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Example-Guided Physically Based Modal Sound Synthesis

机译:实例指导的基于模态声音的合成

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Linear modal synthesis methods have often been used to generate sounds for rigid bodies. One of the key challenges in widely adopting such techniques is the lack of automatic determination of satisfactory material parameters that recreate realistic audio quality of sounding materials. We introduce a novel method using prerecorded audio clips to estimate material parameters that capture the inherent quality of recorded sounding materials. Our method extracts perceptually salient features from audio examples. Based on psychoacoustic principles, we design a parameter estimation algorithm using an optimization framework and these salient features to guide the search of the best material parameters for modal synthesis. We also present a method that compensates for the differences between the real-world recording and sound synthesized using solely linear modal synthesis models to create the final synthesized audio. The resulting audio generated from this sound synthesis pipeline well preserves the same sense of material as a recorded audio example. Moreover, both the estimated material parameters and the residual compensation naturally transfer to virtual objects of different sizes and shapes, while the synthesized sounds vary accordingly. A perceptual study shows the results of this system compare well with real-world recordings in terms of material perception.
机译:线性模态合成方法通常用于生成刚体的声音。广泛采用此类技术的主要挑战之一是缺乏自动确定令人满意的素材参数的方法,这些参数无法再现逼真的素材的真实音频质量。我们介绍了一种使用预先录制的音频剪辑来估计捕获录制的声音材料固有质量的材料参数的新颖方法。我们的方法从音频示例中提取出感知上显着的特征。基于心理声学原理,我们使用优化框架和这些显着特征设计了一种参数估计算法,以指导寻找模态合成的最佳材料参数。我们还提出了一种方法,该方法可以补偿使用纯线性模态合成模型来创建最终合成音频的真实录音与合成声音之间的差异。从此声音合成管道生成的最终音频很好地保留了与录制的音频示例相同的素材感。而且,估计的材料参数和残余补偿自然地传递到不同大小和形状的虚拟物体,而合成声音相应地变化。一项感知研究表明,该系统的结果与真实录音在材料感知方面相比具有良好的对比。

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