Статті в журналах: "Noise subtraction"

1

Ma, Ying, Xiao Hua Zhang, and Bing Lei Xing. "A Speech Enhancement Algorithm Based on the “Music Noise” Analysis." Applied Mechanics and Materials 543-547 (March 2014): 2784–87. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.2784.

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Interference is inevitable process of voice communication will be from the surrounding environment and transmission medium noise, communication equipment, electronic noise, and other speakers. These interference makes the voice receiver received for noisy speech signal with noise pollution. According to the traditional spectral subtraction residual musical noise is too strong, the weighted processing is reduced and the power spectrum correction, spectral subtraction method was adopted to improve the traditional. According to the analysis of real speech data collection simulation, improved spectral subtraction can effectively reduce the musical noise, can satisfy the requirement of speech enhancement.

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2

Lin, Tingting, Xiaokang Yao, Sijia Yu, and Yang Zhang. "Electromagnetic Noise Suppression of Magnetic Resonance Sounding Combined with Data Acquisition and Multi-Frame Spectral Subtraction in the Frequency Domain." Electronics 9, no. 8 (August 5, 2020): 1254. http://dx.doi.org/10.3390/electronics9081254.

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As an advanced groundwater detection method, magnetic resonance sounding (MRS) has received more and more attention. However, the biggest challenge is that MRS measurements always suffer with a bad signal-to-noise ratio (SNR). Aiming at the problem of noise interference in MRS measurement, we propose a novel noise-suppression approach based on the combination of data acquisition and multi-frame spectral subtraction (DA-MFSS). The pure ambient noise from the measurement area is first collected by the receiving coil, and then the noisy MRS signal is recorded following the pulse moments transmitting. The procedure of the pure noise and the noisy MRS signal acquisition will be repeated several times. Then, the pure noise and the noisy signal are averaged to preliminarily suppress the noise. Secondly, the averaged pure noise and the noisy signal are divided into multiple frames. The framed signal is transformed into the frequency domain and the spectral subtraction method is applied to further suppress the electromagnetic noise embedded in the noisy MRS signal. Finally, the de-noised signal is recovered by the overlap-add method and inverse Fourier transformation. The approach was examined by numerical simulation and field measurements. After applying the proposed approach, the SNR of the MRS data was improved by 16.89 dB and both the random noise and the harmonic noise were well suppressed.

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3

Lu, Dong Yu, Guan Yu Tian, Xiao Shan Lu, Xin Ma, and Lan Tian. "Improved Speech Enhancement Algorithm Based on Bark Bands Noise-Estimation for Non-Stationary Environment." Applied Mechanics and Materials 385-386 (August 2013): 1398–401. http://dx.doi.org/10.4028/www.scientific.net/amm.385-386.1398.

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The conventional spectrum subtraction algorithm cannot effectively suppress the noise under highly non-stationary environment and results in the remaining music noise is often heard in the enhanced speech. In order to improve the speech enhancement performance, a novel denoising algorithm is proposed, which is based on speech endpoint detection using spectrum variance and the dynamic spectrum subtraction in Bark bands. According to human auditory characteristics, the Bark bands spectrums of the noisy speech signal are firstly calculated, and the noise power spectrum of each Bark band is then tracked and estimated by the improved minima controlled recursive averaging method. This noise estimation is adjustable frame by frame and more accurate for non-stationary environment. The experiment results showed that the proposed method can suppress the noise more efficiently than the conventional spectrum subtraction and the remaining music noise is almost eliminated.

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4

Muzammel, Chowdhury Shahriar, Mahmudul Hasan, Khalil Ahammad, and Mousumi Hasan Mukti. "Noise Reduction from Speech Signals using Modified Spectral Subtraction Technique." European Journal of Engineering Research and Science 3, no. 7 (July 31, 2018): 78. http://dx.doi.org/10.24018/ejers.2018.3.7.838.

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Varieties of environmental sources of noise and distortion can degrade the quality of the speech signal in a communication system. This research work explores the effects of these interfering sounds on speech applications and introduces a technique for reducing their influence and enhancing the acceptability and intelligibility of the speech signal. In this work, a noise reduction system using single microphone method in time domain to improve SNR of noise contaminated speech is proposed. Traditional Spectral Subtraction method has been reviewed very well and the relationship with wiener filter is also illustrated. The Spectral Subtraction method has been generalized and the focus is put on reducing noise from speech in single channel signals. Voice Activity Detector (VAD) is ignored in this proposed system, because a-priori information about the noise is assumed. The research has been conducted using Gaussian White Noise and Color Noise. The experimental result shows a remarkable improvement in SNR for the generalized version and it is noticed that the result is very much satisfactory when white noises are added but the addition of color noise produces a comparatively poor improvement report. The system has been tested with eight different datasets and on an average, 65.27% improvement in SNR (Signal to Noise Ratio) for White Noise using Generalized Spectral Subtraction Method is achieved comparing with Traditional Spectral Subtraction Method. The average improvement in SNR for Color Noise recorded is 53.31%. The Generalized Spectral Subtraction method is shown to improve the speech quality and to improve SNR as well.

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5

Muzammel, Chowdhury Shahriar, Mahmudul Hasan, Khalil Ahammad, and Mousumi Hasan Mukti. "Noise Reduction from Speech Signals using Modified Spectral Subtraction Technique." European Journal of Engineering and Technology Research 3, no. 7 (July 31, 2018): 78–80. http://dx.doi.org/10.24018/ejeng.2018.3.7.838.

