a convex hull of matrices. We compare the performance of the Lipschitz classifier algorithm with state-of-the-art machine learning methodologies using a benchmark data set as well as a data set randomly generated from Gaussian mixtures.

2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2011

Deep Neural Networks (DNNs) denote multilayer artificial neural networks with more than one hidde... more Deep Neural Networks (DNNs) denote multilayer artificial neural networks with more than one hidden layer and millions of free parameters. We propose a Generalized Discriminant Analysis (GerDA) based on DNNs to learn discriminative features of low dimension optimized with respect to a fast classification from a large set of acoustic features for emotion recognition. On nine frequently used emotional speech corpora, we compare the performance of GerDA features and their subsequent linear classification with previously reported benchmarks obtained using the same set of acoustic features classified by Support Vector Machines (SVMs). Our results impressively show that low-dimensional GerDA features capture hidden information from the acoustic features leading to a significantly raised unweighted average recall and considerably raised weighted average recall.

IEEE Transactions on Neural Networks and Learning Systems, 2012

We present an approach to feature extraction that is a generalization of the classical linear dis... more We present an approach to feature extraction that is a generalization of the classical linear discriminant analysis (LDA) on the basis of deep neural networks (DNNs). As for LDA, discriminative features generated from independent Gaussian class conditionals are assumed. This modeling has the advantages that the intrinsic dimensionality of the feature space is bounded by the number of classes and that the optimal discriminant function is linear. Unfortunately, linear transformations are insufficient to extract optimal discriminative features from arbitrarily distributed raw measurements. The generalized discriminant analysis (GerDA) proposed in this paper uses nonlinear transformations that are learnt by DNNs in a semisupervised fashion. We show that the feature extraction based on our approach displays excellent performance on real-world recognition and detection tasks, such as handwritten digit recognition and face detection. In a series of experiments, we evaluate GerDA features with respect to dimensionality reduction, visualization, classification, and detection. Moreover, we show that GerDA DNNs can preprocess truly high-dimensional input data to low-dimensional representations that facilitate accurate predictions even if simple linear predictors or measures of similarity are used.

The 2010 International Joint Conference on Neural Networks (IJCNN), 2010

We propose a framework for optimizing Deep Neural Networks (DNN) with the objective of learning l... more We propose a framework for optimizing Deep Neural Networks (DNN) with the objective of learning lowdimensional discriminative features from high-dimensional complex patterns.

2010 20th International Conference on Pattern Recognition, 2010

Constructing a recognition system based on raw measurements for different objects usually require... more Constructing a recognition system based on raw measurements for different objects usually requires expert knowledge of domain specific data preprocessing, feature extraction, and classifier design. We seek to simplify this process in a way that can be applied without any knowledge about the data domain and the specific properties of different classification algorithms. That is, a recognition system should be simple to construct and simple to operate in practical applications. For this, we have developed a nonlinear feature extractor for high-dimensional complex patterns, using Deep Neural Networks (DNN). Trained partly supervised and unsupervised, the DNN effectively implements a nonlinear discriminant analysis based on a Fisher criterion in a feature space of very low dimensions. Our experiments show that the automatically extracted features work very well with simple linear discriminants, while the recognition rates improve only minimally if more sophisticated classification algorithms like Support Vector Machines (SVM) are used instead.

Water Science & Technology, 2012

In this paper, we introduce an approach to improve the recognition performance of a Hidden Markov... more In this paper, we introduce an approach to improve the recognition performance of a Hidden Markov Model (HMM) based monophone recognizer using Support Vector Machines (SVMs). We developed and examined a method for re-scoring the HMM recognizer hypotheses by SVMs in a phoneme recognition framework. Compared to a stand-alone HMM system, an improvement of 9.2% was reached on the TIMIT

As the recognition of emotion from speech has matured to a degree where it becomes applicable in ... more As the recognition of emotion from speech has matured to a degree where it becomes applicable in real-life settings, it is time for a realistic view on obtainable performances. Most studies tend to overestimation in this respect: acted data is often used rather than spontaneous data, results are reported on pre-selected prototypical data, and true speaker disjunctive partitioning is still less common than simple cross-validation. Even speaker disjunctive evaluation can give only little insight into the generalization ability of today's emotion recognition engines since training and test data used for system development usually tend to be similar as far as recording conditions, noise overlay, language, and types of emotions are concerned. A considerably more realistic impression can be gathered by inter-set evaluation: we therefore show results employing six standard databases in a cross-corpora evaluation experiment which could also be helpful to learn about chances to add resources for training and overcome the typical sparseness in the field. To better cope with the observed high variances, different types of normalization are investigated. 1.8 k individual evaluations in total indicate the crucial performance inferiority of inter-to intra-corpus testing.