Zhou Qing, Feng Zuren, Benetos Emmanouil
State Key Laboratory for Manufacturing Systems Engineering, School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
School of Electronic Engineering and Computer Science, Queen Mary University of London, London E1 4NS, UK.
Sensors (Basel). 2019 Jul 20;19(14):3206. doi: 10.3390/s19143206.
Sound event detection in real-world environments suffers from the interference of non-stationary and time-varying noise. This paper presents an adaptive noise reduction method for sound event detection based on non-negative matrix factorization (NMF). First, a scheme for noise dictionary learning from the input noisy signal is employed by the technique of robust NMF, which supports adaptation to noise variations. The estimated noise dictionary is used to develop a supervised source separation framework in combination with a pre-trained event dictionary. Second, to improve the separation quality, we extend the basic NMF model to a weighted form, with the aim of varying the relative importance of the different components when separating a target sound event from noise. With properly designed weights, the separation process is forced to rely more on those dominant event components, whereas the noise gets greatly suppressed. The proposed method is evaluated on a dataset of the rare sound event detection task of the DCASE 2017 challenge, and achieves comparable results to the top-ranking system based on convolutional recurrent neural networks (CRNNs). The proposed weighted NMF method shows an excellent noise reduction ability, and achieves an improvement of an F-score by 5%, compared to the unweighted approach.
现实环境中的声音事件检测受到非平稳和时变噪声的干扰。本文提出了一种基于非负矩阵分解（NMF）的声音事件检测自适应降噪方法。首先，采用鲁棒非负矩阵分解技术从输入的噪声信号中学习噪声字典，该技术支持对噪声变化的自适应。估计出的噪声字典与预训练的事件字典相结合，用于构建一个有监督的源分离框架。其次，为了提高分离质量，我们将基本的非负矩阵分解模型扩展为加权形式，目的是在从噪声中分离目标声音事件时改变不同分量的相对重要性。通过合理设计权重，分离过程被迫更多地依赖那些占主导地位的事件分量，而噪声则得到极大抑制。该方法在DCASE 2017挑战赛的罕见声音事件检测任务数据集上进行了评估，取得了与基于卷积循环神经网络（CRNN）的顶级系统相当的结果。所提出的加权非负矩阵分解方法显示出优异的降噪能力，与未加权方法相比，F分数提高了5%。
