2022
DOI: 10.1016/j.heares.2022.108610
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Speech intelligibility prediction based on modulation frequency-selective processing

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Cited by 10 publications
(6 citation statements)
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“…This frequency-selective characteristic of AM processing has been modelled using the concept of a modulation filterbank, based on the idea that AM fluctuations are decomposed through an array of relatively broad bandpass modulation filters with a constant quality (Q) factor of approximately 1-2 (e.g., Dau et al ., 1997a, 1999; Ewert and Dau, 2000). Computational modelling studies have successfully applied the modulation filterbank concept to simulate data from various experimental paradigms, including simultaneous and non-simultaneous spectral and temporal signal detection and masking conditions (Dau et al ., 1997a, 1997b, 1999; Verhey et al ., 1999; Ewert and Dau, 2000; Ewert et al ., 2002; Piechowiak et al ., 2007; Jepsen et al ., 2008; Jepsen and Dau, 2011; King et al ., 2019), sound texture perception (McDermott and Simoncelli, 2011; McDermott et al ., 2013; McWalter and Dau, 2015, 2017), auditory stream segregation (Elhilali et al ., 2009; Christiansen et al ., 2014), and speech intelligibility (Jørgensen and Dau, 2011; Jørgensen et al, 2013; Relaño-Iborra et al, 2016, 2019; Zaar and Dau, 2017; Zaar et al, 2017; Steinmetzger et al, 2019; Zaar and Carney, 2022; for a review, see Relaño-Iborra and Dau, 2022). Furthermore, the modulation filterbank is conceptually consistent with the temporal dimension of a ‘two-dimensional’ spectro-temporal modulation filterbank, inspired by neural responses to spectro-temporally varying stimuli in the auditory cortex of ferrets (Kowalski et al ., 1996; Depireux et al ., 2001) and supported by data from perceptual learning and masking conditions (Sabin et al ., 2012; Oetjen and Verhey, 2015, 2017; Conroy et al ., 2022), as well as models of speech intelligibility (Elhilali et al ., 2003; Chi et al ., 2005; Zilany and Bruce, 2007; Chabot-Leclerc et al ., 2014).…”
Section: Introductionmentioning
confidence: 99%
“…This frequency-selective characteristic of AM processing has been modelled using the concept of a modulation filterbank, based on the idea that AM fluctuations are decomposed through an array of relatively broad bandpass modulation filters with a constant quality (Q) factor of approximately 1-2 (e.g., Dau et al ., 1997a, 1999; Ewert and Dau, 2000). Computational modelling studies have successfully applied the modulation filterbank concept to simulate data from various experimental paradigms, including simultaneous and non-simultaneous spectral and temporal signal detection and masking conditions (Dau et al ., 1997a, 1997b, 1999; Verhey et al ., 1999; Ewert and Dau, 2000; Ewert et al ., 2002; Piechowiak et al ., 2007; Jepsen et al ., 2008; Jepsen and Dau, 2011; King et al ., 2019), sound texture perception (McDermott and Simoncelli, 2011; McDermott et al ., 2013; McWalter and Dau, 2015, 2017), auditory stream segregation (Elhilali et al ., 2009; Christiansen et al ., 2014), and speech intelligibility (Jørgensen and Dau, 2011; Jørgensen et al, 2013; Relaño-Iborra et al, 2016, 2019; Zaar and Dau, 2017; Zaar et al, 2017; Steinmetzger et al, 2019; Zaar and Carney, 2022; for a review, see Relaño-Iborra and Dau, 2022). Furthermore, the modulation filterbank is conceptually consistent with the temporal dimension of a ‘two-dimensional’ spectro-temporal modulation filterbank, inspired by neural responses to spectro-temporally varying stimuli in the auditory cortex of ferrets (Kowalski et al ., 1996; Depireux et al ., 2001) and supported by data from perceptual learning and masking conditions (Sabin et al ., 2012; Oetjen and Verhey, 2015, 2017; Conroy et al ., 2022), as well as models of speech intelligibility (Elhilali et al ., 2003; Chi et al ., 2005; Zilany and Bruce, 2007; Chabot-Leclerc et al ., 2014).…”
Section: Introductionmentioning
confidence: 99%
“…Auditory models [1][2][3][4] provide insights into the inner workings of the auditory system and can be a useful tool when, e.g., analyzing novel audio-processing techniques and hearing-aid algorithms. More importantly, models provide a valuable framework to test hypotheses regarding potential effects of hearing deficits in the auditory system on relevant outcome measures.…”
Section: Evaluating An Auditory Model As Predictor Of Speech Understa...mentioning
confidence: 99%
“…A more realistic implementation of this stage in the overall CASP model is thus hypothesised to improve model predictions, in particular with regard to predictions of data from listeners with a hearing impairment. The large variability observed in data across listeners with hearing impairment, especially in speech-related tasks, is still largely underestimated by current modelling studies [6]. The present study aimed to validate the revised CASP model implementation in psychoacoustic conditions for listeners with normal hearing.…”
Section: Introductionmentioning
confidence: 99%