A computational framework is proposed for analyzing the temporal evolution of perceptual attributes of sound stimuli. As a paradigm, the perceptual attribute of envelopment, which is manifested in different audio sound reproduction formats, is employed. For this, listener temporal ratings of the envelopment for mono, stereo, and 5.0-channel surround music samples, serve as the ground truth for establishing a computational model that can accurately trace temporal changes from such recordings. Combining established and heuristic methodologies, different features of the audio signals were extracted at each segment that envelopment ratings were registered, named long-term (LT) features.Amemory LT computational stage is proposed to account for the temporal variations of the features through the duration of the signal, based on the exponentially weighted moving average of the respective LT features. These are utilized in a gradient tree boosting, machine learning algorithm, leading to a Dynamic Model that accurately predicts the listener’s temporal envelopment ratings.Without the proposed memory LT feature function, a Static Model is also derived, which is shown to have lower performance for predicting such temporal envelopment variations.
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