Auxiliary function-based algorithm for blind extraction of a moving speaker

In this paper, we propose a novel algorithm for blind source extraction (BSE) of a moving acoustic source recorded by multiple microphones. The algorithm is based on independent vector extraction (IVE) where the contrast function is optimized using the auxiliary function-based technique and where the recently proposed constant separating vector (CSV) mixing model is assumed. CSV allows for movements of the extracted source within the analyzed batch of recordings. We provide a practical explanation of how the CSV model works when extracting a moving acoustic source. Then, the proposed algorithm is experimentally verified on the task of blind extraction of a moving speaker. The algorithm is compared with state-of-the-art blind methods and with an adaptive BSE algorithm which processes data in a sequential manner. The results confirm that the proposed algorithm can extract the moving speaker better than the BSE methods based on the conventional mixing model and that it achieves improved extraction accuracy than the adaptive method.

Blind source extraction based on EMD and temporal correlation for rolling element bearing fault diagnosis

Purpose Fault diagnosis methods based on blind source separation (BSS) for rolling element bearings are necessary tools to prevent any unexpected accidents. In the field application, the actual signal acquisition is usually hindered by certain restrictions, such as the limited number of signal channels. The purpose of this study is to fulfill the weakness of the existed BSS method. Design/methodology/approach To deal with this problem, this paper proposes a blind source extraction (BSE) method for bearing fault diagnosis based on empirical mode decomposition (EMD) and temporal correlation. First, a single-channel undetermined BSS problem is transformed into a determined BSS problem using the EMD algorithm. Then, the desired fault signal is extracted from selected intrinsic mode functions with a multi-shift correlation method. Findings Experimental results prove the extracted fault signal can be easily identified through the envelope spectrum. The application of the proposed method is validated using simulated signals and rolling element bearing signals of the train axle. Originality/value This paper proposes an underdetermined BSE method based on the EMD and the temporal correlation method for rolling element bearings. A simulated signal and two bearing fault signal from the train rolling element bearings show that the proposed method can well extract the bearing fault signal. Note that the proposed method can extract the periodic fault signal for bearing fault diagnosis. Thus, it should be helpful in the diagnosis of other rotating machinery, such as gears or blades.

Blind source extraction of rolling bearings' multi-type faults based on self-learned sparse atomics

The feature of rolling element bearings' multi-type faults is very hard to extract using common feature extraction method such as envelope demodulation, and the main reason is that there exists mutual coupling effect when multi-type faults arise in rolling element bearing synchronously. Blind source extraction originating from blind source separation is an effective method for feature extraction of rolling bearings' multi-type faults. However, the extraction result would not be ideal if blind source extraction is used directly due to the above stated mutual coupling effect. Sparse representation is a relative new signal processing method, which could capture the latent fault feature components buried in the vibration signal. So, blind source extraction of rolling element bearings' multi-type faults based on sparse representation is proposed in the paper. Firstly, the self-learned sparse atomics originating from sparse representation is applied to the multi-type faults vibration signals directly and several learned atomics are obtained. Then, the multi-type faults vibration signals are reconstructed based on the obtained learned atomics and sparse multi-type faults vibration signals are obtained. Thirdly, the blind source extraction method is applied to the reconstructed sparse vibration signals. Lastly, envelope demodulation is applied to the blind source extraction results respectively and satisfactory fault feature extraction results are obtained. The feasibility and effectiveness of the proposed method are verified through simulation and experiment.

Separation Enhancement of Power Line Noise from Human ECG Signal Based on Stone Technique

The cardiac signal is very important for the heart disease diagnosis and evaluation. The noise cancelation represent one of the most preprocessing step in ECG signal processing, usually, this signal is very sensitive and varies with time. The ECG signal is mostly contaminated by different signals like Power line noise signal, Baseline signal and muscle signal. The power line interference signal is the most effected signal on the ECG during data recording. Several papers try to cancel the noise based on different ways and to extract the useful information. In this paper a novel approach based on stone blind source extraction is used to extract the pure ECG signal from raw ECG, the main advantage of the proposed approach compared with the classical technique is to separate all the useful information without filtering or cancelling the suitable data from the recording signal. Real ECG data from MIT-BIH databases is taken and the MATLAB program is used to evaluate the experimental results. The performance of the proposed approach is measured based on SNR and MSE. The main contribution of this paper is to use Stone blind source separation technique as a first time in ECG signal analysis and prove that this method is the best technique compared with conventional ways. The obtained result proves Stone BSS technique is very efficient to remove the power line noise.