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- affiliation: University Hamburg, Germany
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Books and Theses
- 2013
[b1]Richard C. Hendriks, Timo Gerkmann
, Jesper Jensen:
DFT-Domain Based Single-Microphone Noise Reduction for Speech Enhancement. Synthesis Lectures on Speech and Audio Processing, Morgan & Claypool Publishers 2013, ISBN 9781627051439
Journal Articles
- 2024
- [j31]Navin Raj Prabhu, Nale Lehmann-Willenbrock, Timo Gerkmann:
End-to-End Label Uncertainty Modeling in Speech Emotion Recognition Using Bayesian Neural Networks and Label Distribution Learning. IEEE Trans. Affect. Comput. 15(2): 579-592 (2024) - [j30]Kristina Tesch, Timo Gerkmann:
Multi-Channel Speech Separation Using Spatially Selective Deep Non-Linear Filters. IEEE ACM Trans. Audio Speech Lang. Process. 32: 542-553 (2024) - [j29]Simon Welker, Henry N. Chapman, Timo Gerkmann:
DriftRec: Adapting Diffusion Models to Blind JPEG Restoration. IEEE Trans. Image Process. 33: 2795-2807 (2024) - 2023
- [j28]Jean-Marie Lemercier, Joachim Thiemann, Raphael Koning, Timo Gerkmann:
A neural network-supported two-stage algorithm for lightweight dereverberation on hearing devices. EURASIP J. Audio Speech Music. Process. 2023(1): 18 (2023) - [j27]Kristina Tesch, Timo Gerkmann:
Insights Into Deep Non-Linear Filters for Improved Multi-Channel Speech Enhancement. IEEE ACM Trans. Audio Speech Lang. Process. 31: 563-575 (2023) - [j26]Huajian Fang, Dennis Becker, Stefan Wermter, Timo Gerkmann:
Integrating Uncertainty Into Neural Network-Based Speech Enhancement. IEEE ACM Trans. Audio Speech Lang. Process. 31: 1587-1600 (2023) - [j25]Julius Richter, Simon Welker, Jean-Marie Lemercier, Bunlong Lay, Timo Gerkmann:
Speech Enhancement and Dereverberation With Diffusion-Based Generative Models. IEEE ACM Trans. Audio Speech Lang. Process. 31: 2351-2364 (2023) - [j24]Jean-Marie Lemercier, Julius Richter, Simon Welker, Timo Gerkmann:
StoRM: A Diffusion-Based Stochastic Regeneration Model for Speech Enhancement and Dereverberation. IEEE ACM Trans. Audio Speech Lang. Process. 31: 2724-2737 (2023) - 2021
- [j23]Kristina Tesch, Timo Gerkmann:
Nonlinear Spatial Filtering in Multichannel Speech Enhancement. IEEE ACM Trans. Audio Speech Lang. Process. 29: 1795-1805 (2021) - [j22]Robert Rehr, Timo Gerkmann:
SNR-Based Features and Diverse Training Data for Robust DNN-Based Speech Enhancement. IEEE ACM Trans. Audio Speech Lang. Process. 29: 1937-1949 (2021) - [j21]Tobias Knopp, Mirco Grosser, Matthias Graeser, Timo Gerkmann, Martin Möddel:
Efficient Joint Estimation of Tracer Distribution and Background Signals in Magnetic Particle Imaging Using a Dictionary Approach. IEEE Trans. Medical Imaging 40(12): 3568-3579 (2021) - 2020
- [j20]Lux Li, Robert Rehr, Patrick Bruns, Timo Gerkmann, Brigitte Röder:
A Survey on Probabilistic Models in Human Perception and Machines. Frontiers Robotics AI 7: 85 (2020) - 2018
- [j19]Robert Rehr, Timo Gerkmann:
On the Importance of Super-Gaussian Speech Priors for Machine-Learning Based Speech Enhancement. IEEE ACM Trans. Audio Speech Lang. Process. 26(2): 357-366 (2018) - [j18]Martin Krawczyk-Becker, Timo Gerkmann:
On Speech Enhancement Under PSD Uncertainty. IEEE ACM Trans. Audio Speech Lang. Process. 26(6): 1140-1149 (2018) - 2017
- [j17]Robert Rehr, Timo Gerkmann:
An Analysis of Adaptive Recursive Smoothing with Applications to Noise PSD Estimation. IEEE ACM Trans. Audio Speech Lang. Process. 25(2): 397-408 (2017) - 2016
- [j16]Martin Krawczyk-Becker, Timo Gerkmann:
Fundamental Frequency Informed Speech Enhancement in a Flexible Statistical Framework. IEEE ACM Trans. Audio Speech Lang. Process. 24(5): 940-951 (2016) - [j15]Martin Krawczyk-Becker, Timo Gerkmann:
On MMSE-Based Estimation of Amplitude and Complex Speech Spectral Coefficients Under Phase-Uncertainty. IEEE ACM Trans. Audio Speech Lang. Process. 24(12): 2251-2262 (2016) - 2015
- [j14]Benjamin Cauchi, Ina Kodrasi, Robert Rehr, Stephan Gerlach, Ante Jukic, Timo Gerkmann, Simon Doclo, Stefan Goetze:
Combination of MVDR beamforming and single-channel spectral processing for enhancing noisy and reverberant speech. EURASIP J. Adv. Signal Process. 2015: 61 (2015) - [j13]Feifei Xiong, Bernd T. Meyer, Niko Moritz, Robert Rehr, Jörn Anemüller, Timo Gerkmann, Simon Doclo, Stefan Goetze:
Front-end technologies for robust ASR in reverberant environments - spectral enhancement-based dereverberation and auditory modulation filterbank features. EURASIP J. Adv. Signal Process. 2015: 70 (2015) - [j12]Timo Gerkmann, Martin Krawczyk-Becker, Jonathan Le Roux:
