The aim of this study is to implement a method to remove ambient noise in biomedical sounds captured in auscultation. We propose an incremental approach based on multichannel non-negative matrix partial co-factorization (NMPCF) for ambient denoising focusing on high noisy environment with a Signal-to-Noise Ratio (SNR) <= -5 dB. The first contribution applies NMPCF assuming that ambient noise can be modelled as repetitive sound events simultaneously found in two single-channel inputs captured by means of different recording devices. The second contribution proposes an incremental algorithm, based on the previous multichannel NMPCF, that refines the estimated biomedical spectrogram throughout a set of incremental stages by eliminating most of the ambient noise that was not removed in the previous stage at the expense of preserving most of the biomedical spectral content. The ambient denoising performance of the proposed method, compared to some of the most relevant state-of-the-art methods, has been evaluated using a set of recordings composed of biomedical sounds mixed with ambient noise that typically surrounds a medical consultation room to simulate high noisy environments with a SNR from -20 dB to -5 dB. Experimental results report that: (i) the performance drop suffered by the proposed method is lower compared to MSS and NLMS; (ii) unlike what happens with MSS and NLMS, the proposed method shows a stable trend of the average SDR and SIR results regardless of the type of ambient noise and the SNR level evaluated; and (iii) a remarkable advantage is the high robustness of the estimated biomedical sounds when the two single-channel inputs suffer from a delay between them.

翻译：本研究旨在实现一种消除听诊采集的生物医学声音中环境噪声的方法。针对信噪比（SNR）≤ -5 dB的高噪声环境，我们提出了一种基于多通道非负矩阵部分协同分解（NMPCF）的增量式环境噪声消除方法。第一项贡献在于应用NMPCF，其假设环境噪声可建模为重复性声学事件，并同时出现在通过不同录音设备采集的两个单通道输入中。第二项贡献提出了一种基于前述多通道NMPCF的增量算法，该算法通过一系列增量阶段逐步优化估计的生物医学声谱图：在尽可能保留生物医学频谱成分的前提下，逐阶段消除前一级未去除的大部分环境噪声。为评估所提方法的环境噪声消除性能，我们使用一组由生物医学声音与模拟高噪声环境（SNR范围-20 dB至-5 dB）的典型诊室环境噪声混合而成的录音，与若干最相关的先进方法进行比较。实验结果表明：（i）与MSS和NLMS相比，所提方法的性能下降幅度更低；（ii）不同于MSS和NLMS的表现，所提方法在不同类型环境噪声和不同SNR水平下均表现出稳定的平均SDR与SIR结果趋势；（iii）当两个单通道输入存在延迟时，所估计生物医学声音仍保持高度鲁棒性，这是该方法的显著优势。