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Time-Scale Atoms Chains for Transients Detection in Audio Signals

机译:用于音频信号瞬态检测的时标原子链

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摘要

This paper presents a novel approach for the extraction of the transients content of audio signals, usually represented as superposition of stationary, transient, and stochastic components. The proposed model exploits the predictable and peculiar time-scale behavior of transients by modeling them as superposition of suitable wavelet atoms. These latter allow to predict transients information even at scales where the tonal component is dominant. In this way it is possible to avoid, if required, the pre-analysis of the tonal component. Extensive experimental results show that the proposed model achieves good performances with a moderate computational effort and without any user's dependence.
机译:本文提出了一种提取音频信号瞬态内容的新颖方法,通常表示为静态,瞬态和随机分量的叠加。所提出的模型通过将瞬态建模为合适的小波原子的叠加,来利用瞬态的可预测且特殊的时标行为。后者可以预测瞬态信息,即使在音调成分占主导的音阶也是如此。通过这种方式,可以在需要时避免对音调成分进行预先分析。大量的实验结果表明,所提出的模型以适度的计算工作量获得了良好的性能,并且没有任何用户的依赖。

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