De-Noising Audio Signals Using MATLAB Wavelets Toolbox

Villanueva-Luna, A. E.; Jaramillo-Nuñez, Alberto; Sanchez-Lucero, Daniel; Ortiz-Lima, Carlos M.; Aguilar-Soto, J. Gabriel; Flores-Gil, Aaron; May-Alarcon, Manuel

doi:10.5772/19851

Cited by 13 publications

(5 citation statements)

References 12 publications

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“…The theory and the detailed information of Discrete Wavelet Transform (DWT) are reported in the literature [10][11][12][13][14][15]20]. The overall flow of wavelet decomposition process is conducted in many steps, firstly the raw vibration signal (original signal) x [n] is decomposed into several levels of frequency bands, then the approximate and detail coefficients in each level with standard deviation values for approximate and detail coefficients are found, finally, a comparison of standard deviation values for normal and faulty bearings is carried out [7].…”

Section: Methods and Experimentsmentioning

confidence: 99%

“…Some techniques such as dual-tree complex wavelet transform (DTCWT), permutation entropy (PE) using the fuzzy means clustering (FCM) to identify fault types [8] and shaft-bearing model is also developed in order to investigate the rolling element vibrations [9]. Signal processing tools such as Gaussian function, convolution, Fast Fourier transform and comparison of methods of short-circuit fault diagnostic based on FFT and DWT approaches [10][11][12][13][14][15] were conducted to detect various bearing faults. Recent developments and applications of computational intelligence to condition monitoring and fault diagnosis are presented in ref.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Detection of Induction Motor Improper Bearing Lubrication by Discrete Wavelet Transforms (DWT) Decomposition

Belkacemi¹,

Saad²,

Ghemari³

et al. 2020

I2M

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The present paper deals with healthy and improper bearing lubrication signals analysis using Discrete Wavelet Transform (DWT) enhanced by MATLAB/ Wavelets toolbox analysis. The identification of bearing faults from the time or the frequency domain are difficult due to non stationary vibration signal. Therefore, for more accurate faults information and identification of bearing with lubrication defects (improper or absence of lubrication), the DWT is used. The validation of this procedure is conducted by an experimental setup designed for vibration signal acquisition and the complete analysis is finalized by MATLAB/ Wavelets toolbox. The recorded data used for the validation are the signals of healthy and un-lubricated bearing driven at a rotation speed of 1500 rpm by 0.78 KW three phase induction motor. From the obtained results it can be observed that, for medium speeds DWT decomposition enhanced by MATLAB Wavelets Toolbox procedure is efficient for improper lubricated bearing related faults diagnosis and detection.

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Section: Methods and Experimentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Detection of Induction Motor Improper Bearing Lubrication by Discrete Wavelet Transforms (DWT) Decomposition

Belkacemi¹,

Saad²,

Ghemari³

et al. 2020

I2M

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show abstract

“…At high scale, the wavelets are stretched. They correspond to slow changing features, that is, to a low frequency [18]. This paper implements coiflet wavelet for filtering.…”

Section: Impulsive Noisementioning

confidence: 99%

Speech Identification using GFCC, Additive White Gaussian Noise (AWGN) and Wavelet Filter

Arora¹,

Neeru²

2016

IJCA

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This paper deals with the identification of speakers identity from the given set of values of speech from the database. The major problem during the identification of speech is noisy environment which degrades the system performance during its mismatch. So one can say identification using speaker recognition is the vital issue in research. This paper tells about the various used techniques like GFCC i.e. Gamma tone Frequency Cepstral Coefficients as its speech detection algorithm and Gaussian Mixture Model (GMM) to estimate the Gaussian model parameters. This paper basically focuses on improvement of speech identification in noisy environment using Wavelet filter which are added to de-noise the speech signals. These techniques are applied on store value of databases in Attendance system application. and the features of the speech are then matched from the database. Experiment are done 15 speech values saying phrases "Present Mam","Present sir","Yes mam","Yes sir"with 4 types of utterance for each phase. This Experiment shows better results for stored database oriented system and gives 85% of the correct recognition rate i.e. CORR and 73% results are given when wavelet filter are not used .

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“…For periodic noise ICA seems to be an indispensible method. However, singular application of ICA is not a healthy effort hence wavelets were introduced for denoising [29,30,31]. EMD is intuitive and adaptive, and data driven thus computation of EMD does not require any previously known value of the signal.…”

Section: Figure 1 Original Speech Signal and Noise Signal To Be Addedmentioning

confidence: 99%

Novel Approach for Industrial Noise Cancellation in Speech Using ICA-EMD with PSO

S¹,

Natraj²,

Rekha³

2014

IJSIP

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Speech Signals have high range of variation in amplitudes and frequency. These acoustic signals with diverse properties are hard to recognize and filter if mixed with noise. To separate noise from original signal, the artifact peaks are separated from original signal and discarded. In this paper, the ICA method of signal denoising is used to differentiate the speech signal from periodic noise and Empirical Mode Decomposition method is proposed to generate the components of signal. The IMF(s) of signal is the non-linear descending order of frequency components that have been filtered for better SNR. Filtering with wiener filter has amended output but also results in loss of information. The selection of IMF(s) for signal regeneration when optimized using objective function of PSO, the information of original signal was dramatically preserved with suppressed noise. The system is tested on 4 example signals and proposed technique illustrates lower mean square error and higher SNR compared to wiener and ICA.

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De-Noising Audio Signals Using MATLAB Wavelets Toolbox

Cited by 13 publications

References 12 publications

Detection of Induction Motor Improper Bearing Lubrication by Discrete Wavelet Transforms (DWT) Decomposition

Detection of Induction Motor Improper Bearing Lubrication by Discrete Wavelet Transforms (DWT) Decomposition

Speech Identification using GFCC, Additive White Gaussian Noise (AWGN) and Wavelet Filter

Novel Approach for Industrial Noise Cancellation in Speech Using ICA-EMD with PSO

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