Masking depends on stimulus fluctuations in ways steady-state models cannot predict.

Evidence

When the noise masker is replaced by 16 pure tones (PT; open circles), performance is markedly better—all three groups show a large improvement in performance, although there appears to be a systematic decrease in performance from young NH to age-matched NH to the HI group. Imposing noise envelope fluctuations to the pure-tone maskers (MT; filled circles) results in a small reduction in performance for all three groups. It appears that both the spectral sparsity of the tonal maskers (PT and MT), as well as the lack of fluctuations (PT), contributes to the release from masking relative to the GN condition. — Dijk, Pim van, Physiology, Psychoacoustics and…, 2188 (#corroborates, via linked claim)

The results from Hawkins and Stevens (1950) illustrate a small, but robust, effect of signal frequency. However, we can better observe the relationship between the frequency of maskers and the frequencies of the signals when experiments are conducted under variable frequency conditions. — Jennifer Lentz, Psychoacoustics- Perception of Normal…, 461 (#corroborates, via linked claim)

Categorization

• XREF: connects to: Steady-state masking noise varies perceptually even with a 100-ms time shift. — Dijk, Pim van, Physiology, Psychoacoustics and…, 5575; Reduced frequency selectivity partly explains diminished sensitivity to inherent masker fluctuations; Masking effectiveness depends on masker-signal power, frequency, and timing relationships — Jennifer Lentz, Psychoacoustics- Perception of Normal…, 461 (+1 more)

Related

• Both spectral sparsity and lack of fluctuations in a masker release speech from masking (similarity 0.93)
• Both spectral sparsity and lack of masker fluctuations contribute to release from masking. (similarity 0.92)
• Masking operates on a continuous scale between audible and fully masked. (similarity 0.90)
• Masking effectiveness depends on masker-signal power, frequency, and timing relationships (similarity 0.89)
• Uniform masking noise is engineered as the inverse of white-noise masking’s frequency dependence. (similarity 0.88)
• Masker effectiveness is determined solely by power falling within one auditory filter. (similarity 0.88)
• Tone, noise, and independently modulated tone maskers produce equal speech masking. (similarity 0.87)
• A masker raises the audibility threshold of a test sound. (similarity 0.87)
• Pure-tone versus noise masking differences depend on masker frequency (similarity 0.87)
• Fluctuating maskers produce masking release that Fletcher’s power spectrum model cannot explain. (similarity 0.87)
• The speech-masking potency of inherent noise fluctuations depends on listener frequency selectivity and level (similarity 0.87)
• Masking relates closely to excitation level patterns. (similarity 0.87)
• Reducing audible fluctuations in a masker lowers post-masked thresholds by up to 24 dB (similarity 0.87)
• Masked threshold shape changes with masker level, becoming more asymmetric at high levels (similarity 0.86)
• Reduced frequency selectivity partly explains diminished sensitivity to inherent masker fluctuations (similarity 0.86)

Broader Connections

• Both spectral sparsity and lack of fluctuations in a masker release speech from masking #corroborates (0.85) — Dijk, Pim van, Physiology, Psychoacoustics and…, 2188
• Reduced frequency selectivity partly explains diminished sensitivity to inherent masker fluctuations #expands (0.78)
• Masking effectiveness depends on masker-signal power, frequency, and timing relationships #corroborates (0.72) — Jennifer Lentz, Psychoacoustics- Perception of Normal…, 461