Deriving loudness growth functions from categorical loudness scaling data

Area of Science:

• Acoustics
• Psychoacoustics
• Auditory Perception

Background:

• Continuous loudness scaling (e.g., magnitude estimation) provides ratio data but can be difficult for listeners.
• Categorical loudness scaling (CLS) is easier but yields non-proportional categorical units (CUs).
• Reconciling these methods is crucial for accurate loudness perception research.

Purpose of the Study:

• To reconcile differences between continuous and categorical loudness scaling methods.
• To develop a method for converting CLS categorical units (CUs) into a ratio scale (sones).
• To validate the new CU_sone metric by reanalyzing existing CLS data.

Main Methods:

• Utilized data from Heeren et al. (2013) to develop the CU_sone metric.
• Reanalyzed CLS data from Rasetshwane et al. (2015) using the CU_sone metric.
• Compared results with established loudness scaling techniques like magnitude estimation.

Main Results:

• The CU_sone metric successfully converted CLS data to a ratio scale.
• Reanalyzed CLS data fitted well with power functions.
• Results demonstrated general agreement with continuous loudness scaling methods.

Conclusions:

• The CU_sone metric effectively bridges the gap between categorical and continuous loudness scaling.
• This approach enables ratio-level comparisons of loudness across listeners using CLS data.
• The findings support the validity and utility of the CU_sone metric in psychoacoustic research.