Self-Subtraction Network for End to End Noise Robust Classification

Donghyeon Kim, D. Han, Hanseok Ko
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Abstract

Acoustic event classification in surveillance applications typically employs deep learning-based end-to-end learning methods. In real environments, their performance degrades significantly due to noise. While various approaches have been proposed to overcome the noise problem, most of these methodologies rely on supervised learning-based feature representation. Supervised learning system, however, requires a pair of noise free and noisy audio streams. Acquisition of ground truth and noisy acoustic event data requires significant efforts to adequately capture the varieties of noise types for training. This paper proposes a novel supervised learning method for noise robust acoustic event classification in an end-to-end framework named Self Subtraction Network (SSN). SSN extracts noise features from an input audio spectrogram and removes them from the input using LSTMs and an auto-encoder. Our method applied to Urbansound8k dataset with 8 noise types at four different levels demonstrates improved performances compared to the state of the art methods.
端到端噪声鲁棒分类的自减法网络
监控应用中的声事件分类通常采用基于深度学习的端到端学习方法。在真实环境中,由于噪声的影响,它们的性能会显著下降。虽然已经提出了各种方法来克服噪声问题,但这些方法大多依赖于基于监督学习的特征表示。然而,监督学习系统需要一对无噪声和有噪声的音频流。获取地面真值和噪声声学事件数据需要大量的努力来充分捕获各种噪声类型进行训练。本文提出了一种基于端到端自减法网络(SSN)框架的噪声鲁棒声事件分类的监督学习方法。SSN从输入音频频谱图中提取噪声特征,并使用lstm和自动编码器从输入中删除它们。我们的方法应用于具有4个不同级别的8种噪声类型的Urbansound8k数据集,与最先进的方法相比,性能得到了改善。
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