Paul M. Baggenstoss

According to our database, Paul M. Baggenstoss authored at least 59 papers between 1991 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Projected Belief Networks With Discriminative Alignment for Acoustic Event Classification: Rivaling State of the Art CNNs.

Paul M. Baggenstoss, Kevin Wilkinghoff, Felix Govaers, Frank Kurth

CoRR, 2024

2023

A Comparison of PDF Projection with Normalizing Flows and SurVAE.

Paul M. Baggenstoss, Felix Govaers

CoRR, 2023

Improved Auto-Encoding using Deterministic Projected Belief Networks.

Paul M. Baggenstoss

CoRR, 2023

Evaluation of Robustness Metrics for Defense of Machine Learning Systems.

J. DeMarchi, R. Rijken, J. Melrose, B. Madahar, Giorgio Fumera, Fabio Roli, Emanuele Ledda, M. Aktas, Frank Kurth, Paul M. Baggenstoss, B. Pelzer, L. Kanestad

Proceedings of the International Conference on Military Communications and Information Systems, 2023

Novel Generative Classifier for Acoustic Events.

Paul M. Baggenstoss, Kevin Wilkinghoff

Proceedings of the 31st European Signal Processing Conference, 2023

Improved Auto-Encoding Using Deterministic Projected Belief Networks and Compound Activation Functions.

Paul M. Baggenstoss

Proceedings of the 31st European Signal Processing Conference, 2023

2022

Nonlinear Dimension Reduction by PDF Estimation.

Paul M. Baggenstoss, Steven Kay

IEEE Trans. Signal Process., 2022

Using the Projected Belief Network at High Dimensions.

Paul M. Baggenstoss

CoRR, 2022

Using the Projected Belief Network at High Dimensions.

Paul M. Baggenstoss, Frank Kurth

Proceedings of the 30th European Signal Processing Conference, 2022

Trainable Compound Activation Functions for Machine Learning.

Paul M. Baggenstoss

Proceedings of the 30th European Signal Processing Conference, 2022

2021

Discriminative Alignment of Projected Belief Networks.

Paul M. Baggenstoss

IEEE Signal Process. Lett., 2021

Maximum Entropy Auto-Encoding.

Paul M. Baggenstoss

CoRR, 2021

On a Detection Method of Adversarial Samples for Deep Neural Networks.

Felix Govaers, Paul M. Baggenstoss

Proceedings of the 24th IEEE International Conference on Information Fusion, 2021

Separation of Bird Calls and DOA estimation using a 4-Microphone Array.

Paul M. Baggenstoss, Karl-Heinz Frommolt, Olaf Jahn, Frank Kurth

Proceedings of the 29th European Signal Processing Conference, 2021

New Restricted Boltzmann Machines and Deep Belief Networks for Audio Classification.

Paul M. Baggenstoss

Proceedings of the 14th ITG Conference on Speech Communication, online, September 29, 2021

2020

The Projected Belief Network Classfier : both Generative and Discriminative.

Paul M. Baggenstoss

CoRR, 2020

A Neural Network Based on First Principles.

Paul M. Baggenstoss

Proceedings of the 2020 IEEE International Conference on Acoustics, 2020

The Projected Belief Network Classifier: both Generative and Discriminative.

Paul M. Baggenstoss

Proceedings of the 28th European Signal Processing Conference, 2020

2019

On the Duality Between Belief Networks and Feed-Forward Neural Networks.

Paul M. Baggenstoss

IEEE Trans. Neural Networks Learn. Syst., 2019

Efficient Phase-Based Acoustic Tracking of Drones using a Microphone Array.

Paul M. Baggenstoss, Mark Springer, Marc Oispuu, Frank Kurth

Proceedings of the 27th European Signal Processing Conference, 2019

Applications of Projected Belief Networks (PBN).

Paul M. Baggenstoss

Proceedings of the 27th European Signal Processing Conference, 2019

2018

Beyond Moments: Extending the Maximum Entropy Principle to Feature Distribution Constraints.

Paul M. Baggenstoss

Entropy, 2018

Acoustic Event Classification Using Multi-Resolution HMM.

Paul M. Baggenstoss

Proceedings of the 26th European Signal Processing Conference, 2018

Robust Speaker Identification by Fusing Classification Scores with a Neural Network.

Kevin Wilkinghoff, Paul M. Baggenstoss, Alessia Cornaggia-Urrigshardt, Frank Kurth

Proceedings of the 13th ITG Symposium on Speech Communication, 2018

2017

Uniform Manifold Sampling (UMS): Sampling the Maximum Entropy PDF.

Paul M. Baggenstoss

IEEE Trans. Signal Process., 2017

Kernel-based generative learning in distortion feature space.

Bo Tang, Paul M. Baggenstoss, Haibo He

Proceedings of the 2017 IEEE Symposium Series on Computational Intelligence, 2017

Glottal mixture model (GLOMM) for speaker identification on telephone channels.

Paul M. Baggenstoss, Kevin Wilkinghoff, Frank Kurth

Proceedings of the 25th European Signal Processing Conference, 2017

Evaluating the RBM without integration using PDF projection.

Paul M. Baggenstoss

Proceedings of the 25th European Signal Processing Conference, 2017

2016

A Bayesian Classification Approach Using Class-Specific Features for Text Categorization.

Bo Tang, Haibo He, Paul M. Baggenstoss, Steven Kay

IEEE Trans. Knowl. Data Eng., 2016

Class-specific model mixtures for the classification of acoustic time series.

