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Martin Heckmann - IEEE Xplore Author Profile
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Martin Heckmann

Also published under: M. Heckmann

Publications
26
Citations
275
Publications by Year
20002020
Co-Authors:
Show All Co-Authors (43)
Image of author Martin Heckmann

Martin Heckmann

Also published under: M. Heckmann

Affiliation

Honda Research Institute Europe GmbH
Offenbach am Main
Offenbach, Germany

Publication Topics

Biography

Martin Heckmann studied electrical engineering at the Universität Karlsruhe, Karlsruhe, Germany, and the Institut National des Sciences Appliquées, Lyon, France. He received the Dipl. Ing. degree and the Ph.D. degree in electrical engineering from Universität Karlsruhe in 1997 and 2003, respectively.,In 1998, he was with Carl Zeiss Meditec, Dublin, CA. While working as a Research Assistant at the Universität Karlsruhe from 1998 to 2003, he was engaged in the Collaborative Research Center Humanoid Robots at the Universität Karlsruhe, and a Visiting Researcher at Gipsa-Lab, Grenoble, France, in the framework of the EC research program SPeech, HEaring and Recognition. Since 2004, he has been a Senior Scientist at Honda Research Institute Europ...
Publications
26
Citations
275
Publications by Year
20002020
Co-Authors:
Show All Co-Authors (43)
Author's Published Works

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A Survey of Personalization for Advanced Driver Assistance Systems

Martina Hasenjäger;Martin Heckmann;Heiko Wersing

IEEE Transactions on Intelligent Vehicles
Year: 2020 | Volume: 5, Issue: 2 | Journal Article |
Cited by: Papers (97)
The field of advanced driver assistance systems (ADAS) has matured towards more and more complex assistance functions, applied with wider scope and a strongly increasing user base due to wider market penetration. To deal with such a large variety of usage conditions and patterns, personalization methods have been developed to ensure optimal user experience. In this article we review current approa...Show More

A Survey of Personalization for Advanced Driver Assistance Systems

Martina Hasenjäger;Martin Heckmann;Heiko Wersing

Year: 2020 | Volume: 5, Issue: 2 | Journal Article |

Analysis of a Speech-Based Intersection Assistant in Real Urban Traffic

Dennis Orth;Nico Steinhardt;Bram Bolder;Mark Dunn;Dorothea Kolossa;Martin Heckmann

2018 21st International Conference on Intelligent Transportation Systems (ITSC)
Year: 2018 | Conference Paper |
Cited by: Papers (2)
Previously, we have presented a speech-based intersection assistant prototype. The system is activated on-demand by the driver and gives afterwards, via speech, information on suitable gaps between the traffic vehicles approaching from the right. It is comparable to a front seat passenger which helps in the maneuver decision for an intended turn left. This system has assumed a more or less constan...Show More

Analysis of a Speech-Based Intersection Assistant in Real Urban Traffic

Dennis Orth;Nico Steinhardt;Bram Bolder;Mark Dunn;Dorothea Kolossa;Martin Heckmann

Year: 2018 | Conference Paper |

A Speech-Based On-Demand Intersection Assistant Prototype

Dennis Orth;Bram Bolder;Nico Steinhardt;Mark Dunn;Dorothea Kolossa;Martin Heckmann

2018 IEEE Intelligent Vehicles Symposium (IV)
Year: 2018 | Conference Paper |
Cited by: Papers (3)
We have recently proposed a speech-based on- demand intersection assistant which helps the driver to handle urban intersections by informing him of the traffic situation on the right hand side and recommending suitable gaps in traffic. In a previous user study, conducted in a simulator, we could show that the system is in general well accepted and preferred by drivers compared to driving without a...Show More

A Speech-Based On-Demand Intersection Assistant Prototype

Dennis Orth;Bram Bolder;Nico Steinhardt;Mark Dunn;Dorothea Kolossa;Martin Heckmann

Year: 2018 | Conference Paper |

Predicting driver left-turn behavior from few training samples using a maximum a posteriori method

Dennis Orth;Dorothea Kolossa;Martin Heckmann

2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC)
Year: 2017 | Conference Paper |
Cited by: Papers (4)
In this work, we introduce a novel maximum a posteriori (MAP) method, which can predict driver left-turn behavior from only a few training samples. For the prediction of the driver behavior in this scenario we utilize the so-called critical gap. It signifies how large a gap minimally has to be for the driver to accept it and take the turn. The latter is especially important for the personalization...Show More

