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Hearing Research|March 11, 2021
DARF: A data-reduced FADE version for simulations of speech recognition thresholds with real hearing aidsDavid Hülsmeier, Marc René Schädler, Birger KollmeierInternational Journal of Audiology|December 4, 2024
Development and verification of non-supervised smartphone-based methods for assessing pure-tone thresholds and loudness perceptionChen Xu, Lena Schell-Majoor, Birger KollmeierInternational Journal of Audiology|March 10, 2010
Monaural and binaural frequency selectivity in hearing-impaired subjectsMarc Nitschmann, Jesko L Verhey, Birger KollmeierThe Journal of the Acoustical Society of America|February 10, 2011
Effect of speech-intrinsic variations on human and automatic recognition of spoken phonemesBernd T Meyer, Thomas Brand, Birger KollmeierTrends in Hearing|April 26, 2018
Objective Prediction of Hearing Aid Benefit Across Listener Groups Using Machine Learning: Speech Recognition Performance With Binaural Noise-Reduction AlgorithmsMarc R Schädler, Anna Warzybok, Birger KollmeierTrends in Hearing|January 30, 2020
Neural Representation of Loudness: Cortical Evoked Potentials in an Induced Loudness Reduction ExperimentFlorian H Schmidt, Manfred Mauermann, Birger KollmeierThe Journal of the Acoustical Society of America|November 9, 2018
Spectral directional cues captured by hearing device microphones in individual human earsFlorian Denk, Stephan D Ewert, Birger KollmeierThe Journal of the Acoustical Society of America|December 17, 2009
The role of across-frequency processes in dichotic listening conditionsMarc Nitschmann, Jesko L Verhey, Birger KollmeierFrontiers in Systems Neuroscience|February 25, 2017
Matching Pursuit Analysis of Auditory Receptive Fields' Spectro-Temporal PropertiesJörg-Hendrik Bach, Birger Kollmeier, Jörn AnemüllerThe Journal of the Acoustical Society of America|April 2, 2022
A model of speech recognition for hearing-impaired listeners based on deep learningJana Roßbach, Birger Kollmeier, Bernd T MeyerPageof 17