Audio Denoising Algorithm Based on Adaptive Wavelet Soft-Threshold of Gain Factor and Teager Energy Operator

Li, Nanshan; Zhou, Mingquan

doi:10.1109/csse.2008.1523

Cited by 9 publications

(7 citation statements)

References 12 publications

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“…Kemudian [7], menerapkan Transformasi Wavelet sebagai preprocessing dalam sistem temu kembali pada lagu berbasis isi. Dalam melakukan thresholding, digunakan Adaptive Wavelet Soft Threshold pada audio dimana keakuratan hasil dari sistem temu kembali bergantung pada koefisien wavelet TEO dan faktor peningkat.…”

Section: Pendahuluanunclassified

Perbandingan Mother Wavelet dalam Proses Denoising pada Suara

Ramadhan¹,

Putra²

2014

Indonesian J. Comput. Cybern. Syst.

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AbstrakTransformasi Wavelet telah digunakan dalam proses denoising pada suara dengan tujuan untuk meningkatkan kualitas dari rekaman suara yang tercampur dengan derau. Jenis-jenis derau yang terlibat antara lain White Gaussian Noise (WGN), White Uniform Noise (WUN) dan Colored Noise. Dalam penelitian ini dilakukan perbandingan terhadap beberapa mother wavelet, diantaranya Daubechies, Coiflet dan Symlet, dalam proses denoising pada sinyal suara yang diberikan WGN, WUN dan Colored Noise. Metode thresholding yang digunakan dalam proses denoising adalah Soft Thresholding dan nilai threshold berupa Time-Adapted Threshold (TAT) yang diperoleh dengan melakukan estimasi energi untuk membangun sinyal melalui Teager Energy Operator (TEO). Pengujian untuk mendapatkan mother wavelet terbaik dilakukan menggunakan uji Kruskal-Wallis yang dilanjutkan dengan uji Mann-Whitney. Hasil yang diperoleh menunjukkan bahwa Db20, Db30, Db40 dan Coif5 merupakan mother wavelet yang baik untuk mereduksi WGN;Db40, Db20 dan Db30 merupakan mother wavelet yang baik untuk mereduksi WUN dan untuk mereduksi Colored Noise, dapat menggunakan beberapa mother wavelet dalam penelitian ini, kecuali Db30 dan Db40. Kata kunci—Mother wavelet, denoising, sinyal suara, TAT, Soft Thresholding. AbstractWavelet Transform was used in denoising process on speech to enhance the quality of speech that courrupted by noise. The kinds of involved noises are White Gaussian Noise (WGN), White Uniform Noise (WUN) and Colored Noise. In this research, the comparison of mother wavelet is performed among Daubechies, Coiflet and Symlet, in denoising process on speech which given by WGN, WUN and Colored Noise. The thresholding method is used in denoising process is Soft Thresholding and threshold value is Time Adapted Threshold (TAT) which obtained by estimating the power for building the signal through Teager Energy Operator (TEO). The testing for obtaining the best moher wavelet is using Kruskal-Wallis test and followed by Mann-Whitney test. The result shows that Db20, Db30, Db40 and Coif5 mother wavelets are better than others to reduce WGN; Db40, Db20 dan Db30mother waveletsare better then the other to reduce WUN and to reduce Colored Noise can use some mother wavelets in this research, except Db30 and Db40. Keywords—Mother wavelet, denoising, speech signal, TAT, Soft Thresholding

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Section: Pendahuluanunclassified

Perbandingan Mother Wavelet dalam Proses Denoising pada Suara

Ramadhan¹,

Putra²

2014

Indonesian J. Comput. Cybern. Syst.

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“…The acoustic signals captured from the engine consist of noise, which requires proper analysis before extracting the features of knock. There are many methods for denoising the signals; among them, wavelets are found to be effective [8,10,11]. Also, there are various mathematical tools for analyzing denoised signals, including the Fourier transform, Wavelet transform, Empirical Mode Decomposition (EMD), etc.…”

Section: Introductionmentioning

confidence: 99%

Engine block vibrations: An indicator of knocking in the SI engine

Joseph,

Thampi

2023

FME Transactions

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The factors influencing the onset of knocking have a significant impact on how well a SI engine performs. Hence, the efficacy in determining the onset and controlling of knock is a key factor in improving the SI engine's performance. This paper provides insight into the role of engine block vibrations in determining the occurrence of knock using Empirical Mode Decomposition and Short-Time Fourier Transform. To comprehend the behaviour of vibration amid normal combustion and knocking conditions, the engine block vibration signals are analyzed and compared with the incylinder pressure fluctuations. The features of knock are extracted from the engine block vibration signals using Empirical Mode Decomposition. The first Intrinsic Mode Function (IMF) thus obtained is used to generate the Hilbert spectrum for detecting the occurrence of knock. Similarly, ShortTime Fourier Transform is also performed on the first IMF to obtain the spectrogram. The findings demonstrate unequivocally that higher frequency variations are produced when knock occurs. These results also indicate that the combination of Empirical Mode Decomposition and Short-Time Fourier Transform can be used effectively for determining the occurrence of knock.

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“…The key advantage of wavelet denoising is to split the data into different frequency compo-nents and study the noise spikes in each frequency component at different resolution (Chang et al, 2000). The wavelet denoising is an emerging advance technique in signal processing that used in a various applications particularly image processing, data compression, impulsive events characterization, pattern recognition signal extraction and denoising (Yu et al, 2007;Li, Zhou, 2008). This type of technique will be useful for removing impact noise produced in the mooring, when acoustic recorders are incorporated in multisuite ocean moorings.…”

Section: Introductionmentioning

confidence: 99%

Acoustic Identification of Dolphin Whistle Types in Deep Waters of Arabian Sea Using Wavelet Threshold Denoising Approach

2023

Archives of Acoustics

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In situ time series measurements of ocean ambient noise, have been made in deep waters of the Arabian Sea, using an autonomous passive acoustic monitoring system deployed as part of the Ocean Moored buoy network in the Northern Indian Ocean (OMNI) buoy mooring operated by the National Institute of Ocean Technology (NIOT), in Chennai during November 2018 to November 2019. The analysis of ambient noise records during the spring (April-June) showed the presence of dolphin whistles but contaminated by unwanted impulsive shackle noise. The frequency contours of the dolphin whistles occur in narrow band in the range 4-16 kHz. However, the unwanted impulsive shackle noise occurs in broad band with the noise level higher by ∼20 dB over the dolphin signals, and it reduces the quality of dolphin whistles. A wavelet based threshold denoising technique followed by a subtraction method is implemented. Reduction of unwanted shackle noise is effectively done and different dolphin whistle types are identified. This wavelet denoising approach is demonstrated for extraction of dolphin whistles in the presence of challenging impulsive shackle noise. Furthermore, this study should be useful for identifying other cetacean species when the signal of interest is interrupted by unwanted mechanical noise.

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Audio Denoising Algorithm Based on Adaptive Wavelet Soft-Threshold of Gain Factor and Teager Energy Operator

Cited by 9 publications

References 12 publications

Perbandingan Mother Wavelet dalam Proses Denoising pada Suara

Perbandingan Mother Wavelet dalam Proses Denoising pada Suara

Engine block vibrations: An indicator of knocking in the SI engine

Acoustic Identification of Dolphin Whistle Types in Deep Waters of Arabian Sea Using Wavelet Threshold Denoising Approach

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