Synthesizing sound textures through wavelet tree learning

Natural sounds are complex phenomena because they typically contain a mixture of events localized in time and frequency. Moreover, dependencies exist across different time scales and frequency bands, which are important for proper sound characterization. Historically, acoustical theorists have represented sound in numerous ways. Our research has focused on a granular method of sonic analysis, which views sound as a series of short, distinct bursts of energy. Using that theory, this article presents a statistical learning algorithm for synthesizing new random instances of natural sounds.