Spectral weighting for sentence recognition in steady-state and amplitude-modulated noise

Shen, Yi; Langley, Lauren

doi:10.1121/10.0017934

Cited by 5 publications

(2 citation statements)

References 47 publications

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“…Bayesian adaptive sampling has also been successfully applied in the auditory domain. For example, it has been used to estimate the auditory filter shape (Shen and Richards, 2013, Shen et al, 2014, the audiogram (Song et al, 2015, Cox andde Vries, 2021), the equal-loudness contour , [Shen et al 2024), and the band importance function for speech intelligibility (Shen and Kern, 2018Jan-Dec, Shen et al, 2020, Shen and Langley, 2023. Many of the previously mentioned studies use a parametric approach, which is suitable when there is sufficient a priori knowledge to formulate the model.…”

Section: Introductionmentioning

confidence: 99%

“…Bayesian adaptive sampling has also been successfully applied in the auditory domain. For example, it has been used to estimate the auditory filter shape (Shen and Richards, 2013, Shen et al, 2014, Schlittenlacher et al, 2020), the audiogram (Song et al, 2015, Cox and de Vries, 2021), the equal-loudness contour (Shen et al, 2018, Schlittenlacher and Moore, 2020, [Shen et al 2024 JASA under-review]), and the band importance function for speech intelligibility (Shen and Kern, 2018 Jan-Dec, Shen et al, 2020, Shen and Langley, 2023).…”

Section: Introductionmentioning

confidence: 99%

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Multi-species numerical validation of an efficient algorithm for auditory brainstem response hearing threshold estimation

Petersen,

Shen

2024

Preprint

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The Auditory Brainstem Response (ABR) can be used to evaluate hearing sensitivity of animals who are unable to respond to behavioral tasks. However, typical data collection methods are time consuming; decreasing the measurement time may save resources or allow researchers to spend more time on other tasks. Here, an adaptive algorithm is proposed for efficient estimation of ABR thresholds. The algorithm relies on the online update of the predicted hearing threshold from a Gaussian process model as ABR data are collected using iteratively optimized stimuli. To validate the algorithm, ABR threshold estimation is simulated by adaptively sub-sampling pre-collected ABR datasets for which the stimuli were systematically varied in frequency and level. The simulated experiment is performed on 5 datasets of Mouse (2 different datasets), Budgerigar, Gerbil, and Guinea Pig ABRs collected by different laboratories, with a total of 27 ears. The original datasets contain between 68 and 106 stimuli conditions, while the adaptive algorithm is run up to a total of 20 stimuli conditions. The adaptive algorithm ABR threshold estimate is compared against human rater estimates who view the full ABR dataset. The adaptive algorithm threshold matches the human estimates within 10 dB, averaged over frequency, for 19 out of 27 ears. The adaptive procedure is able to provide threshold estimates that are comparable to the human rater estimated thresholds while reducing the measurement time by a factor of 3 to 5. The standard deviation of threshold estimates from successive runs is smaller than the inter-human rater differences, indicating adequate test/retest reliability.

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Section: Introductionmentioning

confidence: 99%

“…Bayesian adaptive sampling has also been successfully applied in the auditory domain. For example, it has been used to estimate the auditory filter shape (Shen and Richards, 2013, Shen et al, 2014, Schlittenlacher et al, 2020), the audiogram (Song et al, 2015, Cox and de Vries, 2021), the equal-loudness contour (Shen et al, 2018, Schlittenlacher and Moore, 2020, [Shen et al 2024 JASA under-review]), and the band importance function for speech intelligibility (Shen and Kern, 2018 Jan-Dec, Shen et al, 2020, Shen and Langley, 2023).…”

Section: Introductionmentioning

confidence: 99%

Multi-species numerical validation of an efficient algorithm for auditory brainstem response hearing threshold estimation

Petersen,

Shen

2024

Preprint

Self Cite

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Toward parametric Bayesian adaptive procedures for multi-frequency categorical loudness scaling

Shen,

Petersen,

Neely

2024

The Journal of the Acoustical Society of America

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A series of Bayesian adaptive procedures to estimate loudness growth across a wide frequency range from individual listeners was developed, and these procedures were compared. Simulation experiments were conducted based on multinomial psychometric functions for categorical loudness scaling across ten test frequencies estimated from 61 listeners with normal hearing and 87 listeners with sensorineural hearing loss. Adaptive procedures that optimized the stimulus selection based on the interim estimates of two types of category-boundary models were tested. The first type of model was a phenomenological model of category boundaries adopted from previous research studies, while the other type was a data-driven model derived from a previously collected set of categorical loudness scaling data. An adaptive procedure without Bayesian active learning was also implemented. Results showed that all adaptive procedures provided convergent estimates of the loudness category boundaries and equal-loudness contours between 250 and 8000 Hz. Performing post hoc model fitting, using the data-driven model, on the collected data led to satisfactory accuracies, such that all adaptive procedures tested in the current study, independent of modeling approach and stimulus-selection rules, were able to provide estimates of the equal-loudness-level contours between 20 and 100 phons with root-mean-square errors typically under 6 dB after 100 trials.

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Band importance for speech-in-speech recognition in the presence of extended high-frequency cues

Ananthanarayana,

Buss,

Monson

2024

The Journal of the Acoustical Society of America

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Band importance functions for speech-in-noise recognition, typically determined in the presence of steady background noise, indicate a negligible role for extended high frequencies (EHFs; 8–20 kHz). However, recent findings indicate that EHF cues support speech recognition in multi-talker environments, particularly when the masker has reduced EHF levels relative to the target. This scenario can occur in natural auditory scenes when the target talker is facing the listener, but the maskers are not. In this study, we measured the importance of five bands from 40 to 20 000 Hz for speech-in-speech recognition by notch-filtering the bands individually. Stimuli consisted of a female target talker recorded from 0° and a spatially co-located two-talker female masker recorded either from 0° or 56.25°, simulating a masker either facing the listener or facing away, respectively. Results indicated peak band importance in the 0.4–1.3 kHz band and a negligible effect of removing the EHF band in the facing-masker condition. However, in the non-facing condition, the peak was broader and EHF importance was higher and comparable to that of the 3.3–8.3 kHz band in the facing-masker condition. These findings suggest that EHFs contain important cues for speech recognition in listening conditions with mismatched talker head orientations.

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Spectral weighting for sentence recognition in steady-state and amplitude-modulated noise

Cited by 5 publications

References 47 publications

Multi-species numerical validation of an efficient algorithm for auditory brainstem response hearing threshold estimation

Multi-species numerical validation of an efficient algorithm for auditory brainstem response hearing threshold estimation

Toward parametric Bayesian adaptive procedures for multi-frequency categorical loudness scaling

Band importance for speech-in-speech recognition in the presence of extended high-frequency cues

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