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Rainer Huber

dblp:22/3227 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Audio and music processing
speech enhancement
0.512021
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
0.222021
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
0.112006
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
YearPublicationVenuePosition
2022 Speech Intelligibility Prediction for Hearing-Impaired Listeners with the LEAP Model
abstract
3498
Jana Roßbach, Rainer Huber, Saskia Röttges, Christopher F. Hauth, Thomas Biberger, Thomas Brand, Bernd T. Meyer, Jan Rennies
INTERSPEECH2
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 Models
abstract
S.981-985
Paul Kranzusch, Rainer Huber, Melanie Krüger, Birger Kollmeier, Bernd T. Meyer
INTERSPEECH2
2018 Prediction of Perceived Speech Quality Using Deep Machine Listening
abstract
S.976-980
Jasper Ooster, Rainer Huber, Bernd T. Meyer
INTERSPEECH2
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
INTERSPEECH1
2014 Quality assessment of multi-channel audio processing schemes based on a binaural auditory model
abstract
A 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
ICASSP4
2006 PEMO-Q - A New Method for Objective Audio Quality Assessment Using a Model of Auditory Perception
abstract
A 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