Phase Processing for Single-Channel Speech Enhancement: History and recent advances. IEEE Signal Process. Mag. 32(2): 55-66 (2015) - [j11]Alexander Schasse, Timo Gerkmann, Rainer Martin, Wolfgang Sörgel, Thomas Pilgrim, Henning Puder:
Two-Stage Filter-Bank System for Improved Single-Channel Noise Reduction in Hearing Aids. IEEE ACM Trans. Audio Speech Lang. Process. 23(2): 383-393 (2015) - [j10]Lin Wang, Timo Gerkmann, Simon Doclo:
Noise Power Spectral Density Estimation Using MaxNSR Blocking Matrix. IEEE ACM Trans. Audio Speech Lang. Process. 23(9): 1493-1508 (2015) - [j9]Ante Jukic, Toon van Waterschoot, Timo Gerkmann, Simon Doclo:
Multi-Channel Linear Prediction-Based Speech Dereverberation With Sparse Priors. IEEE ACM Trans. Audio Speech Lang. Process. 23(9): 1509-1520 (2015) - 2014
- [j8]Martin Krawczyk, Timo Gerkmann:
STFT phase reconstruction in voiced speech for an improved single-channel speech enhancement. IEEE ACM Trans. Audio Speech Lang. Process. 22(12): 1931-1940 (2014) - [j7]Timo Gerkmann:
Bayesian Estimation of Clean Speech Spectral Coefficients Given a Priori Knowledge of the Phase. IEEE Trans. Signal Process. 62(16): 4199-4208 (2014) - 2013
- [j6]Timo Gerkmann, Martin Krawczyk:
MMSE-Optimal Spectral Amplitude Estimation Given the STFT-Phase. IEEE Signal Process. Lett. 20(2): 129-132 (2013) - 2012
- [j5]Richard C. Hendriks, Timo Gerkmann:
Noise Correlation Matrix Estimation for Multi-Microphone Speech Enhancement. IEEE Trans. Speech Audio Process. 20(1): 223-233 (2012) - [j4]Timo Gerkmann, Richard C. Hendriks:
Unbiased MMSE-Based Noise Power Estimation With Low Complexity and Low Tracking Delay. IEEE Trans. Speech Audio Process. 20(4): 1383-1393 (2012) - 2009
- [j3]Timo Gerkmann, Rainer Martin:
On the statistics of spectral amplitudes after variance reduction by temporal cepstrum smoothing and cepstral nulling. IEEE Trans. Signal Process. 57(11): 4165-4174 (2009) - 2008
- [j2]Timo Gerkmann, Colin Breithaupt, Rainer Martin:
Improved A Posteriori Speech Presence Probability Estimation Based on a Likelihood Ratio With Fixed Priors. IEEE Trans. Speech Audio Process. 16(5): 910-919 (2008) - 2007
- [j1]Colin Breithaupt, Timo Gerkmann, Rainer Martin:
Cepstral Smoothing of Spectral Filter Gains for Speech Enhancement Without Musical Noise. IEEE Signal Process. Lett. 14(12): 1036-1039 (2007)
Conference and Workshop Papers
- 2024
- [c106]Bunlong Lay, Jean-Marie Lemercier, Julius Richter, Timo Gerkmann:
Single and Few-Step Diffusion for Generative Speech Enhancement. ICASSP 2024: 626-630 - [c105]Tal Peer, Simon Welker, Johannes Kolhoff, Timo Gerkmann:
A Flexible Online Framework for Projection-Based Stft Phase Retrieval. ICASSP 2024: 846-850 - [c104]Simon Welker, Tal Peer, Henry N. Chapman, Timo Gerkmann:
Live Iterative Ptychography with Projection-Based Algorithms. ICASSP 2024: 2455-2459 - [c103]Navin Raj Prabhu, Bunlong Lay, Simon Welker, Nale Lehmann-Willenbrock, Timo Gerkmann:
EMOCONV-Diff: Diffusion-Based Speech Emotion Conversion for Non-Parallel and in-the-Wild Data. ICASSP 2024: 11651-11655 - [c102]Danilo de Oliveira, Timo Gerkmann:
Distilling Hubert with LSTMs via Decoupled Knowledge Distillation. ICASSP 2024: 11711-11715 - [c101]Huajian Fang, Timo Gerkmann:
Uncertainty-Based Remixing for Unsupervised Domain Adaptation in Deep Speech Enhancement. IWAENC 2024: 45-49 - [c100]Danilo de Oliveira, Eric Grinstein, Patrick A. Naylor, Timo Gerkmann:
LASER: Language-Queried Speech Enhancer. IWAENC 2024: 90-94 - [c99]Eloi Moliner, Jean-Marie Lemercier, Simon Welker, Timo Gerkmann, Vesa Välimäki:
BUDDy: Single-Channel Blind Unsupervised Dereverberation with Diffusion Models. IWAENC 2024: 120-124 - [c98]Alina Mannanova, Kristina Tesch, Jean-Marie Lemercier, Timo Gerkmann:
Meta-Learning For Variable Array Configurations in End-to-End Few-Shot Multichannel Speech Enhancement. IWAENC 2024: 200-204 - [c97]Bunlong Lay, Sebastian Zaczek, Kristina Tesch, Timo Gerkmann:
Robustness of Speech Separation Models for Similar-Pitch Speakers. IWAENC 2024: 225-229 - 2023
- [c96]Huajian Fang, Timo Gerkmann:
Uncertainty Estimation in Deep Speech Enhancement Using Complex Gaussian Mixture Models. ICASSP 2023: 1-5 - [c95]Huajian Fang, Niklas Wittmer, Johannes Twiefel, Stefan Wermter, Timo Gerkmann:
Partially Adaptive Multichannel Joint Reduction of Ego-Noise and Environmental Noise. ICASSP 2023: 1-5 - [c94]Jean-Marie Lemercier, Julius Richter, Simon Welker, Timo Gerkmann:
