Adaptive loudness scaling needs only 30 trials versus 440

Evidence

In this paper, we show how a catalog of MCPFs can be used to find the ML estimate of a listener’s MCPF, when a relatively small number of experimental trials are available. The ISO adaptive recommendation for CLS testing results in a relatively low number of experimental trials (≈ 15–30), in order to reduce the testing time and make the test more clinically realizible. Although this is an efficient approach, due to the low number of samples, the resulting CLS function can be inaccurate. The ML estimate of a listener’s loudness perception based on this lower number of experimental trials is able to more accurately predict a listener’s underlying CLS function, without removing outliers or using assumptions about the shape of the function to smooth the result. — Dijk, Pim van, Physiology, Psychoacoustics and…, 2567 (#corroborates, via linked claim)

Categorization

• corroborated: 4 independent passages support this claim
• XREF: connects to: Maximum-likelihood catalog fitting yields accurate loudness estimates from few observations — Dijk, Pim van, Physiology, Psychoacoustics and…, 2567

Related

• Maximum-likelihood catalog fitting yields accurate loudness estimates from few observations (similarity 0.94)
• Maximum-likelihood estimation can predict categorical loudness functions from only 15-30 trials (similarity 0.91)
• Sparse trial counts can still yield accurate loudness functions via estimation theory. (similarity 0.88)
• Short clinical test lengths amplify response variability into inaccurate loudness curves. (similarity 0.88)
• Adaptive loudness scaling requires roughly 15 times fewer trials than fixed-level procedures. (similarity 0.88)
• ML estimation recovers loudness curves from sparse trials without outlier removal or shape assumptions (similarity 0.87)
• Maximum-likelihood estimation improves CLS accuracy over adaptive medians (similarity 0.87)
• Modeling the multi-category psychometric function markedly improves loudness estimation accuracy. (similarity 0.85)
• ISO adaptive loudness testing needs under a tenth the trials of fixed-level testing (similarity 0.84)
• Adaptive stimulus selection can maximize information gain per loudness trial (similarity 0.82)
• Categorical loudness scaling delivers rapid clinical loudness estimates without a reference. (similarity 0.82)
• Categorical loudness measurements use fixed repeated stimuli spanning the dynamic range (similarity 0.81)
• Categorical loudness data is probabilistic, not reducible to a median response curve. (similarity 0.80)
• Categorical loudness scaling is a fast, clinically usable loudness measure. (similarity 0.80)
• Standard loudness analysis discards response variability that probabilistic models can exploit (similarity 0.79)

Broader Connections

• Maximum-likelihood catalog fitting yields accurate loudness estimates from few observations #corroborates (0.85) — Dijk, Pim van, Physiology, Psychoacoustics and…, 2567