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首页> 外文会议>Proceedings of the 1st ACM workshop on Audio and music computing multimedia >Audio brush
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Audio brush

机译：音频刷

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摘要

Starting with a novel audio analysis and editing paradigm, a set of new and adaptive audio analysis and editing algorithms in the spectrogram are developed and integrated into a smart visual audio editing tool in a "what you see is what you hear" style. At the core of our algorithms and methods is a very?exible audio spectrogram that goes beyond FFT and Wavelets and supports manipulating a signal at any chosen time-frequency resolution:the Gabor analysis and synthesis. It gives maximum accuracy of the representation, is fully invertible, and enables resolution zooming. Simple audio objects are localized in time and frequency. They can easily be identified visually and selected by simple geometric selection masks such as rectangles, combs and polygons. For many audio objects, however the structures in the spectrogram are rather complex. Therefore, we present several intelligent and adaptive mask selection approaches. They are based on audio fingerprinting and visual pattern matching algorithms. Spectrograms of individually recorded sounds under controlled conditions or interactively selected in the current spectrogram can be regarded as visual and sophisticated templates. We discuss how to generate templates, how to find the best match out of a database and how to adapt the match to the sound which we want to edit.
机译：从新颖的音频分析和编辑范例开始，开发了频谱图中的一组新的自适应音频分析和编辑算法，并以“所见即所得”的样式将其集成到智能可视音频编辑工具中。我们的算法和方法的核心是一个非常灵活的音频频谱图，它超越了FFT和小波，并支持以任意选定的时频分辨率处理信号：Gabor分析和合成。它提供了最大的表示精度，是完全可反转的，并且可以实现分辨率缩放。简单的音频对象按时间和频率定位。可以很容易地从视觉上识别它们，并可以通过简单的几何选择蒙版（例如矩形，梳子和多边形）进行选择。然而，对于许多音频对象，声谱图中的结构相当复杂。因此，我们提出了几种智能和自适应掩模选择方法。它们基于音频指纹识别和视觉模式匹配算法。在受控条件下或在当前频谱图中交互选择的单独录制的声音的频谱图可以视为可视化和复杂的模板。我们讨论了如何生成模板，如何从数据库中找到最佳匹配以及如何使匹配适合我们要编辑的声音。

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