Synthesizing sound textures through wavelet tree learning

Dubnov S.; Bar-Joseph Z.; El-Yaniv R.; Lischinski D.; Werman M.

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Synthesizing sound textures through wavelet tree learning

机译：通过小波树学习合成声音纹理

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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.

机译：自然声音是复杂的现象，因为它们通常包含时间和频率局部的混合事件。此外，在不同的时标和频带上存在依赖性，这对于正确的声音表征很重要。从历史上看，声学理论家以多种方式表示声音。我们的研究集中于声音分析的一种粒度方法，该方法将声音视为一系列短而独特的能量爆发。使用该理论，本文提出了一种统计学习算法，用于合成自然声音的新随机实例。

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来源
《IEEE Computer Graphics and Applications》 |2002年第4期|p.38-48|共11页
作者
Dubnov S.; Bar-Joseph Z.; El-Yaniv R.; Lischinski D.; Werman M.;
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作者单位

Commun. Syst. Eng. Dept., Ben-Gurion Univ., Israel;

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原文格式 PDF
正文语种 eng
中图分类无线电电子学、电信技术;
关键词
wavelet transforms; acoustic signal processing; virtual reality; learning (artificial intelligence); trees (mathematics); sound texture synthesis; wavelet tree learning; statistical learning algorithm; natural sounds; random instances; sonic analysis;

机译：小波变换声音信号处理;虚拟现实;学习（人工智能）;树木（数学）;声音纹理合成;小波树学习;统计学习算法;自然的声音;随机实例;声波分析;

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Synthesizing sound textures through wavelet tree learning

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