Predicting driver left-turn behavior from few training samples using a maximum a posteriori method

Dennis Orth;Dorothea Kolossa;Martin Heckmann

Year: 2017 | Conference Paper |

A maximum likelihood method for driver-specific critical-gap estimation

Dennis Orth;Dorothea Kolossa;Milton Sarria Paja;Kersten Schaller;Andreas Pech;Martin Heckmann

2017 IEEE Intelligent Vehicles Symposium (IV)
Year: 2017 | Conference Paper |
Cited by: Papers (18)
In this work, we introduce a maximum likelihood (ML) method to estimate the smallest accepted gap of a specific driver, the so-called critical gap. Previous methods, like Troutbeck's or Raff's method, are well known and widely used but require a consistently behaving driver, which is usually not met. The methods will be investigated for the personalization of an intersection assistant, which we ar...Show More

A maximum likelihood method for driver-specific critical-gap estimation

Dennis Orth;Dorothea Kolossa;Milton Sarria Paja;Kersten Schaller;Andreas Pech;Martin Heckmann

Year: 2017 | Conference Paper |

Environmentally robust audio-visual speaker identification

Lea Schönherr;Dennis Orth;Martin Heckmann;Dorothea Kolossa

2016 IEEE Spoken Language Technology Workshop (SLT)
Year: 2016 | Conference Paper |
Cited by: Papers (9)
To improve the accuracy of audio-visual speaker identification, we propose a new approach, which achieves an optimal combination of the different modalities on the score level. We use the i-vector method for the acoustics and the local binary pattern (LBP) for the visual speaker recognition. Regarding the input data of both modalities, multiple confidence measures are utilized to calculate an opti...Show More

Environmentally robust audio-visual speaker identification

Lea Schönherr;Dennis Orth;Martin Heckmann;Dorothea Kolossa

Year: 2016 | Conference Paper |

Comparing speaker independent and speaker adapted classification for word prominence detection

Andrea Schnall;Martin Heckmann

2016 IEEE Spoken Language Technology Workshop (SLT)
Year: 2016 | Conference Paper |
Prosodic cues are an important part of human communication. One of these cues is the word prominence which is used to e.g. highlight important information. Since individual speakers use different ways of expressing prominence, it is not easily extracted and incorporated in a dialog system. As a consequence, up to date prominence only plays a marginal role in human-machine communication. In this pa...Show More

Comparing speaker independent and speaker adapted classification for word prominence detection

Andrea Schnall;Martin Heckmann

Year: 2016 | Conference Paper |

Rider skill identification by probabilistic segmentation into motorcycle maneuver primitives

N. Magiera;H. Janssen;M. Heckmann;H. Winner

2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC)
Year: 2016 | Conference Paper |
Cited by: Papers (3)
As a result of the static and dynamic instabilities of a Powered-Two-Wheeler, the rider performs a highly demanding control task. Rider safety strongly depends on the individual abilities and skills of the rider. To improve the riders' skill level and reduce riding errors, safety trainings are well established. Additionally, safety systems and recently also advanced rider assistance systems help t...Show More

Rider skill identification by probabilistic segmentation into motorcycle maneuver primitives

N. Magiera;H. Janssen;M. Heckmann;H. Winner

Year: 2016 | Conference Paper |

Evaluation of optical flow field features for the detection of word prominence in a human-machine interaction scenario

Andrea Schnall;Martin Heckmann

2015 International Joint Conference on Neural Networks (IJCNN)
Year: 2015 | Conference Paper |
Cited by: Papers (3)
In this paper we investigate optical flow field features for the automatic labeling of word prominence. Visual motion is a rich source of information. Modifying the articulatory parameters to raise the prominence of a segment of an utterance, is usually accompanied by a stronger movement of mouth and head compared to a non-prominent segment. One way to describe such motion is to use optical flow f...Show More

Evaluation of optical flow field features for the detection of word prominence in a human-machine interaction scenario

Andrea Schnall;Martin Heckmann

Year: 2015 | Conference Paper |

Robust spectro-temporal speech features with model-based distribution equalization

Samuel K. Ngouoko M;Martin Heckmann;Britta Wrede

2013 14th International Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS)
Year: 2013 | Conference Paper |
Previously, we applied a distribution equalization on our HIerarchical Spectro-Temporal (HIST) features using distributions estimated from histogram of one or several utterances. Although a performance increase could be observed in both cases, we noticed low performance improvement when estimating the distribution only from one utterance. The aim here is to determine a parametric distribution from...Show More

