DOI: 10.11583/dtu.31079305
title: <b>Dataset for: </b><b>"Predicting spectro-temporal modulation detection thresholds with a functional auditory model"</b>

description: <b>Dataset for: "Predicting spectro-temporal modulation detection thresholds with a functional auditory model"</b>Lily Cassandra Paulick, Torsten Dau and Helia Relaño-IborraThis dataset supports the findings reported in: Paulick, L.C., Dau, T. and Relaño-Iborra, H. (2026). "<i>Predicting spectro-temporal modulation detection thresholds with a functional auditory model</i>." Trends in Hearing, 30, 1-16. doi:10.1177/23312165261425853The README describes the contents, structure and variables of the dataset.<b>Participants</b>The dataset contains data from 20 listeners, consisting of10 young normal hearing (NH) listeners, and10 older listeners with hearing impairment (HI).<b>Data overview</b>The data collected were:Audiograms (AUDIOGRAM.csv)Spectro-temporal modulation detection thresholds (STM.csv)Adaptive categorical loudness scaling (ACALOS.csv) for HI listeners onlyParticipant identifiers (‘Subject’) are consistent across all three files. Listener age (‘Age’) and tested ear (‘Ear’) are included in all files to allow independent use of each dataset.The dataset is licensed under Creative Commons Attribution 4.0 (CC BY 4.0).<b>Audiograms (AUDIOGRAM.csv)</b>This file contains audiometric and demographic information for all 20 participants. The columns include a unique participant identifier (‘Subject’), listener group (‘ListenerType’, NH or HI), age in years (‘Age’), and tested ear (‘Ear’, R or L). Audiometric thresholds are provided in dB Hearing Level (HL) at frequencies of 0.25, 0.5, 1, 2, 4, and 8 kHz. Threshold columns follow the naming convention audiogram_"frequency", where "frequency" specifies the test frequency (e.g., audiogram_250Hz, audiogram_500Hz, audiogram_1kHz).<b>(Spectro)-temporal modulation detection thresholds (STM.csv)</b>This file provides spectro-temporal modulation (STM) detection thresholds for both normal-hearing (NH) and hearing-impaired (HI) listeners. Each row contains the participant identifier (‘Subject’), listener group (‘ListenerType’), age in years (‘Age’), tested ear (‘Ear’), temporal modulation rate (‘fmHz’), spectral modulation rate (‘fmCO’), and the detection threshold (‘ModDepth’), expressed as modulation depth at threshold in dB. Six experimental conditions were tested, defined by all combinations of temporal modulation rates of 4 or 12 Hz and spectral modulation rates of 0, 1, or 2 cycles per octave. Thresholds are averaged across repetitions for each listener and condition.A detailed description of the stimuli and experimental procedure is provided in the associated article.<b>Adaptive categorical loudness scaling (ACALOS.csv)</b>Adaptive categorical loudness scaling (ACALOS) data were collected for the hearing-impaired (HI) listener group only. Each row includes the participant identifier (‘Subject’), age in years (‘Age’), listener group (‘ListenerType’), and tested ear (‘Ear’). For each listener, frequency-specific loudness-related parameters are provided at frequencies of 0.25, 0.5, 1, 2, 4 and 6 kHz. Separate columns are given for the hearing threshold level (HTL), medium-loudness level (MLL), and uncomfortable loudness level (UCL), defined at 2.5, 25, and 50 categorical units (CU), respectively. Column names follow the convention "parameter"_"frequency" (e.g., HTL_500Hz, MLL_1kHz, UCL_2kHz).All loudness functions were fitted using the BTUX method described in Oetting et al. (2014).<b><i>Ethical statement</i></b>All listeners were financially compensated for their time and gave written informed consent. Ethical approval for the study was provided by the Science-Ethics Committee for the Capital Region of Denmark (reference H-16036391).<b><i>References</i></b>Bernstein JGW, Mehraei G, Shamma S, Gallun FJ, Theodoroff SM and Leek MR (2013) o o“Spectrotemporal Modulation Sensitivity as a Predictor of Speech Intelligibility for Hearing-oImpaired Listeners”. J. Am. Acad. Audiol. 24(4): 293–306. DOI:10.3766/jaaa.24.4.5.Brand T and Hohmann V (2002) “An adaptive procedure for categorical loudness scaling”. The oJournal of the Acoustical Society of America 112(4): 1597–1604. DOI:10.1121/1.1502902Oetting D, Brand T and Ewert SD (2014) “Optimized loudness-function estimation for categorical oloudness scaling data”. Hearing Research 316: 16–27. DOI:10.1016/J.HEARES.2014.07.003.Zaar J, Simonsen LB, Dau T and Laugesen S (2023) “Toward a clinically viable spectro-temporal omodulation test for predicting supra-threshold speech reception in hearing-impaired olisteners”. Hearing Research 427: 108650. DOI:10.1016/j.heares.2022.108650.<b><i>Citation and links</i></b>Cite this dataset:Paulick, L.C., Dau, T. and Relaño-Iborra, H. (2026). Dataset for: " Predicting spectro-temporal modulation detection thresholds with a functional auditory model ". Technical University of Denmark. Dataset. https://doi.org/10.11583/DTU.31079305Corresponding article:Paulick, L.C., Dau, T. and Relaño-Iborra, H. (2026). "<i>Predicting spectro-temporal modulation detection thresholds with a functional auditory model</i>." Trends in Hearing, 30, 1-16. doi:10.1177/23312165261425853<b><i>Acknowledgments and funding</i></b>We would like to thank Jonathan Regev for valuable help with the experimental design and setup and providing code for the ACALOS experiment, as well as Johannes Zaar for providing base code for generating the STM stimuli. This work was carried out in connection to the Center for Applied Hearing Research (CAHR) supported by WSA, Oticon, GN Hearing, and the Technical University of Denmark.