Audio recordings contain rich information about sound sources and their properties such as the location, loudness, and frequency of events. One prevalent component in sound recordings is the sound texture, which contains a massive number of events. In such a texture, there can be some distinct and repeated sounds that we term as a foreground sound. Birds chirping in the wind is one such decorative sound texture with the chirping as a foreground sound and the wind as a background texture. To render these decorative sound textures in real-time and with high quality, we create two-layer Markov Models to enable smooth transitions from sound grain to sound grain and propose a hierarchical scheme to generate Head-Related Transfer Function filters for localization cues of sounds represented as area/volume sources. Moreover, during the synthesis stage, we provide control over the frequency and intensity of sounds for customization. Lastly, foreground sounds are often blended into background textures such as the sound of rain splats on car surfaces becoming submerged in the background rain. We develop an extraction component that outperforms existing learning-based methods to facilitate our synthesis with perceptible foreground sounds and well-defined textures.

中文翻译：

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声景的装饰性声音纹理的实时渲染

录音包含有关声源及其属性的丰富信息，例如事件的位置、响度和频率。录音中的一个普遍成分是声音纹理，其中包含大量事件。在这样的纹理中，可能会有一些不同的重复声音，我们称之为前景声音。鸟儿在风中啁啾就是这样一种装饰性的声音纹理，唧唧声作为前景声音，风作为背景纹理。为了实时和高质量地渲染这些装饰性声音纹理，我们创建了两层马尔可夫模型以实现从声音颗粒到声音颗粒的平滑过渡，并提出了一种分层方案来生成用于声音定位线索的头部相关传递函数滤波器表示为面积/体积源。此外，在合成阶段，我们提供对声音频率和强度的控制以进行定制。最后，前景声音经常被混合到背景纹理中，例如汽车表面的雨滴声淹没在背景雨中。我们开发了一个提取组件，它优于现有的基于学习的方法，以促进我们与可感知的前景声音和明确定义的纹理的合成。