Measurement research aims to map objective data onto subjective sound quality ratings

Evidence

Just as the notation of music is now being replaced by the record player, the tape recorder is pushing the physical study of acoustics into the human area of psychoacoustics. And for general field work in measuring the intensity of sound, the flat response of the sound level meter (the so-called C scale) has given way to the contoured A scale, weighted along the lines of the equal loudness contours of the human ear. Similarly, the numerous refinements in measuring aircraft noise in recent years have tended more and more to take human response into consideration. Thus EPNdB (Effective Perceived Noise in Decibels) takes into account the particular tone components of aircraft as well as the duration of the noise, while NNI (Noise and Number Index) uses the EPNdB as a basis and additionally calculates the number of aircraft per day (or night) as a key annoyance factor. — Schafer, Murray R, The Soundscape- Our Sonic Environment…, 440 (#expands, via linked claim)

The problem with subjective evaluation is that the required experiments are typically very laborious and time consuming. The experiments should be carried out in conditions that correspond to real usage of the sounds or related products. Thus, it would be much easier and less expensive to use objective criteria or models for sound quality. Ideally, a computational model provides an estimate of quality from signal analysis which correlates well with subjective data. The development of such a model (Figure 17.1) is typically based on proper subjective listening experiments from which data are collected and analysed statistically. Using various techniques, the factors and features of sounds that best explain the subjective behaviour are sought. The computational model can be a simple linear regression model or a more complex non-linear model, such as a neural network trained to map the feature parameters to quality indices. — Ville Pulkki, Matti Karjalainen, Communication Acoustics- An…, 2332 (#expands, via linked claim)

Categorization

• XREF: connects to: Computational auditory models serve knowledge, audio-quality measurement, and diagnostics — Ville Pulkki, Matti Karjalainen, Communication Acoustics- An…, 821; Subjective experience dictates which measurements matter in audio research; Predicting subjective listening preference from measured data remains a hard unsolved problem. (+11 more)

Broader Connections

• Computational sound quality models are trained on subjective listening data #expands (0.85) — Ville Pulkki, Matti Karjalainen, Communication Acoustics- An…, 2332
• Sound measurement has shifted from flat physical scales toward human-perception-weighted scales #expands (0.85) — Schafer, Murray R, The Soundscape- Our Sonic Environment…, 440
• Sound quality is fundamentally subjective and requires perceptual testing to characterize #expands (0.80)
• Curve smoothness and flatness track perceived sound quality ratings #exemplifies (0.80)
• Auditory-model difference measures predict audible quality degradation #exemplifies (0.80)
• Percentile loudness N5 predicts how listeners judge overall noise immission loudness #exemplifies (0.80) — Professor Dr.-Ing. Hugo Fastl, Professor Dr.-Ing. Eberhard, Psychoacoustics- Facts and Models, 1799
• Blind listening tests make subjective sound judgments objective and repeatable #expands (0.80) — , Toole, Floyd E - Sound reproduction-…, 87
• Objective sound quality models have limited domains of validity and cannot replace subjective testing #complicates (0.75) — Ville Pulkki, Matti Karjalainen, Communication Acoustics- An…, 2337
• Anechoic on-axis and sound power responses suffice to predict perceived loudspeaker quality #exemplifies (0.75) — , Toole, Floyd E - Sound reproduction-…, 1292
• Multidimensional scaling maps perceived timbre differences between instruments #exemplifies (0.75) — David M. Howard, Jamie Angus, Acoustics and Psychoacoustics, 1482