Abstract

This study describes the masking asymmetry between noise and iterated rippled noise (IRN) as a function of spectral region and the IRN delay. Masking asymmetry refers to the fact that noise masks IRN much more effectively than IRN masks noise, even when the stimuli occupy the same spectral region. Detection thresholds for IRN masked by noise and for noise masked by IRN were measured with an adaptive two-alternative, forced choice (2AFC) procedure with signal level as the adaptive parameter. Masker level was randomly varied within a 10-dB range in order to reduce the salience of loudness as a cue for detection. The stimuli were filtered into frequency bands, 2.2-kHz wide, with lower cutoff frequencies ranging from 0.8 to 6.4 kHz. IRN was generated with 16 iterations and with varying delays. The reciprocal of the delay was 16, 32, 64, or 128 Hz. When the reciprocal of the IRN delay was within the pitch range, i.e., above 30 Hz, there was a substantial masking asymmetry between IRN and noise for all filter cutoff frequencies; threshold for IRN masked by noise was about 10 dB larger than threshold for noise masked by IRN. For the 16-Hz IRN, the masking asymmetry decreased progressively with increasing filter cutoff frequency, from about 9 dB for the lowest cutoff frequency to less than 1 dB for the highest cutoff frequency. This suggests that masking asymmetry may be determined by different cues for delays within and below the pitch range. The fact that masking asymmetry exists for conditions that combine very long IRN delays with very high filter cutoff frequencies means that it is unlikely that models based on the excitation patterns of the stimuli would be successful in explaining the threshold data. A range of time-domain models of auditory processing that focus on the time intervals in phase-locked neural activity patterns is reviewed. Most of these models were successful in accounting for the basic masking asymmetry between IRN and noise for conditions within the pitch range, and one of the models produced an exceptionally good fit to the data.

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