论文标题
麦克风分类的光谱降解
Spectral Denoising for Microphone Classification
论文作者
论文摘要
在本文中，我们建议将denoising用于麦克风分类，以使其对涉及嘈杂条件的多个关键应用领域的使用情况。我们描述了用于麦克风分类的提议的分析管道和基线算法，并讨论了可以在时间或光谱域内应用于它的各种降解方法。最后，我们确定了表现最佳的剥离过程，并使用多个SNR级别的加法输入噪声评估了整体，集成方法的性能。结果，所提出的方法在参考基线的含量含量上的平均准确度增加了约25％。
In this paper, we propose the use of denoising for microphone classification, to enable its usage for several key application domains that involve noisy conditions. We describe the proposed analysis pipeline and the baseline algorithm for microphone classification, and discuss various denoising approaches which can be applied to it within the time or spectral domain; finally, we determine the best-performing denoising procedure, and evaluate the performance of the overall, integrated approach with several SNR levels of additive input noise. As a result, the proposed method achieves an average accuracy increase of about 25% on denoised content over the reference baseline.