Robust spectro-temporal speech features with model-based distribution equalization

Samuel K. Ngouoko M;Martin Heckmann;Britta Wrede

Year: 2013 | Conference Paper |

Online data-driven fault detection for robotic systems

Raphael Golombek;Sebastian Wrede;Marc Hanheide;Martin Heckmann

2011 IEEE/RSJ International Conference on Intelligent Robots and Systems
Year: 2011 | Conference Paper |
Cited by: Papers (11)
In this paper we demonstrate the online applicability of the fault detection and diagnosis approach which we previously developed and published in [1]. In our former work we showed that a purely data driven fault detection approach can be successfully built based on monitored inter-component communication data of a robotic system and used for a-posteriori fault detection. Here we propose an extens...Show More

Online data-driven fault detection for robotic systems

Raphael Golombek;Sebastian Wrede;Marc Hanheide;Martin Heckmann

Year: 2011 | Conference Paper |

Pitch extraction in Human-Robot interaction

Martin Heckmann;Frank Joublin;Kazuhiro Nakadai

2010 IEEE/RSJ International Conference on Intelligent Robots and Systems
Year: 2010 | Conference Paper |
Cited by: Papers (2)
We present a system for real-time fundamental frequency, i. e. pitch, extraction on a humanoid robot. The system extracts pitch using an 8 channel microphone array mounted on the Honda humanoid robot in a realistic Human-Robot interaction scenario. The main building blocks of the system are a multi-channel signal enhancement followed by robust pitch extraction and tracking. The signal enhancement ...Show More

Pitch extraction in Human-Robot interaction

Martin Heckmann;Frank Joublin;Kazuhiro Nakadai

Year: 2010 | Conference Paper |

Learning a probabilistic self-awareness model for robotic systems

Raphael Golombek;Sebastian Wrede;Marc Hanheide;Martin Heckmann

2010 IEEE/RSJ International Conference on Intelligent Robots and Systems
Year: 2010 | Conference Paper |
Cited by: Papers (8)
In order to address the problem of failure detection in the robotics domain, we present in this contribution a so-called self-awareness model, based on the system's internal data exchange and the inherent dynamics of inter-component communication. The model is strongly data driven and provides an anomaly detector for robotics systems both applicable in-situ at runtime as well as a-posteriori in po...Show More

Learning a probabilistic self-awareness model for robotic systems

Raphael Golombek;Sebastian Wrede;Marc Hanheide;Martin Heckmann

Year: 2010 | Conference Paper |

Multiple sequence alignment based bootstrapping for improved incremental word learning

Irene Ayllón Clemente;Martin Heckmann;Gerhard Sagerer;Frank Joublin

2010 IEEE International Conference on Acoustics, Speech and Signal Processing
Year: 2010 | Conference Paper |
Cited by: Papers (1)
We investigate incremental word learning with few training examples in a Hidden Markov Model (HMM) framework suitable for an interactive learning scenario with little prior knowledge. When using only a few training examples the initialization of the models is a crucial step. In the bootstrapping approach proposed, an unsupervised initialization of the parameters is performed, followed by the retra...Show More

Multiple sequence alignment based bootstrapping for improved incremental word learning

Irene Ayllón Clemente;Martin Heckmann;Gerhard Sagerer;Frank Joublin

Year: 2010 | Conference Paper |

Combining Auditory Preprocessing and Bayesian Estimation for Robust Formant Tracking

Claudius Glaser;Martin Heckmann;Frank Joublin;Christian Goerick

IEEE Transactions on Audio, Speech, and Language Processing
Year: 2010 | Volume: 18, Issue: 2 | Journal Article |
Cited by: Papers (20)
We present a framework for estimating formant trajectories. Its focus is to achieve high robustness in noisy environments. Our approach combines a preprocessing based on functional principles of the human auditory system and a probabilistic tracking scheme. For enhancing the formant structure in spectrograms we use a Gammatone filterbank, a spectral preemphasis, as well as a spectral filtering usi...Show More

Combining Auditory Preprocessing and Bayesian Estimation for Robust Formant Tracking

Claudius Glaser;Martin Heckmann;Frank Joublin;Christian Goerick

Year: 2010 | Volume: 18, Issue: 2 | Journal Article |

Interactive online multimodal association for internal concept building in humanoids