Paul M. Baggenstoss, Brian F. Harrison

IEEE Trans. Aerosp. Electron. Syst., 2016

EEF: Exponentially Embedded Families With Class-Specific Features for Classification.

Bo Tang, Steven Kay, Haibo He, Paul M. Baggenstoss

IEEE Signal Process. Lett., 2016

Maximum entropy feature fusion.

Paul M. Baggenstoss

Proceedings of the 19th International Conference on Information Fusion, 2016

Combining the glottal mixture model (GLOMM) with UBM for speaker recognition.

Paul M. Baggenstoss

Proceedings of the 24th European Signal Processing Conference, 2016

2015

Maximum Entropy PDF Design Using Feature Density Constraints: Applications in Signal Processing.

Paul M. Baggenstoss

IEEE Trans. Signal Process., 2015

Car detection by fusion of HOG and causal MRF.

Satish Madhogaria, Paul M. Baggenstoss, Marek Schikora, Wolfgang Koch, Daniel Cremers

IEEE Trans. Aerosp. Electron. Syst., 2015

Class-specific model mixtures for the classification of time-series.

Paul M. Baggenstoss

Proceedings of the 23rd European Signal Processing Conference, 2015

Derivative-augmented features as a dynamic model for time-series.

Paul M. Baggenstoss

Proceedings of the 23rd European Signal Processing Conference, 2015

2014

Recursive Decimation/Interpolation for ML Chirp Parameter Estimation.

Paul M. Baggenstoss

IEEE Trans. Aerosp. Electron. Syst., 2014

Optimal Detection and Classification of Diverse Short-duration Signals.

Paul M. Baggenstoss

Proceedings of the 2014 IEEE International Conference on Cloud Engineering, 2014

2013

Specular Decomposition of Active Sonar Returns using Combined Waveforms.

Paul M. Baggenstoss

IEEE Trans. Aerosp. Electron. Syst., 2013

2012

On the Equivalence of Hanning-Weighted and Overlapped Analysis Windows Using Different Window Sizes.

Paul M. Baggenstoss

IEEE Signal Process. Lett., 2012

2011

Two-Dimensional Hidden Markov Model for Classification of Continuous-Valued Noisy Vector Fields.

Paul M. Baggenstoss

IEEE Trans. Aerosp. Electron. Syst., 2011

2010

A multi-resolution hidden Markov model using class-specific features.

Paul M. Baggenstoss

IEEE Trans. Signal Process., 2010

Class-specific classifiers in audio-visual speech recognition.

Virginia Estellers, Paul M. Baggenstoss, Jean-Philippe Thiran

Proceedings of the 18th European Signal Processing Conference, 2010

2008

Iterated class-specific subspaces for speaker-dependent phoneme classification.

Paul M. Baggenstoss

Proceedings of the 2008 16th European Signal Processing Conference, 2008

2004

Image Distortion Analysis Using Polynomial Series Expansion.

Paul M. Baggenstoss

IEEE Trans. Pattern Anal. Mach. Intell., 2004

Speech Music Discrimination Using Class-Specific Features.

Thomas Beierholm, Paul M. Baggenstoss

Proceedings of the 17th International Conference on Pattern Recognition, 2004

2003

The PDF projection theorem and the class-specific method.

Paul M. Baggenstoss

IEEE Trans. Signal Process., 2003

A New Optimal Classifier Architecture to Aviod the Dimensionality Curse.

Paul M. Baggenstoss

Proceedings of the Pattern Recognition and Image Analysis, First Iberian Conference, 2003

2002

The Chain-Rule Processor: Optimal Classification Through Signal Processing.

Paul M. Baggenstoss

Proceedings of the 16th International Conference on Pattern Recognition, 2002

2001

Multidimensional probability density function approximations for detection, classification, and model order selection.

Steven M. Kay, Albert H. Nuttall, Paul M. Baggenstoss

IEEE Trans. Signal Process., 2001

A modified Baum-Welch algorithm for hidden Markov models with multiple observation spaces.

Paul M. Baggenstoss

IEEE Trans. Speech Audio Process., 2001

2000

A Theoretically Optimal Probabilistic Classifier Using Class-Specific Features.

Paul M. Baggenstoss, Heinrich Niemann

Proceedings of the 15th International Conference on Pattern Recognition, 2000

1999

Class-specific feature sets in classification.

Paul M. Baggenstoss

IEEE Trans. Signal Process., 1999

An E-M algorithm for joint model estimation.

Paul M. Baggenstoss, Tod Luginbuhl

Proceedings of the 1999 IEEE International Conference on Acoustics, 1999

1995

Detection of broadband planewaves in the presence of Gaussian noise of unknown covariance: asymptotically optimum tests using the 2-D autoregressive noise model.

Paul M. Baggenstoss, Steven M. Kay

IEEE Trans. Signal Process., 1995

1993

On the estimation of rational transfer functions from samples of the power spectrum.

Paul M. Baggenstoss, Ramdas Kumaresan

IEEE Trans. Signal Process., 1993

1992

An adaptive detector for deterministic signals in noise of unknown spectra using the Rao test.

Paul M. Baggenstoss, Steven M. Kay

IEEE Trans. Signal Process., 1992

1991

On estimating the angle parameters of an exponential signal at high SNR.

Paul M. Baggenstoss, Steven M. Kay

IEEE Trans. Signal Process., 1991