Nano-volt magnetic resonance sounding(MRS) signals are sufficiently weak so that during the actual measurement, they are affected by environmental electromagnetic noise, leading to inaccuracy of the extracted characte...Nano-volt magnetic resonance sounding(MRS) signals are sufficiently weak so that during the actual measurement, they are affected by environmental electromagnetic noise, leading to inaccuracy of the extracted characteristic parameters and hindering effective inverse interpretation. Considering the complexity and non-homogeneous spatial distribution of environmental noise and based on the theory of adaptive noise cancellation, a model system for noise cancellation using multi-reference coils was constructed to receive MRS signals. The feasibility of this system with theoretical calculation and experiments was analyzed and a modified sigmoid variable step size least mean square(SVSLMS) algorithm for noise cancellation was presented. The simulation results show that, the multi-reference coil method performs better than the single one on both signal-to-noise ratio(SNR) improvement and signal waveform optimization after filtering, under the condition of different noise correlations in the reference coils and primary detecting coils and different SNRs. In particular, when the noise correlation is poor and the SNR<0, the SNR can be improved by more than 8 dB after filtering with multi-reference coils. And the average fitting errors for initial amplitude and relaxation time are within 5%. Compared with the normalized least mean square(NLMS) algorithm and multichannel Wiener filter and processing field test data, the effectiveness of the proposed method is verified.展开更多
The acoustic vibration signal of tank is disassembled into the sum of intrinsic mode function （IMF） by multi-resolution empirical mode decomposition （EMD） method. The instantaneous frequency is obtained, and featu...The acoustic vibration signal of tank is disassembled into the sum of intrinsic mode function （IMF） by multi-resolution empirical mode decomposition （EMD） method. The instantaneous frequency is obtained, and feature transformation matrix is figured out by class scatter matrix. Multi- dimensional scale energy vector is mapped into low-dimensional eigenvector, and classification extraction is realized. This method sufficiently separates of different sound target features. The test result indicates that it is effective.展开更多
The paper proposes a new method of multi-band signal reconstruction based on Orthogonal Matching Pursuit(OMP),which aims to develop a robust Ecological Sounds Recognition(ESR)system.Firstly,the OMP is employed to spar...The paper proposes a new method of multi-band signal reconstruction based on Orthogonal Matching Pursuit(OMP),which aims to develop a robust Ecological Sounds Recognition(ESR)system.Firstly,the OMP is employed to sparsely decompose the original signal,thus the high correlation components are retained to reconstruct in the first stage.Then,according to the frequency distribution of both foreground sound and background noise,the signal can be compensated by the residual components in the second stage.Via the two-stage reconstruction,high non-stationary noises are effectively reduced,and the reconstruction precision of foreground sound is improved.At recognition stage,we employ deep belief networks to model the composite feature sets extracted from reconstructed signal.The experimental results show that the proposed approach achieved superior recognition performance on 60 classes of ecological sounds in different environments under different Signal-to-Noise Ratio(SNR),compared with the existing method.展开更多
波达方向(Direction of Arrival,DOA)估计技术是语音增强和声学探测中的重要工具,对于语音机器人、视频会议、助听器和声呐等应用至关重要。最近出现的DOA估计新方法,例如图信号处理(Graph Signal Processing,GSP)方法,展现出优异的角...波达方向(Direction of Arrival,DOA)估计技术是语音增强和声学探测中的重要工具,对于语音机器人、视频会议、助听器和声呐等应用至关重要。最近出现的DOA估计新方法,例如图信号处理(Graph Signal Processing,GSP)方法,展现出优异的角度估计能力,有望提供更佳的声源DOA估计解决方案。然而,由于在多声源情况下GSP算法由邻接矩阵无法直接得到接收信号特征向量的正交补矩阵,导致多声源下GSP算法失效。为解决此问题,本文基于多源宽带语音信号的频域单源区域检测实现多声源分离,进而利用GSP和聚类算法实现宽带多声源的定位。具体而言,本文首先将GSP方法扩展到频域。其次,利用短时傅里叶变换将信号分为若干时频区域,筛选出单源主导的时频区域后,对其进行频域GSP单源定位。最后,对所有定位结果进行聚类,再通过加权平均获得最终的角度估计。我们利用LibriSpeech语音语料库构建声源信号进行多声源定位仿真,仿真结果证明,本文方法优于其他算法,较高信噪比下可将误差控制在3°以内。此外,我们使用圆形六阵元麦克风阵列,对实际录制的若干组录音数据应用所提算法进行定位测量,结果展示所提算法的定位误差更小,并在声源较为靠近时也能做到较好的分辨。展开更多
