Institut für Nachrichtentechnik

T. Robotham, A. Bhattacharya, A. Raake, and E. A. P. Habets, “6-dof exploration with unsupervised machine learning in interactive virtual environments,” in Fortschritte der Akustik DAGA '26, (Dresden, Germany), Mar. 2026. [ bib | DOI | https ]

S. Göring and R. R. Ramachandra Rao, “Instagram-like filters for ai-generated and real photos,” Instagram-like filters for AI-generated and real photos (March 17, 2026), 2026. [ bib | DOI ]

D. Keller, R. R. Ramachandra Rao, J. Prenzel, and A. Raake, “A subjective and objective evaluation of viewing distance and 4k video quality,” IEEE Access, 2026. [ bib ]

L. C. Paulick, T. Dau, and H. Relaño Iborra, “Predicting spectro-temporal modulation detection thresholds with a functional auditory model,” Trends in Hearing, vol. 30, Jan. 2026. [ bib | DOI | https ]

Spectro-temporal modulation (STM) sensitivity has been proposed as a sensitive marker of speech intelligibility in challenging listening conditions, yet the underlying auditory mechanisms involved in STM detection remain incompletely understood. The present study measured STM detection thresholds in young normal-hearing and older hearing-impaired listeners and evaluated whether the revised Computational Auditory Signal Processing and Perception model (CASP) can account for individual performance. Thresholds were obtained for six modulation detection conditions, defined by combinations of spectral (0, 1, and 2 c/o) and temporal (4 and 12 Hz) rates. To individualize CASP, outer and inner hair cell loss estimates were obtained from audiometric and Adaptive Categorical Loudness Scaling (ACALOS) data. The results showed systematically elevated thresholds in older hearing-impaired listeners as compared to the young normal-hearing group, particularly at higher spectral rates. The model simulations reproduced overall threshold patterns, but substantially underestimated group differences and interindividual variability in the data. Moreover, the simulations showed limited sensitivity to estimates of outer and inner hair cell loss, supporting the idea that additional supra-threshold mechanisms contribute to STM deficits. While these findings demonstrate the potential of auditory models to predict STM performance, they also highlight the need for refined representations of peripheral and central processing to account for individual STM detection thresholds.

L. C. Paulick, T. Dau, and H. Relaño Iborra, “Dataset for: "predicting spectro-temporal modulation detection thresholds with a functional auditory model",” 2026. [ bib | DOI ]