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ORCIDPaul M. Baggenstoss
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2020 – today
- 2024
[c32]Paul M. Baggenstoss:
Projected Belief Networks with Discriminative Alignment for Classifying Marine Mammals. EUSIPCO 2024: 201-205- [i11]Paul M. Baggenstoss, Kevin Wilkinghoff, Felix Govaers, Frank Kurth:
Projected Belief Networks With Discriminative Alignment for Acoustic Event Classification: Rivaling State of the Art CNNs. CoRR abs/2401.11199 (2024) - [i10]Paul M. Baggenstoss:
On Maximum Entropy Linear Feature Inversion. CoRR abs/2407.14166 (2024) - 2023
- [c31]Paul M. Baggenstoss, Kevin Wilkinghoff
:
Novel Generative Classifier for Acoustic Events. EUSIPCO 2023: 196-200 - [c30]Paul M. Baggenstoss:
Improved Auto-Encoding Using Deterministic Projected Belief Networks and Compound Activation Functions. EUSIPCO 2023: 1250-1254 - [c29]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:
Evaluation of Robustness Metrics for Defense of Machine Learning Systems. ICMCIS 2023: 1-12 - [i9]Paul M. Baggenstoss:
Improved Auto-Encoding using Deterministic Projected Belief Networks. CoRR abs/2309.07481 (2023) - [i8]Paul M. Baggenstoss, Felix Govaers:
A Comparison of PDF Projection with Normalizing Flows and SurVAE. CoRR abs/2311.14412 (2023) - 2022
- [j24]Paul M. Baggenstoss, Steven Kay:
Nonlinear Dimension Reduction by PDF Estimation. IEEE Trans. Signal Process. 70: 1493-1505 (2022) - [c28]Paul M. Baggenstoss:
Trainable Compound Activation Functions for Machine Learning. EUSIPCO 2022: 1382-1386 - [c27]Paul M. Baggenstoss, Frank Kurth:
Using the Projected Belief Network at High Dimensions. EUSIPCO 2022: 1526-1530 - [i7]Paul M. Baggenstoss:
Trainable Compound Activation Functions for Machine Learning. CoRR abs/2204.12920 (2022) - [i6]Paul M. Baggenstoss:
Using the Projected Belief Network at High Dimensions. CoRR abs/2204.12922 (2022) - 2021
- [j23]Paul M. Baggenstoss:
Discriminative Alignment of Projected Belief Networks. IEEE Signal Process. Lett. 28: 1963-1967 (2021) - [c26]Paul M. Baggenstoss:
New Restricted Boltzmann Machines and Deep Belief Networks for Audio Classification. ITG Conference on Speech Communication 2021: 1-5 - [c25]Paul M. Baggenstoss, Karl-Heinz Frommolt, Olaf Jahn, Frank Kurth:
Separation of Bird Calls and DOA estimation using a 4-Microphone Array. EUSIPCO 2021: 166-170 - [c24]Felix Govaers, Paul M. Baggenstoss:
On a Detection Method of Adversarial Samples for Deep Neural Networks. FUSION 2021: 1-5 - [i5]Paul M. Baggenstoss:
Maximum Entropy Auto-Encoding. CoRR abs/2104.07448 (2021) - 2020
- [c23]Paul M. Baggenstoss:
The Projected Belief Network Classifier: both Generative and Discriminative. EUSIPCO 2020: 795-799 - [c22]Paul M. Baggenstoss:
A Neural Network Based on First Principles. ICASSP 2020: 4002-4006 - [i4]Paul M. Baggenstoss:
A Neural Network Based on First Principles. CoRR abs/2002.07469 (2020) - [i3]Paul M. Baggenstoss:
The Projected Belief Network Classfier : both Generative and Discriminative. CoRR abs/2008.06434 (2020)
2010 – 2019
- 2019
- [j22]Paul M. Baggenstoss:
On the Duality Between Belief Networks and Feed-Forward Neural Networks. IEEE Trans. Neural Networks Learn. Syst. 30(1): 190-200 (2019) - [c21]Paul M. Baggenstoss:
Applications of Projected Belief Networks (PBN). EUSIPCO 2019: 1-5 - [c20]Paul M. Baggenstoss, Mark Springer, Marc Oispuu, Frank Kurth:
Efficient Phase-Based Acoustic Tracking of Drones using a Microphone Array. EUSIPCO 2019: 1-5 - 2018
- [j21]Paul M. Baggenstoss:
Beyond Moments: Extending the Maximum Entropy Principle to Feature Distribution Constraints. Entropy 20(9): 650 (2018) - [c19]Kevin Wilkinghoff, Paul M. Baggenstoss, Alessia Cornaggia-Urrigshardt, Frank Kurth:
Robust Speaker Identification by Fusing Classification Scores with a Neural Network. ITG Symposium on Speech Communication 2018: 1-5 - [c18]Paul M. Baggenstoss:
Acoustic Event Classification Using Multi-Resolution HMM. EUSIPCO 2018: 972-976 - 2017
- [j20]Paul M. Baggenstoss:
Uniform Manifold Sampling (UMS): Sampling the Maximum Entropy PDF. IEEE Trans. Signal Process. 65(9): 2455-2470 (2017) - [c17]Paul M. Baggenstoss:
Evaluating the RBM without integration using PDF projection. EUSIPCO 2017: 828-832 - [c16]Paul M. Baggenstoss, Kevin Wilkinghoff, Frank Kurth:
Glottal mixture model (GLOMM) for speaker identification on telephone channels. EUSIPCO 2017: 2734-2738 - [c15]Bo Tang, Paul M. Baggenstoss, Haibo He:
Kernel-based generative learning in distortion feature space. SSCI 2017: 1-8 - 2016
- [j19]Bo Tang, Steven Kay, Haibo He, Paul M. Baggenstoss:
EEF: Exponentially Embedded Families With Class-Specific Features for Classification. IEEE Signal Process. Lett. 23(7): 969-973 (2016) - [j18]Paul M. Baggenstoss, Brian F. Harrison:
Class-specific model mixtures for the classification of acoustic time series. IEEE Trans. Aerosp. Electron. Syst. 52(4): 1937-1952 (2016) - [j17]Bo Tang, Haibo He, Paul M. Baggenstoss, Steven Kay:
A Bayesian Classification Approach Using Class-Specific Features for Text Categorization. IEEE Trans. Knowl. Data Eng. 28(6): 1602-1606 (2016) - [c14]Paul M. Baggenstoss:
Combining the glottal mixture model (GLOMM) with UBM for speaker recognition. EUSIPCO 2016: 2156-2160 - [c13]Paul M. Baggenstoss:
Maximum entropy feature fusion. FUSION 2016: 1163-1169 - [i2]Bo Tang, Steven Kay, Haibo He, Paul M. Baggenstoss:
EEF: Exponentially Embedded Families with Class-Specific Features for Classification. CoRR abs/1605.03631 (2016) - [i1]Bo Tang, Paul M. Baggenstoss, Haibo He:
Kernel-based Generative Learning in Distortion Feature Space. CoRR abs/1606.06377 (2016) - 2015
- [j16]Satish Madhogaria, Paul M. Baggenstoss, Marek Schikora, Wolfgang Koch, Daniel Cremers:
Car detection by fusion of HOG and causal MRF. IEEE Trans. Aerosp. Electron. Syst. 51(1): 575-590 (2015) - [j15]Paul M. Baggenstoss:
Maximum Entropy PDF Design Using Feature Density Constraints: Applications in Signal Processing. IEEE Trans. Signal Process. 63(11): 2815-2825 (2015) - [c12]Paul M. Baggenstoss:
Derivative-augmented features as a dynamic model for time-series. EUSIPCO 2015: 958-962 - [c11]Paul M. Baggenstoss:
Class-specific model mixtures for the classification of time-series. EUSIPCO 2015: 2341-2345 - 2014
- [j14]Paul M. Baggenstoss:
Recursive Decimation/Interpolation for ML Chirp Parameter Estimation. IEEE Trans. Aerosp. Electron. Syst. 50(1): 445-455 (2014) - [c10]Paul M. Baggenstoss:
Optimal Detection and Classification of Diverse Short-duration Signals. IC2E 2014: 534-539 - 2013
- [j13]Paul M. Baggenstoss:
Specular Decomposition of Active Sonar Returns using Combined Waveforms. IEEE Trans. Aerosp. Electron. Syst. 49(4): 2509-2521 (2013) - 2012
- [j12]Paul M. Baggenstoss:
On the Equivalence of Hanning-Weighted and Overlapped Analysis Windows Using Different Window Sizes. IEEE Signal Process. Lett. 19(1): 27-30 (2012) - 2011