Analysing Diffusion-based Generative Approaches Versus Discriminative Approaches for Speech Restoration. ICASSP 2023: 1-5 - [c93]Tal Peer, Simon Welker, Timo Gerkmann:
DiffPhase: Generative Diffusion-Based STFT Phase Retrieval. ICASSP 2023: 1-5 - [c92]Julius Richter, Simon Welker, Jean-Marie Lemercier, Bunlong Lay, Tal Peer, Timo Gerkmann:
Speech Signal Improvement Using Causal Generative Diffusion Models. ICASSP 2023: 1-2 - [c91]Kristina Tesch, Timo Gerkmann:
Spatially Selective Deep Non-Linear Filters For Speaker Extraction. ICASSP 2023: 1-5 - [c90]Ehsan Yaghoubi, André Peter Kelm, Timo Gerkmann, Simone Frintrop:
Acoustic and Visual Knowledge Distillation for Contrastive Audio-Visual Localization. ICMI 2023: 15-23 - [c89]Héctor Martel, Julius Richter, Kai Li, Xiaolin Hu, Timo Gerkmann:
Audio-Visual Speech Separation in Noisy Environments with a Lightweight Iterative Model. INTERSPEECH 2023: 1673-1677 - [c88]Danilo de Oliveira, Navin Raj Prabhu, Timo Gerkmann:
Leveraging Semantic Information for Efficient Self-Supervised Emotion Recognition with Audio-Textual Distilled Models. INTERSPEECH 2023: 3632-3636 - [c87]Bunlong Lay, Simon Welker, Julius Richter, Timo Gerkmann:
Reducing the Prior Mismatch of Stochastic Differential Equations for Diffusion-based Speech Enhancement. INTERSPEECH 2023: 3809-3813 - [c86]Jean-Marie Lemercier, Julian Tobergte, Timo Gerkmann:
Extending DNN-based Multiplicative Masking to Deep Subband Filtering for Improved Dereverberation. INTERSPEECH 2023: 4024-4028 - [c85]Jean-Marie Lemercier, Simon Welker, Timo Gerkmann:
Diffusion Posterior Sampling for Informed Single-Channel Dereverberation. WASPAA 2023: 1-5 - 2022
- [c84]Navin Raj Prabhu, Nale Lehmann-Willenbrock, Timo Gerkmann:
Label Uncertainty Modeling and Prediction for Speech Emotion Recognition using t-Distributions. ACII 2022: 1-8 - [c83]Jean-Marie Lemercier, Joachim Thiemann, Raphael Koning, Timo Gerkmann:
Customizable End-To-End Optimization Of Online Neural Network-Supported Dereverberation For Hearing Devices. ICASSP 2022: 171-175 - [c82]Huajian Fang, Tal Peer, Stefan Wermter, Timo Gerkmann:
Integrating Statistical Uncertainty into Neural Network-Based Speech Enhancement. ICASSP 2022: 386-390 - [c81]Simon Welker, Tal Peer, Henry N. Chapman, Timo Gerkmann:
Deep Iterative Phase Retrieval for Ptychography. ICASSP 2022: 1591-1595 - [c80]Julius Richter, Jeanine Liebold, Timo Gerkmann:
Continuous Phoneme Recognition based on Audio-Visual Modality Fusion. IJCNN 2022: 1-8 - [c79]Navin Raj Prabhu, Guillaume Carbajal, Nale Lehmann-Willenbrock, Timo Gerkmann:
End-To-End Label Uncertainty Modeling for Speech-based Arousal Recognition Using Bayesian Neural Networks. INTERSPEECH 2022: 151-155 - [c78]Jean-Marie Lemercier, Joachim Thiemann, Raphael Koning, Timo Gerkmann:
Neural Network-augmented Kalman Filtering for Robust Online Speech Dereverberation in Noisy Reverberant Environments. INTERSPEECH 2022: 226-230 - [c77]Kristina Tesch, Nils-Hendrik Mohrmann, Timo Gerkmann:
On the Role of Spatial, Spectral, and Temporal Processing for DNN-based Non-linear Multi-channel Speech Enhancement. INTERSPEECH 2022: 2908-2912 - [c76]Simon Welker, Julius Richter, Timo Gerkmann:
Speech Enhancement with Score-Based Generative Models in the Complex STFT Domain. INTERSPEECH 2022: 2928-2932 - [c75]Danilo de Oliveira, Tal Peer, Timo Gerkmann:
Efficient Transformer-based Speech Enhancement Using Long Frames and STFT Magnitudes. INTERSPEECH 2022: 2948-2952 - [c74]Tal Peer, Simon Welker, Timo Gerkmann:
Beyond Griffin-LIM: Improved Iterative Phase Retrieval for Speech. IWAENC 2022: 1-5 - [c73]Bunlong Lay, Timo Gerkmann:
Speech Enhancement Regularized by a Speaker Verification Model. MMSP 2022: 1-6 - 2021
- [c72]Huajian Fang, Guillaume Carbajal, Stefan Wermter, Timo Gerkmann:
Joint Reduction of Ego-noise and Environmental Noise with a Partially-adaptive Dictionary. ITG Conference on Speech Communication 2021: 1-5 - [c71]Jean-Marie Lemercier, Leroy Bartel, David Ditter, Timo Gerkmann:
An Integrated Deep Clustering-Based System for Speaker Count Agnostic Speech Separation. ITG Conference on Speech Communication 2021: 1-5 - [c70]Tal Peer, Timo Gerkmann:
Intelligibility Prediction of Speech Reconstructed From Its Magnitude or Phase. ITG Conference on Speech Communication 2021: 1-5 - [c69]Tal Peer, Klaus-Johan Ziegert, Timo Gerkmann:
Plosive Enhancement Using Phase Linearization and Smoothing. ITG Conference on Speech Communication 2021: 1-5 - [c68]Huajian Fang, Guillaume Carbajal, Stefan Wermter, Timo Gerkmann:
Variational Autoencoder for Speech Enhancement with a Noise-Aware Encoder. ICASSP 2021: 676-680 - [c67]Guillaume Carbajal, Julius Richter, Timo Gerkmann:
Guided Variational Autoencoder for Speech Enhancement with a Supervised Classifier. ICASSP 2021: 681-685 - [c66]Danu Caus, Guillaume Carbajal, Timo Gerkmann, Simone Frintrop:
See the Silence: Improving Visual-Only Voice Activity Detection by Optical Flow and RGB Fusion. ICVS 2021: 41-51 - [c65]Tobias Knopp, Mirco Grosser, Matthias Graeser, Timo Gerkmann, Martin Möddel:
Dictionary-Based Background Signal Estimation For Magnetic Particle Imaging. ISBI 2021: 1540-1543 - [c64]Xiaolin Hu, Kai Li, Weiyi Zhang, Yi Luo, Jean-Marie Lemercier, Timo Gerkmann:
Speech Separation Using an Asynchronous Fully Recurrent Convolutional Neural Network. NeurIPS 2021: 22509-22522 - [c63]Guillaume Carbajal, Julius Richter, Timo Gerkmann:
Disentanglement Learning for Variational Autoencoders Applied to Audio-Visual Speech Enhancement. WASPAA 2021: 126-130 - 2020
- [c62]David Ditter, Timo Gerkmann:
A Multi-Phase Gammatone Filterbank for Speech Separation Via Tasnet. ICASSP 2020: 36-40 - [c61]Kristina Tesch, Timo Gerkmann:
Nonlinear Spatial Filtering for Multichannel Speech Enhancement in Inhomogeneous Noise Fields. ICASSP 2020: 196-200 - [c60]Quan Nguyen, Julius Richter, Mikko Lauri, Timo Gerkmann, Simone Frintrop:
Improving mix-and-separate training in audio-visual sound source separation with an object prior. ICPR 2020: 5844-5851 - [c59]Julius Richter, Guillaume Carbajal, Timo Gerkmann:
Speech Enhancement with Stochastic Temporal Convolutional Networks. INTERSPEECH 2020: 4516-4520 - [c58]Hongzhuo Liang, Chuangchuang Zhou, Shuang Li, Xiaojian Ma, Norman Hendrich, Timo Gerkmann, Fuchun Sun, Marcus Stoffel, Jianwei Zhang:
Robust Robotic Pouring using Audition and Haptics. IROS 2020: 10880-10887 - 2019
- [c57]Robert Rehr, Timo Gerkmann:
An Analysis of Noise-aware Features in Combination with the Size and Diversity of Training Data for DNN-based Speech Enhancement. ICASSP 2019: 601-605 - [c56]Kristina Tesch, Robert Rehr, Timo Gerkmann:
On Nonlinear Spatial Filtering in Multichannel Speech Enhancement. INTERSPEECH 2019: 91-95 - [c55]David Ditter, Timo Gerkmann:
Influence of Speaker-Specific Parameters on Speech Separation Systems. INTERSPEECH 2019: 4584-4588 - [c54]Hongzhuo Liang, Shuang Li, Xiaojian Ma, Norman Hendrich, Timo Gerkmann, Fuchun Sun, Jianwei Zhang:
Making Sense of Audio Vibration for Liquid Height Estimation in Robotic Pouring. IROS 2019: 5333-5339 - 2018
- [c53]Robert Rehr, Timo Gerkmann:
Robust DNN-Based Speech Enhancement with Limited Training Data. ITG Symposium on Speech Communication 2018: 1-5 - [c52]Martin Krawczyk-Becker, Timo Gerkmann:
A Study on the Benefits of Phase-Aware Speech Enhancement in Challenging Noise Scenarios. LVA/ICA 2018: 407-416 - [c51]Huy Phan, Martin Krawczyk-Becker, Timo Gerkmann, Alfred Mertins:
Weighted and Multi-Task Loss for Rare Audio Event Detection. ICASSP 2018: 336-340 - [c50]Martin Krawczyk-Becker, Timo Gerkmann:
Nonlinear Speech Enhancement Under Speech PSD Uncertainty. ICASSP 2018: 641-645 - 2017
- [c49]Robert Rehr, Timo Gerkmann:
MixMax Approximation as a Super-Gaussian Log-Spectral Amplitude Estimator for Speech Enhancement. INTERSPEECH 2017: 1983-1987 - 2016
- [c48]Dörte Fischer, Simon Doclo, Emanuël A. P. Habets, Timo Gerkmann:
Combined Single-Microphone Wiener and MVDR Filtering based on Speech Interframe Correlations and Speech Presence Probability. ITG Symposium on Speech Communication 2016: 1-5 - [c47]Robert Rehr, Timo Gerkmann:
A Combination of Pre-Trained Approaches and Generic Methods for an Improved Speech Enhancement. ITG Symposium on Speech Communication 2016: 1-5 - [c46]Dörte Fischer, Timo Gerkmann:
Single-microphone speech enhancement using MVDR filtering and Wiener post-filtering. ICASSP 2016: 201-205 - [c45]Robert Rehr, Timo Gerkmann:
BIAS correction methods for adaptive recursive smoothing with applications in noise PSD estimation. ICASSP 2016: 206-210 - [c44]Benjamin Cauchi, Hamza A. Javed, Timo Gerkmann, Simon Doclo, Stefan Goetze, Patrick A. Naylor:
Perceptual and instrumental evaluation of the perceived level of reverberation. ICASSP 2016: 629-633 - [c43]Christoph Brauer, Timo Gerkmann, Dirk A. Lorenz:
Sparse reconstruction of quantized speech signals. ICASSP 2016: 5940-5944 - [c42]Ante Jukic, Zichao Wang, Toon van Waterschoot, Timo Gerkmann, Simon Doclo:
Constrained multi-channel linear prediction for adaptive speech dereverberation. IWAENC 2016: 1-5 - 2015
- [c41]Benjamin Cauchi, Patrick A. Naylor, Timo Gerkmann, Simon Doclo, Stefan Goetze:
Late reverberant spectral variance estimation using acoustic channel equalization. EUSIPCO 2015: 2481-2485 - [c40]Adam Kuklasinski, Simon Doclo, Timo Gerkmann, Søren Holdt Jensen, Jesper Jensen:
Multi-channel PSD estimators for speech dereverberation - A theoretical and experimental comparison. ICASSP 2015: 91-95 - [c39]Ante Jukic, Nasser Mohammadiha, Toon van Waterschoot, Timo Gerkmann, Simon Doclo:
Multi-channel linear prediction-based speech dereverberation with low-rank power spectrogram approximation. ICASSP 2015: 96-100 - [c38]Martin Krawczyk-Becker, Dörte Fischer, Timo Gerkmann:
Utilizing spectro-temporal correlations for an improved speech presence probability based noise power estimation. ICASSP 2015: 365-369 - [c37]Robert Rehr, Timo Gerkmann:
Cepstral noise subtraction for robust automatic speech recognition. ICASSP 2015: 375-378 - [c36]Sidsel Marie Nørholm, Martin Krawczyk-Becker, Timo Gerkmann, Steven van de Par, Jesper Rindom Jensen, Mads Græsbøll Christensen:
Least squares estimate of the initial phases in STFT based speech enhancement. INTERSPEECH 2015: 1750-1754 - [c35]Ante Jukic, Toon van Waterschoot, Timo Gerkmann, Simon Doclo:
Group sparsity for mimo speech dereverberation. WASPAA 2015: 1-5 - [c34]Martin Krawczyk-Becker, Timo Gerkmann:
MMSE-optimal combination of wiener filtering and harmonic model based speech enhancement in a general framework. WASPAA 2015: 1-5 - [c33]Robert Rehr, Timo Gerkmann:
On the bias of adaptive first-order recursive smoothing. WASPAA 2015: 1-5 - 2014
- [c32]Naveen Kumar Desiraju, Simon Doclo, Timo Gerkmann, Tobias Wolff:
Efficient Multi-Channel Acoustic Echo Cancellation Using Constrained Sparse Filter Updates in the Subband Domain. ITG Symposium on Speech Communication 2014: 1-4 - [c31]Balázs Fodor, Timo Gerkmann:
A speech presence probability estimator based on fixed priors and a heavy-tailed speech model. EUSIPCO 2014: 2305-2309 - [c30]Ante Jukic, Toon van Waterschoot, Timo Gerkmann, Simon Doclo:
Speech dereverberation with multi-channel linear prediction and sparse priors for the desired signal. HSCMA 2014: 23-26 - [c29]Timo Gerkmann:
MMSE-optimal enhancement of complex speech coefficients with uncertain prior knowledge of the clean speech phase. ICASSP 2014: 4478-4482 - [c28]Ina Kodrasi, Timo Gerkmann, Simon Doclo:
Frequency-domain single-channel inverse filtering for speech dereverberation: Theory and practice. ICASSP 2014: 5177-5181 - [c27]Robert Rehr, Martin Krawczyk, Timo Gerkmann:
A posteriori voiced/unvoiced probability estimation based on a sinusoidal model. ICASSP 2014: 6944-6948 - [c26]Balázs Fodor, Timo Gerkmann:
A posteriori speech presence probability estimation based on averaged observations and a super-Gaussian speech model. IWAENC 2014: 11-15 - [c25]Ante Jukic, Toon van Waterschoot, Timo Gerkmann, Simon Doclo:
Speech dereverberation with convolutive transfer function approximation using map and variational deconvolution approaches. IWAENC 2014: 50-54 - [c24]Tobias May, Timo Gerkmann:
Generalization of supervised learning for binary mask estimation. IWAENC 2014: 154-158 - [c23]Stefan Goetze, Anna Warzybok, Ina Kodrasi, Jan Ole Jungmann, Benjamin Cauchi, Jan Rennies, Emanuël A. P. Habets, Alfred Mertins, Timo Gerkmann, Simon Doclo, Birger Kollmeier:
A study on speech quality and speech intelligibility measures for quality assessment of single-channel dereverberation algorithms. IWAENC 2014: 233-237 - [c22]Steffen Kortlang, Stephan Dieter Ewert, Timo Gerkmann:
Single channel noise reduction based on an auditory filterbank. IWAENC 2014: 283-287 - [c21]Anna Warzybok, Ina Kodrasi, Jan Ole Jungmann, Emanuël A. P. Habets, Timo Gerkmann, Alfred Mertins, Simon Doclo, Birger Kollmeier, Stefan Goetze:
Subjective speech quality and speech intelligibility evaluation of single-channel dereverberation algorithms. IWAENC 2014: 332-336 - 2013
- [c20]Richard C. Hendriks, Zekeriya Erkin, Timo Gerkmann:
Privacy preserving distributed beamforming based on homomorphic encryption. EUSIPCO 2013: 1-5 - [c19]Martin Krawczyk, Robert Rehr, Timo Gerkmann:
Phase-sensitive real-time capable speech enhancement under voiced-unvoiced uncertainty. EUSIPCO 2013: 1-5 - [c18]Richard C. Hendriks, Zekeriya Erkin, Timo Gerkmann:
Privacy-preserving distributed speech enhancement forwireless sensor networks by processing in the encrypted domain. ICASSP 2013: 7005-7009 - [c17]Ramón Fernandez Astudillo, Timo Gerkmann:
On the relation between speech corruption models in the spectral and the cepstral domain. ICASSP 2013: 7044-7048 - 2012
- [c16]Timo Gerkmann, Richard C. Hendriks:
Improved mmse-based noise PSD tracking using temporal cepstrum smoothing. ICASSP 2012: 105-108 - [c15]Martin Krawczyk, Timo Gerkmann:
STFT Phase Improvement for Single Channel Speech Enhancement. IWAENC 2012 - [c14]Lin Wang, Timo Gerkmann, Simon Doclo:
Noise PSD Estimation Using Blind Source Separation in a Diffuse Noise Field. IWAENC 2012 - 2011
- [c13]Timo Gerkmann:
Cepstral weighting for speech dereverberation without musical noise. EUSIPCO 2011: 2309-2313 - [c12]Richard C. Hendriks, Timo Gerkmann:
Estimation of the noise correlation matrix. ICASSP 2011: 4740-4743 - [c11]Nasser Mohammadiha, Timo Gerkmann, Arne Leijon:
A new approach for speech enhancement based on a constrained Nonnegative Matrix Factorization. ISPACS 2011: 1-5 - [c10]Jalil Taghia, Timo Gerkmann, Arne Leijon:
Blind source separation of nondisjoint sources in the time-frequency domain with model-based determination of source contribution. ISSPIT 2011: 276-280 - [c9]Nasser Mohammadiha, Timo Gerkmann, Arne Leijon:
A new linear MMSE filter for single channel speech enhancement based on Nonnegative Matrix Factorization. WASPAA 2011: 45-48 - [c8]Timo Gerkmann, Richard C. Hendriks:
Noise power estimation based on the probability of speech presence. WASPAA 2011: 145-148 - 2010
- [c7]Timo Gerkmann, Rainer Martin:
Cepstral Smoothing with Reduced Computational Complexity. Sprachkommunikation 2010: 1-4 - [c6]Anil M. Nagathil, Timo Gerkmann, Rainer Martin:
Musical genre classification based on a highly-resolved cepstral modulation spectrum. EUSIPCO 2010: 462-466 - [c5]Timo Gerkmann, Martin Krawczyk, Rainer Martin:
Speech presence probability estimation based on temporal cepstrum smoothing. ICASSP 2010: 4254-4257 - 2009
- [c4]Timo Gerkmann, Rainer Martin, Derya Dalga:
Multi-microphone maximum a posteriori fundamental frequency estimation in the cepstral domain. ICASSP 2009: 4505-4508 - 2008
- [c3]Colin Breithaupt, Timo Gerkmann, Rainer Martin:
A novel a priori SNR estimation approach based on selective cepstro-temporal smoothing. ICASSP 2008: 4897-4900 - 2006
- [c2]Justinian Rosca, Timo Gerkmann, Doru-Cristian Balcan:
Statistical Inference of Missing Speech Data in the ICA Domain. ICASSP (5) 2006: 617-620 - [c1]Timo Gerkmann, Rainer Martin:
Soft decision combining for dual channel noise reduction. INTERSPEECH 2006
Informal and Other Publications
- 2024
- [i60]Bunlong Lay, Timo Gerkmann:
An Analysis of the Variance of Diffusion-based Speech Enhancement. CoRR abs/2402.00811 (2024) - [i59]Jean-Marie Lemercier, Julius Richter, Simon Welker, Eloi Moliner, Vesa Välimäki, Timo Gerkmann:
Diffusion Models for Audio Restoration. CoRR abs/2402.09821 (2024) - [i58]Eloi Moliner, Jean-Marie Lemercier, Simon Welker, Timo Gerkmann, Vesa Välimäki:
BUDDy: Single-Channel Blind Unsupervised Dereverberation with Diffusion Models. CoRR abs/2405.04272 (2024) - [i57]Danilo de Oliveira, Simon Welker, Julius Richter, Timo Gerkmann:
The PESQetarian: On the Relevance of Goodhart's Law for Speech Enhancement. CoRR abs/2406.03460 (2024) - [i56]Julius Richter, Yi-Chiao Wu, Steven Krenn, Simon Welker, Bunlong Lay, Shinji Watanabe, Alexander Richard, Timo Gerkmann:
EARS: An Anechoic Fullband Speech Dataset Benchmarked for Speech Enhancement and Dereverberation. CoRR abs/2406.06185 (2024) - [i55]Bunlong Lay, Sebastian Zaczek, Kristina Tesch, Timo Gerkmann:
Robustness of Speech Separation Models for Similar-pitch Speakers. CoRR abs/2407.15749 (2024) - [i54]Jean-Marie Lemercier, Eloi Moliner, Simon Welker, Vesa Välimäki, Timo Gerkmann:
Unsupervised Blind Joint Dereverberation and Room Acoustics Estimation with Diffusion Models. CoRR abs/2408.07472 (2024) - [i53]Navin Raj Prabhu, Maria Tsfasman, Catharine Oertel, Timo Gerkmann, Nale Lehmann-Willenbrock:
Dynamics of Collective Group Affect: Group-level Annotations and the Multimodal Modeling of Convergence and Divergence. CoRR abs/2409.08578 (2024) - [i52]Julius Richter, Danilo de Oliveira, Timo Gerkmann:
Investigating Training Objectives for Generative Speech Enhancement. CoRR abs/2409.10753 (2024) - 2023
- [i51]Bunlong Lay, Simon Welker, Julius Richter, Timo Gerkmann:
