强干扰环境下声源可视化重建方法的研究进展

准确识别噪声源是机电产品噪声控制的关键,其中,近场声全息和波束形成是两种常用的声源可视化重建方法,分别适用于近场低频和远场高频声源重建的情况。传统的声全息和波束形成方法基于自由场假设,即适用于目标声源辐射声与干扰噪声之间的信噪比大于10 dB的情况。然而很多机电产品的噪声测试只能在工作现场进行,不满足自由场条件。为此,从声学传播方程和信号处理两个方面出发,回顾了强干扰环境下声源可视化重建方法的研究发展历程,评点了每种方法的特点和适用范围。重点介绍了强干扰环境下的近场声全息方法,包括声场分离法和逆块传递函数法。另外,还介绍了混响环境下的声源重建方法以及基于信号处理的信号噪声分离方法。最后,讨论了强干扰环境下声源重建有待解决的问题及其发展趋势。     

Binaural Speech Separation Algorithm Based on Long and Short Time Memory Networks

Speaker separation in complex acoustic environment is one of challenging tasks in speech separation. In practice, speakers are very often unmoving or moving slowly in normal communication. In this case, the spatial features among the consecutive speech frames become highly correlated such that it is helpful for speaker separation by providing additional spatial information. To fully exploit this information, we design a separation system on Recurrent Neural Network (RNN) with long short-term memory (LSTM) which effectively learns the temporal dynamics of spatial features. In detail, a LSTM-based speaker separation algorithm is proposed to extract the spatial features in each time-frequency (TF) unit and form the corresponding feature vector. Then, we treat speaker separation as a supervised learning problem, where a modified ideal ratio mask (IRM) is defined as the training function during LSTM learning. Simulations show that the proposed system achieves attractive separation performance in noisy and reverberant environments. Specifically, during the untrained acoustic test with limited priors, e.g., unmatched signal to noise ratio (SNR) and reverberation, the proposed LSTM based algorithm can still outperforms the existing DNN based method in the measures of PESQ and STOI. It indicates our method is more robust in untrained conditions.     

Cutting force denoising in micro-milling tool condition monitoring

An independent component analysis (ICA) algorithm for cutting force denoising was applied in micro-milling tool condition monitoring. In micro-milling, the comparatively small cutting force signal is prone to contamination by relatively large noise, and as a result it is important to denoise the force signal before further processing it. However, the traditional denoising methods, based on Gaussian noise assumption, lose here because the noise is identified as containing a high non-Gaussian component in the experiment. ICA was recently developed to deal with the blind source separation (BSS) problem. It solves the BSS problem by measuring the non-Gaussianity of the signal and it is particularly effective in the separation of non-Gaussian signals. This approach employs fixed-point ICA (FastICA), assuming the noises are sources and the force signal is an instantaneous mixture of sources and by treating the signal denoising process as a BSS. The results are illustrated both in time and frequency domains. The FastICA denoising performances are compared with the popular wavelet thresholding. The results show that FastICA performs better than wavelet. Theoretical discussion of the nature of ICA and wavelet thresholding supports the results: ICA separates both Gaussian and non-Gaussian noise sources, while wavelet only suppresses Gaussian noise.     

Blind source separation: a new pre-processing tool for rotating machines monitoring?

Blind source separation (BSS) is a general signal processing method, which consists of recovering from a finite set of observations recorded by sensors, the contributions of different physical sources independently of the propagation medium and without any a priori knowledge of the sources. Recently, these methods paved a new way for the monitoring or the diagnosis of mechanical systems in a working environment. Actually, we show that BSS allows recovering the vibratory information issued from a single rotating machine working in a noisy environment by freeing the sensor signal from the contribution of other working machines. In that way, BSS can be used as a pre-processing step for rotating machine fault detection and diagnosis.     

