Loudness of time-varying environmental sounds: Still a challenge for current loudness models?
Otto von Guericke University Magdeburg
Wednesday 3 june, 2015, 10:00 - 10:20
0.6 Madrid (49)
Environmental sounds are often non-stationary, i.e., they vary in their physical properties over time. A prominent example is speech but also various technical sounds have such a non-stationary character. The present study focusses on the loudness of these sounds by comparing own measured loudness data with predictions of existing loudness models. Dynamic loudness models commonly assume that the instantaneous loudness is smoothed with one or more time constants and that the overall loudness is determined by the maximum or a certain percentile of this smoothed loudness time function. The time constant and the measures used to derive overall loudness differ between the different loudness models. For example, the model of Chalupper and Fastl (2002) uses a 8-Hz low-pass filter to derive a short-term loudness time function. The model of time-varying loudness by Glasberg and Moore (2002) even proposes two loudness measures within the same model, the short-term and long-term loudness, where the latter is determined by longer time constants than the former. This long-term loudness was specifically developed to predict the loudness of amplitude modulated sounds. It is, however, unclear which measure is more appropriate to predict loudness of environmental sounds. Levels at equal loudness between reference sounds and speech as well as technical sounds were measured and compared to the predicted overall loudness of the two models mentioned above. The data indicate that loudness of speech-like signals seems to be largely determined by a loudness function that was smoothed with longer time constants than currently used in dynamic models, since the long-term spectrum largely determined the loudness. The loudness of time-varying, technical signals could not be fully predicted with any of the existing models but again longer time-constants appear to be beneficial for the prediction. However, discrepancies remain.
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