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Fractal Additive Synthesis

机译：分形加成合成

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In this article, we outlined time-frequency techniques that, when brought together, form the basis for FAS. The method is based on an exact orthogonal transform, the HBWT. The FAS model can be seen as an intelligent spectrogram, i.e., as a spectrogram where the frequency bins (the HBWT subbands) are adapted to the spectrum of the analyzed sound by tuning the number of channels P to the period of the sound. Local cosines replace the classical windowed complex exponentials, whose main property is that the basis elements form sidebands of the harmonics rather than being centered on the harmonics themselves. The wavelet transform nonuniform frequency subdivision characteristic is exploited to synthesize each sideband of the harmonic peaks by means of colored noise, generating an approximate pseudoperiodic 1/f behavior. The pseudoharmonics are modeled by narrow sidebands whose weights can be generated by means of amplitude envelopes and phase functions in a complexified HBWT domain. We also showed how perceptual criteria can be employed to reduce the number of synthesis parameters. The possibility of independently controlling the noisy components and the harmonic terms and the definition of parameters such as volumes and envelopes for all of the components independently provide powerful tools for processing voiced-sound for sound design purposes
机译：在本文中，我们概述了时频技术，这些技术结合在一起构成了FAS的基础。该方法基于精确的正交变换HBWT。 FAS模型可以看作是智能频谱图，即频谱图，其中通过将通道P的数量调整为声音周期，频率点（HBWT子带）适合于所分析声音的频谱。局部余弦代替了经典的开窗复数指数，其主要特性是基元形成了谐波的边带，而不是以谐波本身为中心。利用小波变换非均匀频率细分特性，通过有色噪声合成谐波峰值的每个边带，从而产生近似伪周期的1 / f行为。伪谐波是通过窄边带建模的，这些边带的权重可以通过复杂的HBWT域中的幅度包络和相位函数产生。我们还展示了如何使用感知标准来减少合成参数的数量。独立控制高噪声成分和谐波项的可能性以及所有成分的参数（例如音量和包络）的定义独立地提供了强大的工具，可处理声音以实现声音设计目的

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