基金Projects(41204079,41504086)supported by the National Natural Science Foundation of ChinaProject(20160101281JC)supported by the Natural Science Foundation of Jilin Province,ChinaProjects(2016M590258,2015T80301)supported by the Postdoctoral Science Foundation of China
文摘Nano-volt magnetic resonance sounding(MRS) signals are sufficiently weak so that during the actual measurement, they are affected by environmental electromagnetic noise, leading to inaccuracy of the extracted characteristic parameters and hindering effective inverse interpretation. Considering the complexity and non-homogeneous spatial distribution of environmental noise and based on the theory of adaptive noise cancellation, a model system for noise cancellation using multi-reference coils was constructed to receive MRS signals. The feasibility of this system with theoretical calculation and experiments was analyzed and a modified sigmoid variable step size least mean square(SVSLMS) algorithm for noise cancellation was presented. The simulation results show that, the multi-reference coil method performs better than the single one on both signal-to-noise ratio(SNR) improvement and signal waveform optimization after filtering, under the condition of different noise correlations in the reference coils and primary detecting coils and different SNRs. In particular, when the noise correlation is poor and the SNR<0, the SNR can be improved by more than 8 dB after filtering with multi-reference coils. And the average fitting errors for initial amplitude and relaxation time are within 5%. Compared with the normalized least mean square(NLMS) algorithm and multichannel Wiener filter and processing field test data, the effectiveness of the proposed method is verified.
文摘The acoustic vibration signal of tank is disassembled into the sum of intrinsic mode function （IMF） by multi-resolution empirical mode decomposition （EMD） method. The instantaneous frequency is obtained, and feature transformation matrix is figured out by class scatter matrix. Multi- dimensional scale energy vector is mapped into low-dimensional eigenvector, and classification extraction is realized. This method sufficiently separates of different sound target features. The test result indicates that it is effective.
基金Supported by the National Natural Science Foundation of China(No.61075022)
文摘The paper proposes a new method of multi-band signal reconstruction based on Orthogonal Matching Pursuit(OMP),which aims to develop a robust Ecological Sounds Recognition(ESR)system.Firstly,the OMP is employed to sparsely decompose the original signal,thus the high correlation components are retained to reconstruct in the first stage.Then,according to the frequency distribution of both foreground sound and background noise,the signal can be compensated by the residual components in the second stage.Via the two-stage reconstruction,high non-stationary noises are effectively reduced,and the reconstruction precision of foreground sound is improved.At recognition stage,we employ deep belief networks to model the composite feature sets extracted from reconstructed signal.The experimental results show that the proposed approach achieved superior recognition performance on 60 classes of ecological sounds in different environments under different Signal-to-Noise Ratio(SNR),compared with the existing method.
文摘波达方向(Direction of Arrival,DOA)估计技术是语音增强和声学探测中的重要工具,对于语音机器人、视频会议、助听器和声呐等应用至关重要。最近出现的DOA估计新方法,例如图信号处理(Graph Signal Processing,GSP)方法,展现出优异的角度估计能力,有望提供更佳的声源DOA估计解决方案。然而,由于在多声源情况下GSP算法由邻接矩阵无法直接得到接收信号特征向量的正交补矩阵,导致多声源下GSP算法失效。为解决此问题,本文基于多源宽带语音信号的频域单源区域检测实现多声源分离,进而利用GSP和聚类算法实现宽带多声源的定位。具体而言,本文首先将GSP方法扩展到频域。其次,利用短时傅里叶变换将信号分为若干时频区域,筛选出单源主导的时频区域后,对其进行频域GSP单源定位。最后,对所有定位结果进行聚类,再通过加权平均获得最终的角度估计。我们利用LibriSpeech语音语料库构建声源信号进行多声源定位仿真,仿真结果证明,本文方法优于其他算法,较高信噪比下可将误差控制在3°以内。此外,我们使用圆形六阵元麦克风阵列,对实际录制的若干组录音数据应用所提算法进行定位测量,结果展示所提算法的定位误差更小,并在声源较为靠近时也能做到较好的分辨。
