Modulation-frequency-specific disruption captures interferer masking effects better than energy models

Evidence

Although this model predicted the trend for the 800-Hz target the effect was too small. For the 730-Hz target, no effect was predicted, in contrast to the data. Thus this energy-based AC model was unable to predict the effect of the interferer on the diotic thresholds. The second model assumed that modulation cues with frequencies adjacent to the beating frequency between interferer and masker could not be used for signal detection. This was realised in the modulation-based AC model by setting the output of the corresponding modulation filters to zero. This approach predicted general trends of the diotic data of the present study. Among others it predicted a larger effect of the interferer for the 800-Hz signal than for the 730-Hz target, in agreement with the data. — Dijk, Pim van, Physiology, Psychoacoustics and…, 3494 (#corroborates, via linked claim)

Categorization

• corroborated: 3 independent passages support this claim
• XREF: connects to: Intrinsic noise amplitude fluctuation masks detection of imposed amplitude modulation; Narrow-band noise self-modulation masks external amplitude modulation — Professor Dr.-Ing. Hugo Fastl, Professor Dr.-Ing. Eberhard, Psychoacoustics- Facts and Models, 3302; Energy-based models of modulation filterbanks fail to predict interferer effects on detection — Dijk, Pim van, Physiology, Psychoacoustics and…, 3494 (+4 more)

Related

• A distant interferer tone can disrupt target detection via across-frequency MDI. (similarity 0.94)
• Energy-based models of modulation filterbanks fail to predict interferer effects on detection (similarity 0.91)
• Interferers partially rather than fully suppress the modulation cues used for detection (similarity 0.90)
• Intrinsic noise amplitude fluctuation masks detection of imposed amplitude modulation (similarity 0.89)
• Modulation Detection Interference is frequency-selective in its effectiveness (similarity 0.88)
• Only modulation-based processing, not energy-based, predicts how interferers raise detection thresholds. (similarity 0.88)
• Modulation Detection Interference operates across different auditory frequency channels (similarity 0.87)
• Across-channel interference can be modelled by subtracting off-frequency filter energy (similarity 0.87)
• Modulation interference degrades rather than abolishes modulation cues across a range of frequencies. (similarity 0.87)
• Both models mispredict how near an interferer must sit to raise detection thresholds. (similarity 0.86)
• Modulation-based analysis underlies the AC model’s matching of measured thresholds. (similarity 0.84)
• A tone matching the masker-signal spectral distance degrades beating-cue detection more than other interferers (similarity 0.84)
• A modulation filter bank may explain source grouping but remains unproven (similarity 0.84)
• Modulation in off-frequency channels can suppress on-frequency modulation processing (similarity 0.84)
• Auditory modulation processing is frequency-selective in detecting masked targets (similarity 0.84)

Broader Connections

• Energy-based models of modulation filterbanks fail to predict interferer effects on detection #corroborates (0.95) — Dijk, Pim van, Physiology, Psychoacoustics and…, 3494
• A distant interferer tone can disrupt target detection via across-frequency MDI. #corroborates (0.90)
• The excitation-level model of masking reproduces measured thresholds at both low and high frequencies #contradicts (0.85)
• MDI interference peaks when target and interferer share symmetric offsets. #expands (0.85)
• Across-channel interference can be modelled by subtracting off-frequency filter energy #contradicts (0.80)