Christian Goerick;Jens Schmudderich;Bram Bolder;Herbert Janßen;Michael Gienger;Achim Bendig;Martin Heckmann;Tobias Rodemann;Holger Brandl;Xavier Domont;Inna Mikhailova

2009 9th IEEE-RAS International Conference on Humanoid Robots
Year: 2009 | Conference Paper |
Cited by: Papers (10)
In this paper we report the results of our research on learning and developing cognitive systems. The results are integrated into ALIS 3, our Autonomous Learning and Interacting System version 3 realized the humanoid robot ASIMO. The results presented address crucial issues in autonomously acquiring mental concepts in artifacts. The major contributions are the following: We researched distributed ...Show More

Interactive online multimodal association for internal concept building in humanoids

Christian Goerick;Jens Schmudderich;Bram Bolder;Herbert Janßen;Michael Gienger;Achim Bendig;Martin Heckmann;Tobias Rodemann;Holger Brandl;Xavier Domont;Inna Mikhailova

Year: 2009 | Conference Paper |

Teaching a humanoid robot: Headset-free speech interaction for audio-visual association learning

Martin Heckmann;Holger Brandl;Jens Schmuedderich;Xavier Domont;Bram Bolder;Inna Mikhailova;Herbert Janssen;Michael Gienger;Achim Bendig;Tobias Rodemann;Mark Dunn;Frank Joublin;Christian Goerick

RO-MAN 2009 - The 18th IEEE International Symposium on Robot and Human Interactive Communication
Year: 2009 | Conference Paper |
Cited by: Papers (5)
Based on inspirations from infant development we present a system which learns associations between acoustic labels and visual representations in interaction with its tutor. The system is integrated with a humanoid robot. Except for a few trigger phrases to start learning all acoustical representations are learned online and in interaction. Similar, for the visual domain the clusters are not prede...Show More

Teaching a humanoid robot: Headset-free speech interaction for audio-visual association learning

Martin Heckmann;Holger Brandl;Jens Schmuedderich;Xavier Domont;Bram Bolder;Inna Mikhailova;Herbert Janssen;Michael Gienger;Achim Bendig;Tobias Rodemann;Mark Dunn;Frank Joublin;Christian Goerick

Year: 2009 | Conference Paper |

Listen to the parrot: Demonstrating the quality of online pitch and formant extraction via feature-based resynthesis

Martin Heckmann;Claudius Glaser;Miguel Vaz;Tobias Rodemann;Frank Joublin;Christian Goerick

2008 IEEE/RSJ International Conference on Intelligent Robots and Systems
Year: 2008 | Conference Paper |
Cited by: Papers (4)
We present a system for online extraction of the fundamental frequency and the first four formant frequencies from a speech signal. In order to evaluate the performance of the extraction a resynthesis of the speech signal is performed. The resynthesis is based on the extracted frequencies and the energy of the input signal at the formant locations. The extraction of the fundamental frequency and t...Show More

Listen to the parrot: Demonstrating the quality of online pitch and formant extraction via feature-based resynthesis

Martin Heckmann;Claudius Glaser;Miguel Vaz;Tobias Rodemann;Frank Joublin;Christian Goerick

Year: 2008 | Conference Paper |

Hierarchical spectro-temporal features for robust speech recognition

Xavier Domont;Martin Heckmann;Frank Joublin;Christian Goerick

2008 IEEE International Conference on Acoustics, Speech and Signal Processing
Year: 2008 | Conference Paper |
Cited by: Papers (7) | Patents (3)
Previously we presented an auditory-inspired feed-forward architecture which achieves good performance in noisy conditions on a segmented word recognition task. In this paper we propose to use a modified version of this hierarchical model to generate features for standard hidden Markov models. To obtain these features we firstly compute the spectrograms using a Gammatone filterbank. A filtering ov...Show More

Hierarchical spectro-temporal features for robust speech recognition

Xavier Domont;Martin Heckmann;Frank Joublin;Christian Goerick

Year: 2008 | Conference Paper |

Joint Estimation of Formant Trajectories via Spectro-Temporal Smoothing and Bayesian Techniques

