On estimating the angle parameters of an exponential signal at high SNR. - Paul M. Baggenstoss; Steven M. Kay (IEEE Trans. Signal Process., 1991)

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)

On the estimation of rational transfer functions from samples of the power spectrum. - Paul M. Baggenstoss; Ramdas Kumaresan (IEEE Trans. Signal Process., 1993)

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)

An E-M algorithm for joint model estimation. - Paul M. Baggenstoss; Tod Luginbuhl (ICASSP, 1999)

Class-specific feature sets in classification. - Paul M. Baggenstoss (IEEE Trans. Signal Process., 1999)

A Theoretically Optimal Probabilistic Classifier Using Class-Specific Features. - Paul M. Baggenstoss; Heinrich Niemann (ICPR, 2000)

A modified Baum-Welch algorithm for hidden Markov models with multiple observation spaces. - Paul M. Baggenstoss (IEEE Trans. Speech Audio Process., 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)

The Chain-Rule Processor: Optimal Classification Through Signal Processing. - Paul M. Baggenstoss (ICPR (1), 2002)

A New Optimal Classifier Architecture to Aviod the Dimensionality Curse. - Paul M. Baggenstoss (IbPRIA, 2003)

The PDF projection theorem and the class-specific method. - Paul M. Baggenstoss (IEEE Trans. Signal Process., 2003)

Speech Music Discrimination Using Class-Specific Features. - Thomas Beierholm; Paul M. Baggenstoss (ICPR (2), 2004)

Image Distortion Analysis Using Polynomial Series Expansion. - Paul M. Baggenstoss (IEEE Trans. Pattern Anal. Mach. Intell., 2004)

Iterated class-specific subspaces for speaker-dependent phoneme classification. - Paul M. Baggenstoss (EUSIPCO, 2008)

Class-specific classifiers in audio-visual speech recognition. - Virginia Estellers; Paul M. Baggenstoss; Jean-Philippe Thiran (EUSIPCO, 2010)

A multi-resolution hidden Markov model using class-specific features. - Paul M. Baggenstoss (IEEE Trans. Signal Process., 2010)

Two-Dimensional Hidden Markov Model for Classification of Continuous-Valued Noisy Vector Fields. - Paul M. Baggenstoss (IEEE Trans. Aerosp. Electron. Syst., 2011)

On the Equivalence of Hanning-Weighted and Overlapped Analysis Windows Using Different Window Sizes. - Paul M. Baggenstoss (IEEE Signal Process. Lett., 2012)

Specular Decomposition of Active Sonar Returns using Combined Waveforms. - Paul M. Baggenstoss (IEEE Trans. Aerosp. Electron. Syst., 2013)

Optimal Detection and Classification of Diverse Short-duration Signals. - Paul M. Baggenstoss (IC2E, 2014)

Recursive Decimation/Interpolation for ML Chirp Parameter Estimation. - Paul M. Baggenstoss (IEEE Trans. Aerosp. Electron. Syst., 2014)

Derivative-augmented features as a dynamic model for time-series. - Paul M. Baggenstoss (EUSIPCO, 2015)

Class-specific model mixtures for the classification of time-series. - Paul M. Baggenstoss (EUSIPCO, 2015)

Car detection by fusion of HOG and causal MRF. - Satish Madhogaria; Paul M. Baggenstoss; Marek Schikora; Wolfgang Koch 001; Daniel Cremers (IEEE Trans. Aerosp. Electron. Syst., 2015)

Maximum Entropy PDF Design Using Feature Density Constraints: Applications in Signal Processing. - Paul M. Baggenstoss (IEEE Trans. Signal Process., 2015)

Combining the glottal mixture model (GLOMM) with UBM for speaker recognition. - Paul M. Baggenstoss (EUSIPCO, 2016)

Maximum entropy feature fusion. - Paul M. Baggenstoss (FUSION, 2016)

EEF: Exponentially Embedded Families With Class-Specific Features for Classification. - Bo Tang 011; Steven Kay; Haibo He; Paul M. Baggenstoss (IEEE Signal Process. Lett., 2016)

Class-specific model mixtures for the classification of acoustic time series. - Paul M. Baggenstoss; Brian F. Harrison (IEEE Trans. Aerosp. Electron. Syst., 2016)

