The excitation-level model of masking reproduces measured thresholds at both low and high frequencies
Related
- Excitation patterns can be computed from masking data in either direction (similarity 0.86)
- Masking reduces to a comparison of excitation-level patterns over frequency and time (similarity 0.85)
- Frequency masking is modelled as convolution with a spreading function (similarity 0.85)
- Amounts of masking are expressed as dB differences, not absolute dB SPL levels. (similarity 0.84)
- Masking relates closely to excitation level patterns. (similarity 0.84)
- Masking thresholds can be transformed into time functions of specific loudness (similarity 0.84)
- Auditory masking spread can be modelled by convolving with a spreading function (similarity 0.84)
- Louder maskers increasingly mask high frequencies over low ones. (similarity 0.84)
- Masked threshold always exceeds quiet threshold except when masker and test frequencies differ greatly (similarity 0.83)
- Masking is quantified as the threshold elevation a masker produces. (similarity 0.83)
- Masking operates on a continuous scale between audible and fully masked. (similarity 0.83)
- The masked threshold’s high-frequency slope steepness depends on masker level (similarity 0.83)
- Beyond threshold, masker and signal levels rise one-for-one in decibels (similarity 0.83)
- Speech masking thresholds are estimated adaptively by adjusting relative talker levels. (similarity 0.83)
- Masking effectiveness depends only on masker power inside the critical band. (similarity 0.82)