Trained listeners can verbally connect sound preference to measurable on-axis and listening-window curves
Evidence
These tests were totally different. Here, Olive has shown that listeners trained in recognizing resonances, the building blocks of the sounds we are interested in hearing, can describe, in physically measurable terms, why they prefer certain sounds. They told us that what they heard was best described by the on-axis and listening window curves—the direct sound. — Toole, Floyd E, Sound reproduction- the acoustics and…, 1227
Categorization
- XREF: Connects to the broader research stream on how trained listeners can verbalize perceptual dimensions in physically measurable terms, a marker of mature psychoacoustic modeling. — connects to: Trained listener panels differ systematically from the general audience; PIR predicts preference via smoothness cues, not the room curve itself; Controlled experiments trace sound-feature variables against listeners’ behavioral responses (+5 more)
- corroborated: 2 independent passages support this claim — Toole, Floyd E, Sound reproduction- the acoustics and…, 1227
Broader Connections
- Controlled experiments trace sound-feature variables against listeners’ behavioral responses
#expands(0.90) - Experimental method determines what discrepancies listeners can perceive
#expands(0.80) - Blind listening tests make subjective sound judgments objective and repeatable
#expands(0.78) — , Toole, Floyd E - Sound reproduction-…, 87 - Computational sound quality models are trained on subjective listening data
#expands(0.75) — Ville Pulkki, Matti Karjalainen, Communication Acoustics- An…, 2332 - Visual appearance of loudspeakers shifts listener preference ratings
#qualifies(0.75) — Toole, Floyd E, Sound reproduction- the acoustics and…, 1058 - Simple forced-choice listening tests order noise samples by annoyance
#exemplifies(0.72)