A Bayesian Classification Approach Using Class-Specific Features for Text Categorization. - Bo Tang 011; Haibo He; Paul M. Baggenstoss; Steven Kay (IEEE Trans. Knowl. Data Eng., 2016)

Evaluating the RBM without integration using PDF projection. - Paul M. Baggenstoss (EUSIPCO, 2017)

Glottal mixture model (GLOMM) for speaker identification on telephone channels. - Paul M. Baggenstoss; Kevin Wilkinghoff; Frank Kurth (EUSIPCO, 2017)

Kernel-based generative learning in distortion feature space. - Bo Tang 011; Paul M. Baggenstoss; Haibo He (SSCI, 2017)

Uniform Manifold Sampling (UMS): Sampling the Maximum Entropy PDF. - Paul M. Baggenstoss (IEEE Trans. Signal Process., 2017)

Robust Speaker Identification by Fusing Classification Scores with a Neural Network. - Kevin Wilkinghoff; Paul M. Baggenstoss; Alessia Cornaggia-Urrigshardt; Frank Kurth (ITG Symposium on Speech Communication, 2018)

Acoustic Event Classification Using Multi-Resolution HMM. - Paul M. Baggenstoss (EUSIPCO, 2018)

Beyond Moments: Extending the Maximum Entropy Principle to Feature Distribution Constraints. - Paul M. Baggenstoss (Entropy, 2018)

Applications of Projected Belief Networks (PBN). - Paul M. Baggenstoss (EUSIPCO, 2019)

Efficient Phase-Based Acoustic Tracking of Drones using a Microphone Array. - Paul M. Baggenstoss; Mark Springer; Marc Oispuu; Frank Kurth (EUSIPCO, 2019)

On the Duality Between Belief Networks and Feed-Forward Neural Networks. - Paul M. Baggenstoss (IEEE Trans. Neural Networks Learn. Syst., 2019)

The Projected Belief Network Classifier: both Generative and Discriminative. - Paul M. Baggenstoss (EUSIPCO, 2020)

A Neural Network Based on First Principles. - Paul M. Baggenstoss (ICASSP, 2020)

The Projected Belief Network Classfier : both Generative and Discriminative. - Paul M. Baggenstoss (CoRR, 2020)

New Restricted Boltzmann Machines and Deep Belief Networks for Audio Classification. - Paul M. Baggenstoss (ITG Conference on Speech Communication, 2021)

Separation of Bird Calls and DOA estimation using a 4-Microphone Array. - Paul M. Baggenstoss; Karl-Heinz Frommolt; Olaf Jahn; Frank Kurth (EUSIPCO, 2021)

On a Detection Method of Adversarial Samples for Deep Neural Networks. - Felix Govaers; Paul M. Baggenstoss (FUSION, 2021)

Maximum Entropy Auto-Encoding. - Paul M. Baggenstoss (CoRR, 2021)

Discriminative Alignment of Projected Belief Networks. - Paul M. Baggenstoss (IEEE Signal Process. Lett., 2021)

Trainable Compound Activation Functions for Machine Learning. - Paul M. Baggenstoss (EUSIPCO, 2022)

Using the Projected Belief Network at High Dimensions. - Paul M. Baggenstoss; Frank Kurth (EUSIPCO, 2022)

Nonlinear Dimension Reduction by PDF Estimation. - Paul M. Baggenstoss; Steven Kay (IEEE Trans. Signal Process., 2022)

Improved Auto-Encoding Using Deterministic Projected Belief Networks and Compound Activation Functions. - Paul M. Baggenstoss (EUSIPCO, 2023)

Novel Generative Classifier for Acoustic Events. - Paul M. Baggenstoss; Kevin Wilkinghoff (EUSIPCO, 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; Metin Aktas; Frank Kurth; Paul M. Baggenstoss; Björn Pelzer; L. Kanestad (ICMCIS, 2023)

Improved Auto-Encoding using Deterministic Projected Belief Networks. - Paul M. Baggenstoss (CoRR, 2023)

A Comparison of PDF Projection with Normalizing Flows and SurVAE. - Paul M. Baggenstoss; Felix Govaers (CoRR, 2023)

Projected Belief Networks with Discriminative Alignment for Classifying Marine Mammals. - Paul M. Baggenstoss (EUSIPCO, 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)

On Maximum Entropy Linear Feature Inversion. - Paul M. Baggenstoss (CoRR, 2024)