- [j11]Paul M. Baggenstoss:
Two-Dimensional Hidden Markov Model for Classification of Continuous-Valued Noisy Vector Fields. IEEE Trans. Aerosp. Electron. Syst. 47(2): 1073-1080 (2011) - 2010
- [j10]Paul M. Baggenstoss:
A multi-resolution hidden Markov model using class-specific features. IEEE Trans. Signal Process. 58(10): 5165-5177 (2010) - [c9]Virginia Estellers, Paul M. Baggenstoss, Jean-Philippe Thiran:
Class-specific classifiers in audio-visual speech recognition. EUSIPCO 2010: 1998-2002
2000 – 2009
- 2008
- [c8]Paul M. Baggenstoss:
Iterated class-specific subspaces for speaker-dependent phoneme classification. EUSIPCO 2008: 1-5 - [c7]Paul M. Baggenstoss:
A multi-resolution hidden Markov model using class-specific features. EUSIPCO 2008: 1-5 - 2004
- [j9]Paul M. Baggenstoss:
Image Distortion Analysis Using Polynomial Series Expansion. IEEE Trans. Pattern Anal. Mach. Intell. 26(11): 1438-1451 (2004) - [c6]Thomas Beierholm, Paul M. Baggenstoss:
Speech Music Discrimination Using Class-Specific Features. ICPR (2) 2004: 379-382 - 2003
- [j8]Paul M. Baggenstoss:
The PDF projection theorem and the class-specific method. IEEE Trans. Signal Process. 51(3): 672-685 (2003) - [c5]Paul M. Baggenstoss:
A New Optimal Classifier Architecture to Aviod the Dimensionality Curse. IbPRIA 2003: 70-79 - 2002
- [c4]Paul M. Baggenstoss:
The Chain-Rule Processor: Optimal Classification Through Signal Processing. ICPR (1) 2002: 230-234 - 2001
- [j7]Paul M. Baggenstoss:
A modified Baum-Welch algorithm for hidden Markov models with multiple observation spaces. IEEE Trans. Speech Audio Process. 9(4): 411-416 (2001) - [j6]Steven M. Kay, Albert H. Nuttall, Paul M. Baggenstoss:
Multidimensional probability density function approximations for detection, classification, and model order selection. IEEE Trans. Signal Process. 49(10): 2240-2252 (2001) - 2000
- [c3]Paul M. Baggenstoss:
A modified Baum-Welch algorithm for hidden Markov models with multiple observation spaces. ICASSP 2000: 717-720 - [c2]Paul M. Baggenstoss, Heinrich Niemann:
A Theoretically Optimal Probabilistic Classifier Using Class-Specific Features. ICPR 2000: 2763-2768
1990 – 1999
- 1999
- [j5]Paul M. Baggenstoss:
Class-specific feature sets in classification. IEEE Trans. Signal Process. 47(12): 3428-3432 (1999) - [c1]Paul M. Baggenstoss, Tod Luginbuhl:
An E-M algorithm for joint model estimation. ICASSP 1999: 1825-1828 - 1995
- [j4]Paul M. Baggenstoss, Steven M. Kay:
Detection of broadband planewaves in the presence of Gaussian noise of unknown covariance: asymptotically optimum tests using the 2-D autoregressive noise model. IEEE Trans. Signal Process. 43(4): 950-966 (1995) - 1993
- [j3]Paul M. Baggenstoss, Ramdas Kumaresan:
On the estimation of rational transfer functions from samples of the power spectrum. IEEE Trans. Signal Process. 41(3): 1431-1435 (1993) - 1992
- [j2]Paul M. Baggenstoss, Steven M. Kay:
An adaptive detector for deterministic signals in noise of unknown spectra using the Rao test. IEEE Trans. Signal Process. 40(6): 1460-1468 (1992) - 1991
- [j1]Paul M. Baggenstoss, Steven M. Kay:
On estimating the angle parameters of an exponential signal at high SNR. IEEE Trans. Signal Process. 39(5): 1203-1205 (1991)
Coauthor Index
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last updated on 2024-11-07 21:29 CET by the dblp team
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