An adaptive short-time frequency domain algorithm for blind separation of nonstationary convolved mixtures

2002 ◽  
Author(s):  
I. Kopriva ◽  
Z. Devcic ◽  
H. Szu
Keyword(s):  
Frequency Domain ◽  
Blind Separation ◽  
Time Frequency ◽  
Short Time

scholarly journals Application of the Acoustic Emission Method for Diagnosis of On-Load Tap Changer

2017 ◽  
Vol 42 (1) ◽  
pp. 29-35 ◽  
Author(s):  
Henryk Majchrzak ◽  
Andrzej Cichoń ◽  
Sebastian Borucki
Keyword(s):  
Acoustic Emission ◽  
Frequency Domain ◽  
Time Domain ◽  
Operating Conditions ◽  
Time Frequency ◽  
Measurement Results ◽  
Tap Changer ◽  
The Time Domain ◽  
Load Tap Changer ◽  
Short Time

Abstract This paper provides an example of the application of the acoustic emission (AE) method for the diagnosis of technical conditions of a three-phase on-load tap-changer (OLTC) GIII type. The measurements were performed for an amount of 10 items of OLTCs, installed in power transformers with a capacity of 250 MVA. The study was conducted in two different OLTC operating conditions during the tapping process: under load and free running conditions. The analysis of the measurement results was made in both time domain and time-frequency domain. The description of the AE signals generated by the OLTC in the time domain was performed using the analysis of waveforms and determined characteristic times. Within the time-frequency domain the measured signals were described by short-time Fourier transform spectrograms.

Blind Source Separation Based on Wavelet and Cross-Wavelet

2011 ◽  
Vol 328-330 ◽  
pp. 2064-2068 ◽  
Author(s):  
Jing Hui Wang ◽  
Yuan Chao Zhao
Keyword(s):  
Frequency Domain ◽  
Blind Source Separation ◽  
Time Scale ◽  
Source Separation ◽  
Blind Separation ◽  
Time Frequency ◽  
Non Stationary Signal ◽  
Cross Wavelet

In this paper, a novel blind separation approach using wavelet and cross-wavelet is presented. This method extends the separate technology from time-frequency domain to time-scale domain. The simulation showed that this method is suitable for dealing with non-stationary signal.

scholarly journals VoSE: An algorithm to Separate and Enhance Voices from Mixed Signals using Gradient Boosting

2020 ◽  
Author(s):  
Monica Gupta ◽  
R K Singh ◽  
Sachin Sinha
Keyword(s):  
Frequency Domain ◽  
Gradient Boosting ◽  
Ripple Effect ◽  
Indian Languages ◽  
Time Frequency ◽  
Voice Signal ◽  
Single Voice ◽  
Short Time ◽  
The Voice ◽  
Sound Files

Abstract Voice Separation and Enhancement (VoSE) algorithm aims at designing a predictive model to solve the problem of speech enhancement and separation from a mixed signal. VoSE can be used for any language, with or without a large Datasets. VoSE can be utilized by any voice response system like, Siri, Alexa, Google Assistant which as of now work on single voice command. The pre-processing of the voice is done using a Trimming Negative and Nonzero voice filter (TNNVF), designed by the authors. TNNVF is independent of language, it works on any voice signal. The segmentation of a voice is generally carried out on frequency domain or time domain. Independently they are known to have ripple or rising effect. To rule out the ripple effect, data is filtered in the time-frequency domain. Voice print of the entire sound files is created for the training and testing purpose. 80% of the voice prints are used to train the network and 20% are kept for testing. The training set contains over 48,000 voice prints. LightGBM with TensorFlow helps in generating unique voice prints in a short time. To enhance the retrieved voice signals, Enhance Predictive Voice(EPV) function is designed. The tests are conducted on English and Indian languages. The proposed work is compared with K-means, Decision Stump, Naïve Bayes, and LSTM.