C. Glaser;M. Heckmann;F. Joublin;C. Goerick;H.M. Gross

2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07
Year: 2007 | Conference Paper |
Cited by: Papers (1)
We propose a method for the joint estimation of formant trajectories from spectrograms. Formants are enhanced in the spectrograms obtained from the application of a Gammatone filterbank via a smoothing along the frequency axis. In contrast to previously published approaches, the used tracking algorithm relies on the joint distribution of formants rather than using independent tracker instances. Mo...Show More

Joint Estimation of Formant Trajectories via Spectro-Temporal Smoothing and Bayesian Techniques

C. Glaser;M. Heckmann;F. Joublin;C. Goerick;H.M. Gross

Year: 2007 | Conference Paper |

Auditory Inspired Binaural Robust Sound Source Localization in Echoic and Noisy Environments

Martin Heckmann;Tobias Rodemann;Frank Joublin;Christian Goerick;Bjorn Scholling

2006 IEEE/RSJ International Conference on Intelligent Robots and Systems
Year: 2006 | Conference Paper |
Cited by: Papers (22)
We propose a new approach for binaural sound source localization in real world environments implementing a new model of the precedence effect. This enables the robust measurement of the localization cue values (ITD, UD and IED) in echoic environments. The system is inspired by the auditory system of mammals. It uses a Gammatone filter bank for preprocessing and extracts the ITD and IED cues via ze...Show More

Auditory Inspired Binaural Robust Sound Source Localization in Echoic and Noisy Environments

Martin Heckmann;Tobias Rodemann;Frank Joublin;Christian Goerick;Bjorn Scholling

Year: 2006 | Conference Paper |

Real-time Sound Localization With a Binaural Head-system Using a Biologically-inspired Cue-triple Mapping

Tobias Rodemann;Martin Heckmann;Frank Joublin;Christian Goerick;Bjorn Scholling

2006 IEEE/RSJ International Conference on Intelligent Robots and Systems
Year: 2006 | Conference Paper |
We present a sound localization system that operates in real-time, calculates three binaural cues (IED, UD, and ITD) and integrates them in a biologically inspired fashion to a combined localization estimation. Position information is furthermore integrated over frequency channels and time. The localization system controls a head motor to fovealize on and track the dominant sound source. Due to an...Show More

Real-time Sound Localization With a Binaural Head-system Using a Biologically-inspired Cue-triple Mapping

Tobias Rodemann;Martin Heckmann;Frank Joublin;Christian Goerick;Bjorn Scholling

Year: 2006 | Conference Paper |

Sound source separation for a robot based on pitch

M. Heckmann;F. Joublin;E. Korner

2005 IEEE/RSJ International Conference on Intelligent Robots and Systems
Year: 2005 | Conference Paper |
Cited by: Papers (3) | Patents (3)
We present a novel method for the separation of monaurally recorded speech signals based on pitch. Our method is inspired by the ability of some auditory neurons to phase lock with the excitation signal. After applying a Gammatone filter-bank on the original signal we compare the distances between zero crossings of possible harmonics and decide upon the result of this comparison if they share the ...Show More

Sound source separation for a robot based on pitch

M. Heckmann;F. Joublin;E. Korner

Year: 2005 | Conference Paper |

Optimal weighting of posteriors for audio-visual speech recognition

M. Heckmann;F. Berthommier;K. Kroschel

2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221)
Year: 2001 | Conference Paper |
Cited by: Papers (6)
We investigate the fusion of audio and video a posteriori phonetic probabilities in a hybrid ANN/HMM audio-visual speech recognition system. Three basic conditions to the fusion process are stated and implemented in a linear and a geometric weighting scheme. These conditions are the assumption of conditional independence of the audio and video data and the contribution of only one of the two paths...Show More

Optimal weighting of posteriors for audio-visual speech recognition

M. Heckmann;F. Berthommier;K. Kroschel

Year: 2001 | Conference Paper |

Frequency selective step-size control for acoustic echo cancellation

Martin Heckmann;Julia Vogel;Kristian Kroschel

2000 10th European Signal Processing Conference
Year: 2000 | Conference Paper |
A new algorithm to control the step-size of a frequency domain echo cancellation system is presented. The step-size is controlled via the estimate of the coherence between the microphone signal and the output of the echo cancellation filter. The use of coherence allows an independent step-size control for each frequency bin. Additive local noise correlated with the echo signal and thus corrupting ...Show More

Frequency selective step-size control for acoustic echo cancellation

Martin Heckmann;Julia Vogel;Kristian Kroschel

Year: 2000 | Conference Paper |

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