Audio Event Recognition in Noisy Environments using Power Spectral Density and Dimensionality Reduction

Md Siddat Bin Nesar, Bradley M. Whitaker
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引用次数: 0

Abstract

Researchers are showing great interest in audio event detection due to its applications in surveillance, audio forensics, and other areas. However, one challenge in event detection is the usual presence of noisy environments. In this paper, we propose a robust system that is reliable when trained on quiet or noisy conditions. Another problem arises when considering the computational costs of collecting and analyzing long audio signals. In this work, we use power spectral density (PSD) and mel-frequency cepstral coefficients (MFCC) for feature extraction. and apply feature transformation and selection techniques to reduce the dimension significantly. Our system exhibits an overall accuracy of 99.05% with the raw features, and 87.10% with a significantly reduced number of features.
基于功率谱密度和降维的噪声环境下音频事件识别
由于音频事件检测在监控、音频取证等领域的应用,引起了研究人员的极大兴趣。然而,事件检测中的一个挑战是通常存在噪声环境。在本文中,我们提出了一个鲁棒系统,无论在安静或嘈杂的条件下训练都是可靠的。另一个问题出现在考虑收集和分析长音频信号的计算成本时。在这项工作中,我们使用功率谱密度(PSD)和梅尔频率倒谱系数(MFCC)进行特征提取。并应用特征变换和选择技术对图像进行了显著降维。我们的系统在原始特征上的总体准确率为99.05%,在显著减少特征数量时的总体准确率为87.10%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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