scholarly journals Experimental Investigation of Unsteady Pressure Pulsation in New Type Dishwasher Pump with Special Double-Tongue Volute

Machines ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 288
Author(s):  
Yilei Zhu ◽  
Jinfeng Zhang ◽  
Yalin Li ◽  
Ping Huang ◽  
Hui Xu ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Frequency Domain ◽  
Pressure Pulsation ◽  
Rotation Speed ◽  
Monitoring Point ◽  
Time Frequency ◽  
Passive Rotation ◽  
Changing Trends ◽  
New Type ◽  
Short Time

A pressure pulsation experiment of a dishwasher pump with a passive rotation double-tongue volute was carried out and compared with the pressure pulsation of a single-tongue volute and a static double-tongue volute. The pressure pulsation of the three volute models was compared and analyzed from two aspects of different impeller speeds and different monitoring points. The frequency domain and time–frequency domain of pressure pulsation were obtained by a Fourier transform and short-time Fourier transform, respectively. The results showed that the average pressure of each monitoring point on the rotating double-tongue volute was the smallest and that on the single-tongue volute was the largest. When the impeller rotates at 3000 rpm, there were eight peaks and valleys in the pressure pulsation time domain curve of the single-tongue volute, while the double-tongue volute was twice that of the single-tongue volute. Under different impeller speeds, the changing trends of pressure pulsation time and frequency domain curves of static and rotating double-tongue volutes at monitoring point p1 are basically the same. Therefore, a volute reference scheme with passive rotation speed is proposed in this study, which can effectively improve the flow pattern and reduce pressure inside the dishwasher pump, and also provide a new idea for rotor–rotor interference to guide the innovation of dishwashers.

scholarly journals Exposing Speech Resampling Manipulation by Local Texture Analysis on Spectrogram Images

Electronics ◽  
2019 ◽  
Vol 9 (1) ◽  
pp. 23 ◽  
Author(s):  
Yujin Zhang ◽  
Shuxian Dai ◽  
Wanqing Song ◽  
Lijun Zhang ◽  
Dongmei Li
Keyword(s):  
Frequency Domain ◽  
Local Binary Pattern ◽  
Support Vector ◽  
Short Time Fourier Transform ◽  
Time Frequency ◽  
Local Texture ◽  
Detection Tool ◽  
Signal Changes ◽  
Original Speech ◽  
Short Time

Speech tampering may be aided by the resampling operation. It is significant for speech forensics to effectively detect the resampling; however, there are few studies on speech resampling detection. The purpose of this paper was therefore to provide a new training ideal to detect speech resampling. After resampling, the speech signal changes regularly in the time–frequency domain. In this paper, we theoretically analyzed the corresponding relationship between time domain and frequency domain of the resampled speech. Compared with the original speech, the bandwidth of resampled speech was stretched or compressed. First, the spectrogram was generated by short-time Fourier transform (STFT) from the speech. Then, the local binary pattern (LBP) operator was applied to model the statistical changes in the spectrogram and the LBP histogram was calculated as discriminative features. Finally, a support vector machine (SVM) was applied to classify the developed features to identify whether the speech had undergone the resampling operation. The experimental results show that the proposed method has superior detection performance in different resampling scenarios than some existing methods, and the proposed features are very robust against the commonly used compression post-processing operation. This highlights the promising potential of the proposed method as a speech resampling detection tool in practical forensics applications.

scholarly journals Underdetermined Blind Separation of Nondisjoint Sources in the Time-Frequency Domain

2007 ◽  
Vol 55 (3) ◽  
pp. 897-907 ◽  
Author(s):  
Abdeldjalil Aissa-El-Bey ◽  
Nguyen Linh-Trung ◽  
Karim Abed-Meraim ◽  
Adel Belouchrani ◽  
Yves Grenier
Keyword(s):  
Frequency Domain ◽  
Blind Separation ◽  
Time Frequency
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