Demo Data from EDBT 2026 / reviewers in the wild / expert
Rainer Huber
dblp:22/3227
· DBLP profile ↗
8ranked-venue papers
2as first author
2since 2021 · last 2022
0000-0001-7241-2037ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 6 · 2 first-author · 2 since 2021Graphics, computer vision, multimedia, augmented reality and games · 6 · 1 first-author · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer graphics and multimedia
2 papers |
Audio and music processing · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Audio and music processing
speech enhancement |
0.5 | 1 | 2021 | Reduction of Subjective Listening Effort for TV Broadcast Signals With Recurrent Neural Networks · IEEE ACM Trans. Audio Speech Lang. Process. 2021 |
Audio and music processing
audio quality assessment |
0.2 | 2 | 2021 | Reduction of Subjective Listening Effort for TV Broadcast Signals With Recurrent Neural Networks · IEEE ACM Trans. Audio Speech Lang. Process. 2021 PEMO-Q - A New Method for Objective Audio Quality Assessment Using a Model of Auditory Perception · IEEE Trans. Speech Audio Process. 2006 |
Audio and music processing › auditory processing
auditory modeling |
0.1 | 1 | 2006 | PEMO-Q - A New Method for Objective Audio Quality Assessment Using a Model of Auditory Perception · IEEE Trans. Speech Audio Process. 2006 |
Methods — techniques the papers use, named apart from their topics
recurrent neural network · 0.5psychoacoustic model · 0.1peripheral auditory model · 0.1cross-correlation · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | Speech Intelligibility Prediction for Hearing-Impaired Listeners with the LEAP Modelabstract3498 Jana Roßbach, Rainer Huber, Saskia Röttges, Christopher F. Hauth, Thomas Biberger, Thomas Brand, Bernd T. Meyer, Jan Rennies |
INTERSPEECH | 2 |
| 2021 | Reduction of Subjective Listening Effort for TV Broadcast Signals With Recurrent Neural Networks
Nils L. Westhausen, Rainer Huber, Hannah Baumgartner, Ragini Sinha, Jan Rennies, Bernd T. Meyer |
IEEE ACM Trans. Audio Speech Lang. Process. | 2 |
| 2018 | Prediction of Subjective Listening Effort from Acoustic Data with Non-Intrusive Deep ModelsabstractS.981-985 Paul Kranzusch, Rainer Huber, Melanie Krüger, Birger Kollmeier, Bernd T. Meyer |
INTERSPEECH | 2 |
| 2018 | Prediction of Perceived Speech Quality Using Deep Machine ListeningabstractS.976-980 Jasper Ooster, Rainer Huber, Bernd T. Meyer |
INTERSPEECH | 2 |
| 2018 | Evaluation of an automated speech-controlled listening test with spontaneous and read responses
Jasper Ooster, Rainer Huber, Birger Kollmeier, Bernd T. Meyer |
Speech Commun. | 2 |
| 2017 | Single-Ended Prediction of Listening Effort Based on Automatic Speech Recognition
Rainer Huber, Constantin Spille, Bernd T. Meyer |
INTERSPEECH | 1 |
| 2014 | Quality assessment of multi-channel audio processing schemes based on a binaural auditory modelabstractA perceptual, binaural audio-quality model is introduced. The model was developed for predicting any kinds of perceived spatial quality differences between two audio signals in multi-channel reproduction and audio processing schemes. It employs a recent binaural auditory model as front-end to provide perceptually relevant binaural features for the reference and test audio signal. Correlations between the binaural features of both signals are combined to an overall spatial quality measure by the use of multivariate adaptive regression splines (MARS). Furthermore, a database was generated to train and evaluate the model. The database contains various multi-channel audio signals, which were subjectively assessed in formal listening tests with 15 trained listeners. The results show different model prediction performances depending on the type of quality degradation. Combination of the proposed spatial quality measure with established monaural quality measures improved the predictive power. Jan-Hendrik Flesner, Stephan Dieter Ewert, Birger Kollmeier, Rainer Huber |
ICASSP | 4 |
| 2006 | PEMO-Q - A New Method for Objective Audio Quality Assessment Using a Model of Auditory PerceptionabstractA new method for the objective assessment and prediction of perceived audio quality is introduced. It represents an expansion of the speech quality measure qC, introduced by Hansen and Kollmeier, and is based on a psychoacoustically validated, quantitative model of the "effective" peripheral auditory processing by Dau et al. To evaluate the audio quality of a given distorted signal relative to a corresponding high-quality reference signal, the auditory model is employed to compute "internal representations" of the signals, which are partly assimilated in order to account for assumed cognitive aspects. The linear cross correlation coefficient of the assimilated internal representations represents the perceptual similarity measure (PSM). PSM shows good correlations with subjective quality ratings if different types of audio signals are considered separately, whereas a better accuracy of signal-independent quality prediction is achieved by a second quality measure PSMtrepresented by the fifth percentile of the sequence of instantaneous audio quality PSM(t). The new measures were evaluated using a large database of subjective listening tests that were originally carried out on behalf of the International Telecommunication Union (ITU) and Moving Pictures Experts Group (MPEG) for the evaluation of various low bit-rate audio codecs. Additional tests with data unknown in the development phase of the model were carried out. Except for linear distortions, the new method shows a higher prediction accuracy than the ITU-R recommendation BS.1387 ("PEAQ") for the tested data Rainer Huber, Birger Kollmeier |
IEEE Trans. Speech Audio Process. | 1 |