水下耦合机械噪声源的量化识别及试验

目前水下机械噪声源及其传递路径识别效果较难。为此,将盲源分离算法和传递路径分析方法融合和集成。视多振源信号为卷积混叠,结合LU分解,提出一种新的非正交联合块对角化方法进行耦合振动源的分离。将分离振源作为工况传递路径分析方法的输入振源,建立水下机械振动噪声源识别算法,并对潜艇舱段模型的水下振动-声辐射试验对算法进行验证。结果表明,与现存方法相比,该盲源分离算法具有易实现、收敛速度快、精度高等优点;所集成的源识别算法在水下声场预报和振源贡献量排序中的性能均优于振源耦合时的结果,与实际情况吻合好,达到了高效、准确地识别机械噪声源的目的。     

Experimental evaluation of a nested microphone array with adaptive noise cancellers

This paper proposes a near-field broadband adaptive beamforming scheme for intelligent computer telephony and teleconferencing applications, namely the nested microphone array with adaptive noise canceller (NMA-ANC). The NMA-ANC scheme incorporates an harmonically nested array with a nonuniformly subbanded multirate filter bank. Each subband array employs several near-field delay-filter-and-sum beamformers and an adaptive noise canceller (ANC). The proposed NMA-ANC is evaluated via a noise rejection experiment and dereverberation experiment performed in an anechoic chamber and a real conference room, respectively. The experiment data are recorded by a multichannel digital recording system developed using commercial off-the-shelf (COTS) equipments. A perceptual analysis/measurement system (PAMS) test is also carried out using a COTS digital speech level analyzer. The results of the experimental evaluation and PAMS test show that the proposed NMA-ANC scheme is able to improve the sound quality by adaptively rejecting multiple interfering signals and attenuating the reverberant noises and avoiding the desired signal cancellation.     

基于波束域相干信号子空间法的宽带源小尺度基阵定向

在给定的小尺度平面四元阵的基础上,主要研究了波束域相关信号子空间算法(BeamSpaceCoherentSignal-subspaceMethod—BSCSM)在宽带源定向中的应用以及性能分析。用实测的直升机噪声仿真产生各个阵元上输出的宽带信号源,并且使得这组信号具有特定的波达方向(DirectionofArrival—DOA)。分别使用波束域相干信号子空间法(BSCSM)和阵元域的相干信号子空间法(CoherentSignal-subspaceMethod—CSM)与非相干信号子空间法(InCoherentSignal-subspaceMethod—ICSM),通过计算机仿真实验对其进行方位估计。在仿真实验中,可以直观地看出三种方法的性能优劣。根据计算机仿真结果,统计意义上的误差分析,以及对宽带平面波波达角度的角度分辨率等性能指标的分析比较,可以得出在小尺度基阵定向研究中,波束域方法能够获得比其他两种阵元域算法更好的性能。     

Design of a MEMS acoustical beamforming sensor microarray

This paper describes the design methodology for a microelectromechanical systems (MEMS)-based acoustical beamforming sensor microarray. The proposed acoustical array offers the potential of controlled directional sensitivity with constant beamwidth when used in conjunction with the appropriate digital signal processor. The array has been designed for use in a hearing instrument with a digital beamsteering engine to provide controlled directional sensitivity and constant beamwidth over the audio frequency range to improve speech intelligibility in noisy and reverberant environments. A MEMS-based packaging solution that allows the sensor array to be mounted in the ear canal is also described. The MEMS sensor-package interface features microspring contacts that enable low impedance connectivity between the sensor array and the related microelectronics. This allows the array to be easily removed for cleaning or replacement purposes.     

相关机械振源的盲源分离方法

相关源不满足独立分量分析关于源的统计独立性假设,标准的独立分量分析方法无法准确分离相关机械振源信号。在相关振源信号的部分频带满足统计独立的假设前提下,提出了一种基于小波包分解的相关机械源盲源分离方法。该方法将观测信号用小波包分解成子带观测信号,根据互信息标准选择相关性较小的若干子带观测信号重构观测信号。通过重构的观测信号的独立分量分析估计分离矩阵,然后用该矩阵分离原始观测信号从而实现相关机械振源信号的分离。仿真试验验证了该方法的有效性。     

The Research of Blind Source Separation (BSS)in Machinery Fault Diagnosis

1 Introduction：While a machine is running, it will create vibration, and also strong noise. Commonly, when the vibration is more intense, the noise is greater. Just like the vibration signals, the sound signals of the machinery contain affluent facility status information, so the sound signals can also be used in the machinery fault diagnosis. We call the technology the sound fault diagnosis which uses the sound signals in fault diagnosis. Even more, using the sound signal in fault diagnosis…