Sound localization by elevation is guided by plausibility, not just acoustics
Evidence
Vertical localization “blur” (uncertainty) is about 17° for an unfamiliar voice, about 9° for a familiar voice and 4° for white noise (Blauert, 1996). If we have no idea of the spectral content of a specific sound, there is no assurance that we would automatically perceive it at its true elevation. … We rely greatly on “plausibility” when localizing in complex situations. Aircraft are automatically assumed to be above us. Nothing is localized below the floor. Voices are where the lips are on the screen—the ventriloquism effect. If listeners are predisposed to think that sounds are coming from above, that is what they are likely to perceive; hence the emphasis on hovering helicopters and bird sounds in demonstrations of immersive sound systems. — , Toole, Floyd E - Sound reproduction-…, 1762
Categorization
- XREF: Plausibility-driven perception parallels Bayesian/perceptual-prior findings across vision and hearing, and connects to how augmented-reality and immersive audio systems engineer expectation. — connects to: Elevation localization is a timbral recognition task driven by resonant peaks — Toole, Floyd E, Sound reproduction- the acoustics and…, 744; Plausibility and expectation drive perceived vertical localization more than physical cues; Acoustical interference dips cannot serve as reliable elevation cues (+4 more)
Related
- Plausibility and expectation drive perceived vertical localization more than physical cues (similarity 0.97)
- Horizontal-plane localization is mature while elevation reproduction is newly addressed (similarity 0.93)
- Acoustical interference dips cannot serve as reliable elevation cues (similarity 0.93)
- Virtual source elevation perception is personal but azimuth is predictable (similarity 0.91)
- Elevation localization is a timbral recognition task driven by resonant peaks (similarity 0.89)
- Elevation localization rests on spectral recognition of familiar sounds, not spatial cues. (similarity 0.89)
- Listener expectations, not acoustic cues alone, drive perceived spatial location. (similarity 0.87)
- Horizontal localization uses binaural cues while vertical localization does not (similarity 0.82)
- Spectral elevation cues predict human sound-localization performance via machine learning (similarity 0.82)
- Horizontal localization is most acute straight ahead and degrades sharply to the sides. (similarity 0.81)
- Neural networks trained on one subject’s data can predict human sound-elevation localization (similarity 0.81)
- Neural networks trained on single-subject data can accurately model human elevation localization (similarity 0.80)
- Lateralization is one-dimensional while localization spans three dimensions (similarity 0.80)
- Auditory elevation calibration requires visual confirmation of sound sources (similarity 0.79)
- Sound localization is most accurate when facing the sound source (similarity 0.79)