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Temporal entropy-based texturedness indicator for audio signals

In this paper, we present a temporal entropy-based indicator that reflects the texturedness level of a given audio signal. Inspired from an image homogeneity evaluation via a multidirectional cumulative entropy computation, we similarly propose an audio signal homogeneity analysis through a direct and reverse progressive auditory information content tracking. A [0 - 5] texturedness indicator is then constructed using auditory-inspired parameters, and is inherently associated with short listening time. Using this indicator, a new audio signals classification is proposed where speech signals are assigned low texturedness scores, and academic noise signals are assigned the higher range of texturedness scores. Classically known audio textures are assigned, in this scale, various intermediate to high texturedness scores.