Since the evaluation of audio systems or processing schemes is time-consuming and resource-expensive, alternative objective evaluation methods attracted considerable research interests. However, current perceptual models are not yet capable of replacing a human listener especially when the test stimulus is complex, for example, a sound scene consisting of time-varying multiple acoustic images. This paper describes a data-driven approach to develop a model to predict the subjective evaluation of complex acoustic scenes, where the extensive set of listening test results collected in the latest MPEG-H 3-D audio initiative was used as training data. The results showed that a few selected outputs of various auditory models may be a useful set of features, where linear regression and multilayer perceptron models reasonably predicted the overall distribution of listening test scores, estimating both mean and variance.
Authors:
Härmä, Aki; Park, Munhum; Kohlrausch, Armin
Affiliation:
Philips Research Europe, Eindhoven, The Netherlands
AES Convention:
136 (April 2014)
Paper Number:
9025
Publication Date:
April 25, 2014
Subject:
Perception
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