Reducing the Prior Mismatch of Stochastic Differential Equations for Diffusion-based Speech Enhancement. CoRR abs/2302.14748 (2023) - [i50]Jean-Marie Lemercier, Julian Tobergte, Timo Gerkmann:
Extending DNN-based Multiplicative Masking to Deep Subband Filtering for Improved Dereverberation. CoRR abs/2303.00529 (2023) - [i49]Julius Richter, Simon Welker, Jean-Marie Lemercier, Bunlong Lay, Tal Peer, Timo Gerkmann:
Speech Signal Improvement Using Causal Generative Diffusion Models. CoRR abs/2303.08674 (2023) - [i48]Huajian Fang, Niklas Wittmer, Johannes Twiefel, Stefan Wermter, Timo Gerkmann:
Partially Adaptive Multichannel Joint Reduction of Ego-noise and Environmental Noise. CoRR abs/2303.15042 (2023) - [i47]Kristina Tesch, Timo Gerkmann:
Multi-channel Speech Separation Using Spatially Selective Deep Non-linear Filters. CoRR abs/2304.12023 (2023) - [i46]Huajian Fang, Dennis Becker, Stefan Wermter, Timo Gerkmann:
Integrating Uncertainty into Neural Network-based Speech Enhancement. CoRR abs/2305.08744 (2023) - [i45]Danilo de Oliveira, Navin Raj Prabhu, Timo Gerkmann:
Leveraging Semantic Information for Efficient Self-Supervised Emotion Recognition with Audio-Textual Distilled Models. CoRR abs/2305.19184 (2023) - [i44]Héctor Martel, Julius Richter, Kai Li, Xiaolin Hu, Timo Gerkmann:
Audio-Visual Speech Separation in Noisy Environments with a Lightweight Iterative Model. CoRR abs/2306.00160 (2023) - [i43]Julius Richter, Simone Frintrop, Timo Gerkmann:
Audio-Visual Speech Enhancement with Score-Based Generative Models. CoRR abs/2306.01432 (2023) - [i42]Navin Raj Prabhu, Nale Lehmann-Willenbrock, Timo Gerkmann:
In-the-wild Speech Emotion Conversion Using Disentangled Self-Supervised Representations and Neural Vocoder-based Resynthesis. CoRR abs/2306.01916 (2023) - [i41]Danilo de Oliveira, Julius Richter, Jean-Marie Lemercier, Tal Peer, Timo Gerkmann:
On the Behavior of Intrusive and Non-intrusive Speech Enhancement Metrics in Predictive and Generative Settings. CoRR abs/2306.03014 (2023) - [i40]Jean-Marie Lemercier, Simon Welker, Timo Gerkmann:
Diffusion Posterior Sampling for Informed Single-Channel Dereverberation. CoRR abs/2306.12286 (2023) - [i39]Jean-Marie Lemercier, Joachim Thiemann, Raphael Koning, Timo Gerkmann:
Wind Noise Reduction with a Diffusion-based Stochastic Regeneration Model. CoRR abs/2306.12867 (2023) - [i38]Tal Peer, Simon Welker, Johannes Kolhoff, Timo Gerkmann:
A Flexible Online Framework for Projection-Based STFT Phase Retrieval. CoRR abs/2309.07043 (2023) - [i37]Navin Raj Prabhu, Bunlong Lay, Simon Welker, Nale Lehmann-Willenbrock, Timo Gerkmann:
EMOCONV-DIFF: Diffusion-based Speech Emotion Conversion for Non-parallel and In-the-wild Data. CoRR abs/2309.07828 (2023) - [i36]Bunlong Lay, Jean-Marie Lemercier, Julius Richter, Timo Gerkmann:
Single and Few-step Diffusion for Generative Speech Enhancement. CoRR abs/2309.09677 (2023) - [i35]Danilo de Oliveira, Timo Gerkmann:
Distilling HuBERT with LSTMs via Decoupled Knowledge Distillation. CoRR abs/2309.09920 (2023) - 2022
- [i34]Simon Welker, Tal Peer, Henry N. Chapman, Timo Gerkmann:
Deep Iterative Phase Retrieval for Ptychography. CoRR abs/2202.10573 (2022) - [i33]Huajian Fang, Tal Peer, Stefan Wermter, Timo Gerkmann:
Integrating Statistical Uncertainty into Neural Network-Based Speech Enhancement. CoRR abs/2203.02288 (2022) - [i32]Tal Peer, Timo Gerkmann:
Phase-Aware Deep Speech Enhancement: It's All About The Frame Length. CoRR abs/2203.16222 (2022) - [i31]Simon Welker, Julius Richter, Timo Gerkmann:
Speech Enhancement with Score-Based Generative Models in the Complex STFT Domain. CoRR abs/2203.17004 (2022) - [i30]Jean-Marie Lemercier, Joachim Thiemann, Raphael Koning, Timo Gerkmann:
Customizable End-to-end Optimization of Online Neural Network-supported Dereverberation for Hearing Devices. CoRR abs/2204.02694 (2022) - [i29]Jean-Marie Lemercier, Joachim Thiemann, Raphael Koning, Timo Gerkmann:
Neural Network-augmented Kalman Filtering for Robust Online Speech Dereverberation in Noisy Reverberant Environments. CoRR abs/2204.02741 (2022) - [i28]Jean-Marie Lemercier, Joachim Thiemann, Raphael Koning, Timo Gerkmann:
End-To-End Optimization of Online Neural Network-supported Two-Stage Dereverberation for Hearing Devices. CoRR abs/2204.02978 (2022) - [i27]Tal Peer, Simon Welker, Timo Gerkmann:
Beyond Griffin-Lim: Improved Iterative Phase Retrieval for Speech. CoRR abs/2205.05496 (2022) - [i26]Kristina Tesch, Nils-Hendrik Mohrmann, Timo Gerkmann:
On the Role of Spatial, Spectral, and Temporal Processing for DNN-based Non-linear Multi-channel Speech Enhancement. CoRR abs/2206.11181 (2022) - [i25]Danilo de Oliveira, Tal Peer, Timo Gerkmann:
Efficient Transformer-based Speech Enhancement Using Long Frames and STFT Magnitudes. CoRR abs/2206.11703 (2022) - [i24]Kristina Tesch, Timo Gerkmann:
Insights into Deep Non-linear Filters for Improved Multi-channel Speech Enhancement. CoRR abs/2206.13310 (2022) - [i23]Navin Raj Prabhu, Nale Lehmann-Willenbrock, Timo Gerkmann:
Label Uncertainty Modeling and Prediction for Speech Emotion Recognition using t-Distributions. CoRR abs/2207.12135 (2022) - [i22]Julius Richter, Simon Welker, Jean-Marie Lemercier, Bunlong Lay, Timo Gerkmann:
Speech Enhancement and Dereverberation with Diffusion-based Generative Models. CoRR abs/2208.05830 (2022) - [i21]Navin Raj Prabhu, Nale Lehmann-Willenbrock, Timo Gerkmann:
End-to-End Label Uncertainty Modeling in Speech Emotion Recognition using Bayesian Neural Networks and Label Distribution Learning. CoRR abs/2209.15449 (2022) - [i20]Jean-Marie Lemercier, Julius Richter, Simon Welker, Timo Gerkmann:
Analysing Diffusion-based Generative Approaches versus Discriminative Approaches for Speech Restoration. CoRR abs/2211.02397 (2022) - [i19]Kristina Tesch, Timo Gerkmann:
Spatially Selective Deep Non-linear Filters for Speaker Extraction. CoRR abs/2211.02420 (2022) - [i18]Tal Peer, Simon Welker, Timo Gerkmann:
DiffPhase: Generative Diffusion-based STFT Phase Retrieval. CoRR abs/2211.04332 (2022) - [i17]Simon Welker, Henry N. Chapman, Timo Gerkmann:
DriftRec: Adapting diffusion models to blind image restoration tasks. CoRR abs/2211.06757 (2022) - [i16]Huajian Fang, Timo Gerkmann:
Uncertainty Estimation in Deep Speech Enhancement Using Complex Gaussian Mixture Models. CoRR abs/2212.04831 (2022) - [i15]Jean-Marie Lemercier, Julius Richter, Simon Welker, Timo Gerkmann:
StoRM: A Diffusion-based Stochastic Regeneration Model for Speech Enhancement and Dereverberation. CoRR abs/2212.11851 (2022) - 2021
- [i14]Guillaume Carbajal, Julius Richter, Timo Gerkmann:
Guided Variational Autoencoder for Speech Enhancement With a Supervised Classifier. CoRR abs/2102.06454 (2021) - [i13]Huajian Fang, Guillaume Carbajal, Stefan Wermter, Timo Gerkmann:
Variational Autoencoder for Speech Enhancement with a Noise-Aware Encoder. CoRR abs/2102.08706 (2021) - [i12]Kristina Tesch, Timo Gerkmann:
Nonlinear Spatial Filtering in Multichannel Speech Enhancement. CoRR abs/2104.11033 (2021) - [i11]Guillaume Carbajal, Julius Richter, Timo Gerkmann:
Disentanglement Learning for Variational Autoencoders Applied to Audio-Visual Speech Enhancement. CoRR abs/2105.08970 (2021) - [i10]Navin Raj Prabhu, Guillaume Carbajal, Nale Lehmann-Willenbrock, Timo Gerkmann:
End-to-end label uncertainty modeling for speech emotion recognition using Bayesian neural networks. CoRR abs/2110.03299 (2021) - [i9]Xiaolin Hu, Kai Li, Weiyi Zhang, Yi Luo, Jean-Marie Lemercier, Timo Gerkmann:
Speech Separation Using an Asynchronous Fully Recurrent Convolutional Neural Network. CoRR abs/2112.02321 (2021) - 2020
- [i8]Hongzhuo Liang, Chuangchuang Zhou, Shuang Li, Xiaojian Ma, Norman Hendrich, Timo Gerkmann, Fuchun Sun, Jianwei Zhang:
Robust Robotic Pouring using Audition and Haptics. CoRR abs/2003.00342 (2020) - [i7]Robert Rehr, Timo Gerkmann:
SNR-Based Features and Diverse Training Data for Robust DNN-Based Speech Enhancement. CoRR abs/2004.03512 (2020) - [i6]Thilo Fryen, Manfred Eppe, Phuong D. H. Nguyen, Timo Gerkmann, Stefan Wermter:
Reinforcement Learning with Time-dependent Goals for Robotic Musicians. CoRR abs/2011.05715 (2020) - 2019
- [i5]Hongzhuo Liang, Shuang Li, Xiaojian Ma, Norman Hendrich, Timo Gerkmann, Jianwei Zhang:
Making Sense of Audio Vibration for Liquid Height Estimation in Robotic Pouring. CoRR abs/1903.00650 (2019) - [i4]David Ditter, Timo Gerkmann:
A Multi-Phase Gammatone Filterbank for Speech Separation via TasNet. CoRR abs/1910.11615 (2019) - 2017
- [i3]Robert Rehr, Timo Gerkmann:
On the Importance of Super-Gaussian Speech Priors for Pre-Trained Speech Enhancement. CoRR abs/1703.05003 (2017) - [i2]Huy Phan, Martin Krawczyk-Becker, Timo Gerkmann, Alfred Mertins:
DNN and CNN with Weighted and Multi-task Loss Functions for Audio Event Detection. CoRR abs/1708.03211 (2017) - [i1]Robert Rehr, Timo Gerkmann:
Improving the Generalizability of Deep Neural Network Based Speech Enhancement. CoRR abs/1709.02175 (2017)
Coauthor Index
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