fastACI

fastACI provides a compilation of tools for collecting and analyzing data from auditory reverse-correlation experiments to assess the acoustic features used by listeners performing behavioral listening tasks.

Key Features:

• Experiment Design: Supports running existing experiments or designing new auditory reverse-correlation experiments.
• Data Analysis: Provides main options for analyzing collected stimulus-response data from each trial.
• Auditory Classification Image Generation: Summarizes results as a matrix of perceptual weights termed auditory classification image.
• Flexible Framework: Accommodates different auditory mechanisms and experimental paradigms.

Scientific Applications:

• Tone-in-Noise Detection: Probes auditory mechanisms for detecting target tones in additive background noise.
• Amplitude Modulation Detection: Investigates perception of amplitude modulations in acoustic signals.
• Phoneme-in-Noise Categorization: Studies categorization of phonemes presented with random fluctuation in background noise.
• Word Segmentation: Examines mechanisms underlying segmentation of words from continuous speech or noise.
• Noise Type and Estimation Method Comparison: Quantifies how noise type and estimation method affect auditory classification image quality.

Methodology:

Auditory reverse-correlation experiments present one or more target sounds with random fluctuation, typically additive background noise, and analyze paired stimulus-response data to estimate perceptual weights.