Long-term averaging obscures transient attack and release sounds

Evidence

Section 10.2 introduced a simple loudness model that accurately estimates the loudness perception evoked by many relatively simple signals. Such models have been found to be unable to derive accurate loudness estimates for spectrally complex and time-variant signals. This shortcoming has motivated the design of more advanced loudness models (Florentine et al., 2005), of which two approaches are briefly touched upon next. The first one (Zwicker, 1977) processes the signal in the time domain and produces a continuous signal representing the loudness as a function of time. This approach opens up the possibility of predicting the loudness perception evoked by a time-variant signal based on the peak values in the model output. The second model (Glasberg and Moore, 2002) divides the signal into overlapping time frames and extracts short-term loudness values from each time frame. The model also emulates the temporal integration of loudness between adjacent time frames, and consequently, the model provides accurate estimates for time-variant signals as well, although this estimate is derived by simply averaging across the short-term loudness values. Despite the improved accuracy in predicting perceived loudness of complex signals, the different models cannot yet fully explain loudness perception. — Ville Pulkki, Matti Karjalainen, Communication Acoustics- An…, 1659 (#expands, via linked claim)

Categorization

• XREF: connects to: Standard signal-detection measures fail for time-evolving detection tasks — Dijk, Pim van, Physiology, Psychoacoustics and…, 3327; Long-term averaging of spectra suppresses random background noise — David M. Howard, Jamie Angus, Acoustics and Psychoacoustics, 1431; Instrument identity depends largely on attack and release transients, not steady tone (+4 more)

Related

• Two competing loudness models predict time-variant loudness by peak tracking or frame averaging (similarity 0.89)
• Sound level meters trade average level against tracking speed via time response. (similarity 0.87)
• Perceived loudness of time-varying sounds tracks near-peak levels, not averages (similarity 0.85)
• Static loudness models fail on time-variant sounds, motivating frame-based temporal integration models (similarity 0.85)
• Loudness models need 100-200 ms time constants to capture temporal integration and post-masking. (similarity 0.81)
• A simple high-pass filter can replace elaborate loudness models (similarity 0.80)
• Perceived loudness of time-varying sounds is set by short-time peak values. (similarity 0.79)
• Long-term averaging of spectra suppresses random background noise (similarity 0.79)
• Hearing is highly sensitive to temporal changes in sound (similarity 0.78)

Broader Connections

• Octave-band averaging can hide perceptually relevant acoustic variations #expands (0.88)
• Two competing loudness models predict time-variant loudness by peak tracking or frame averaging #expands (0.82) — Ville Pulkki, Matti Karjalainen, Communication Acoustics- An…, 1659