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A robust howling detection algorithm based on a statistical approach

机译:基于统计方法的鲁棒啸叫检测算法

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This paper presents an algorithm for the detection of howlings that arise in audio signals. Our method is based on the combination of two energy-based features and one new feature related to the frequency stability of a howling component. The decision stage, which implies a Support Vector Machine (SVM) model, outputs a decision every 20 ms. The evaluation, carried out on a large database, showed that the algorithm is able to detect both pure and multiple tones howling in a wide range of energy. Furthermore, even on complex signals such as music, the detection is still efficient with very few false alarms.
机译:本文提出了一种用于检测音频信号中出现的啸声的算法。我们的方法基于两个基于能量的特征和一个与啸叫分量的频率稳定性有关的新特征的组合。决策阶段(表示支持向量机(SVM)模型)每20毫秒输出一次决策。在大型数据库上进行的评估表明,该算法能够检测多种能量范围内的纯音和多音。此外,即使在诸如音乐之类的复杂信号上,检测仍然非常有效,几乎没有错误警报。

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