A weighting function for improvement of spectral coherence based envelope spectrum

The low-frequency noise generated by the vibration of the compressor in the machinery room of refrigerators is considered as annoying sound. Active noise control is used to reduce this noise without any change in the design of the compressor in the machinery room. In configuring the control system, various signals are measured and analyzed to select the reference signal that best represents the compressor noise. As the space inside the machinery room is small, the size of a speaker is limited, and the magnitude of the controller transfer function is designed to be small at low frequencies, the controller uses FIR filter structure converged by the FxLMS algorithm using the pre-measured time signal. To manage the convergence speed for each frequency, the frequency-weighting function is applied to FxLMS algorithm. A series of measurements are performed to design the controller and to evaluate the control performance. After the control, the sound power transmitted by the refrigerator is reduced by 9 dB at the first dominant frequency (408 Hz in this case) and 3 dB at the second dominant frequency (459 Hz here), and the overall sound power decreases by 2.6 dB. Through this study, an active control system for the noise generated by refrigerator compressors is established.

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Separating Multiple Moving Sources by Microphone Array Signals for Wayside Acoustic Fault Diagnosis

Journal of Vibration and Acoustics ◽

10.1115/1.4043508 ◽

2019 ◽

Vol 141 (5) ◽

Author(s):

Wei Xiong ◽

Qingbo He ◽

Zhike Peng

Keyword(s):

Fault Diagnosis ◽

Time Domain ◽

Feature Matching ◽

Matching Pursuit ◽

Microphone Array ◽

Time Frequency ◽

Moving Sources ◽

The Time Domain ◽

Envelope Spectrum ◽

Threshold Setting

Wayside acoustic defective bearing detector (ADBD) system is a potential technique in ensuring the safety of traveling vehicles. However, Doppler distortion and multiple moving sources aliasing in the acquired acoustic signals decrease the accuracy of defective bearing fault diagnosis. Currently, the method of constructing time-frequency (TF) masks for source separation was limited by an empirical threshold setting. To overcome this limitation, this study proposed a dynamic Doppler multisource separation model and constructed a time domain-separating matrix (TDSM) to realize multiple moving sources separation in the time domain. The TDSM was designed with two steps of (1) constructing separating curves and time domain remapping matrix (TDRM) and (2) remapping each element of separating curves to its corresponding time according to the TDRM. Both TDSM and TDRM were driven by geometrical and motion parameters, which would be estimated by Doppler feature matching pursuit (DFMP) algorithm. After gaining the source components from the observed signals, correlation operation was carried out to estimate source signals. Moreover, fault diagnosis could be carried out by envelope spectrum analysis. Compared with the method of constructing TF masks, the proposed strategy could avoid setting thresholds empirically. Finally, the effectiveness of the proposed technique was validated by simulation and experimental cases. Results indicated the potential of this method for improving the performance of the ADBD system.

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Towards Robust Multiple Blind Source Localization Using Source Separation and Beamforming

Sensors ◽

10.3390/s21020532 ◽

2021 ◽

Vol 21 (2) ◽

pp. 532

Author(s):

Henglin Pu ◽

Chao Cai ◽

Menglan Hu ◽

Tianping Deng ◽

Rong Zheng ◽

...

Keyword(s):

Source Localization ◽

Sound Source ◽

Indoor Localization ◽

Weighting Function ◽

Signal To Noise Ratio ◽

Source Separation ◽

Sound Source Localization ◽

Angle Of Arrival ◽

Sound Sources ◽

Localization Algorithms

Multiple blind sound source localization is the key technology for a myriad of applications such as robotic navigation and indoor localization. However, existing solutions can only locate a few sound sources simultaneously due to the limitation imposed by the number of microphones in an array. To this end, this paper proposes a novel multiple blind sound source localization algorithms using Source seParation and BeamForming (SPBF). Our algorithm overcomes the limitations of existing solutions and can locate more blind sources than the number of microphones in an array. Specifically, we propose a novel microphone layout, enabling salient multiple source separation while still preserving their arrival time information. After then, we perform source localization via beamforming using each demixed source. Such a design allows minimizing mutual interference from different sound sources, thereby enabling finer AoA estimation. To further enhance localization performance, we design a new spectral weighting function that can enhance the signal-to-noise-ratio, allowing a relatively narrow beam and thus finer angle of arrival estimation. Simulation experiments under typical indoor situations demonstrate a maximum of only 4∘ even under up to 14 sources.

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Enhanced context-aware recommendation using topic modeling and particle swarm optimization

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-210331 ◽

2021 ◽

pp. 1-16

Author(s):

Ibtissem Gasmi ◽

Mohamed Walid Azizi ◽

Hassina Seridi-Bouchelaghem ◽

Nabiha Azizi ◽

Samir Brahim Belhaouari

Keyword(s):

Topic Modeling ◽

Latent Dirichlet Allocation ◽

State Of The Art ◽

Weighting Function ◽

Contextual Factors ◽

Pearson Correlation ◽

Correlation Coefficients ◽

Pso Algorithm ◽

Context Aware ◽

Proposed Model

Context-Aware Recommender System (CARS) suggests more relevant services by adapting them to the user’s specific context situation. Nevertheless, the use of many contextual factors can increase data sparsity while few context parameters fail to introduce the contextual effects in recommendations. Moreover, several CARSs are based on similarity algorithms, such as cosine and Pearson correlation coefficients. These methods are not very effective in the sparse datasets. This paper presents a context-aware model to integrate contextual factors into prediction process when there are insufficient co-rated items. The proposed algorithm uses Latent Dirichlet Allocation (LDA) to learn the latent interests of users from the textual descriptions of items. Then, it integrates both the explicit contextual factors and their degree of importance in the prediction process by introducing a weighting function. Indeed, the PSO algorithm is employed to learn and optimize weights of these features. The results on the Movielens 1 M dataset show that the proposed model can achieve an F-measure of 45.51% with precision as 68.64%. Furthermore, the enhancement in MAE and RMSE can respectively reach 41.63% and 39.69% compared with the state-of-the-art techniques.

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