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Varieties of environmental sources of noise and distortion can degrade the quality of the speech signal in a communication system. This research work explores the effects of these interfering sounds on speech applications and introduces a technique for reducing their influence and enhancing the acceptability and intelligibility of the speech signal. In this work, a noise reduction system using single microphone method in time domain to improve SNR of noise contaminated speech is proposed. Traditional Spectral Subtraction method has been reviewed very well and the relationship with wiener filter is also illustrated. The Spectral Subtraction method has been generalized and the focus is put on reducing noise from speech in single channel signals. Voice Activity Detector (VAD) is ignored in this proposed system, because a-priori information about the noise is assumed. The research has been conducted using Gaussian White Noise and Color Noise. The experimental result shows a remarkable improvement in SNR for the generalized version and it is noticed that the result is very much satisfactory when white noises are added but the addition of color noise produces a comparatively poor improvement report. The system has been tested with eight different datasets and on an average, 65.27% improvement in SNR (Signal to Noise Ratio) for White Noise using Generalized Spectral Subtraction Method is achieved comparing with Traditional Spectral Subtraction Method. The average improvement in SNR for Color Noise recorded is 53.31%. The Generalized Spectral Subtraction method is shown to improve the speech quality and to improve SNR as well.

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6

Kawamura, Arata, Weerawut Thanhikam, and Youji Iiguni. "Single Channel Speech Enhancement Techniques in Spectral Domain." ISRN Mechanical Engineering 2012 (July 22, 2012): 1–9. http://dx.doi.org/10.5402/2012/919234.

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This paper presents single-channel speech enhancement techniques in spectral domain. One of the most famous single channel speech enhancement techniques is the spectral subtraction method proposed by S.F. Boll in 1979. In this method, an estimated speech spectrum is obtained by simply subtracting a preestimated noise spectrum from an observed one. Hence, the spectral subtraction method is not concerned with speech spectral properties. It is well known that the spectral subtraction method produces an annoying artificial noise in the extracted speech signal. On the other hand, recent successful speech enhancement methods positively utilize the speech property and achieve an efficient speech enhancement capability. This paper presents a historical review about some speech estimation techniques and explicitly states the difference between their theoretical back-ground. Moreover, to evaluate their speech enhancement capabilities, we perform computer simulations. The results show that an adaptive speech enhancement method based on MAP estimation gives the best noise reduction capability in comparison to other speech enhancement methods presented in this paper.

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7

Zhang, Shenghuan, and Ye Cheng. "Masking and noise reduction processing of music signals in reverberant music." Journal of Intelligent Systems 31, no. 1 (January 1, 2022): 420–27. http://dx.doi.org/10.1515/jisys-2022-0024.

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Abstract Noise will be inevitably mixed with music signals in the recording process. To improve the quality of music signals, it is necessary to reduce noise as much as possible. This article briefly introduces noise, the masking effect, and the spectral subtraction method for reducing noise in reverberant music. The spectral subtraction method was improved by the human ear masking effect to enhance its noise reduction performance. Simulation experiments were carried out on the traditional and improved spectral subtraction methods. The results showed that the improved spectral subtraction method could reduce the noise in reverberant music more effectively; under an objective evaluation criterion, the signal-to-noise ratio, the de-reverberated music signal processed by the improved spectral subtraction method had a higher signal-to-noise ratio; under a subjective evaluation criterion, mean opinion score (MOS), the de-reverberated music signal processed by the improved spectral subtraction method also had a better evaluation.

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8

Cella, G. "Thermal noise correlations and subtraction." Physics Letters A 382, no. 33 (August 2018): 2269–74. http://dx.doi.org/10.1016/j.physleta.2017.06.026.

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9

Douarche, F., L. Buisson, S. Ciliberto, and A. Petrosyan. "A simple noise subtraction technique." Review of Scientific Instruments 75, no. 12 (December 2004): 5084–89. http://dx.doi.org/10.1063/1.1821625.

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10

Alimi, Isiaka Ajewale. "Performance Improvement of Digital Hearing Aid Systems." Journal of Communications Technology, Electronics and Computer Science 1 (October 22, 2015): 27. http://dx.doi.org/10.22385/jctecs.v1i0.15.

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Digital hearing aids addresses the issues of noise and speech intelligibility that is associated with the analogue types. One of the main functions of the digital signal processor (DSP) of digital hearing aid systems is noise reduction which can be achieved by speech enhancement algorithms which in turn improve system performance and flexibility. However, studies have shown that the quality of experience (QoE) with some of the current hearing aids is not up to expectation in a noisy environment due to interfering sound, background noise and reverberation. It is also suggested that noise reduction features of the DSP can be further improved accordingly. Recently, we proposed an adaptive spectral subtraction algorithm to enhance the performance of communication systems and address the issue of associated musical noise generated by the conventional spectral subtraction algorithm. The effectiveness of the algorithm has been confirmed by different objective and subjective evaluations. In this study, an adaptive spectral subtraction algorithm is implemented using the noise-estimation algorithm for highly non-stationary noisy environments instead of the voice activity detection (VAD) employed in our previous work due to its effectiveness. Also, signal to residual spectrum ratio (SR) is implemented in order to control the amplification distortion for speech intelligibility improvement. The results show that the proposed scheme gives comparatively better performance and can be easily employed in digital hearing aid system for improving speech quality and intelligibility.

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11

Soon, Ing Yann, Soo Ngee Koh, and Zhong-Xuan Yuan. "Selective magnitude subtraction for noise reduction." Journal of Communications and Networks 2, no. 3 (September 2000): 226–29. http://dx.doi.org/10.1109/jcn.2000.6596712.

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12

Zhao, Xiu Ying, Hong Yu Wang, Shou Yu Tong, De You Fu, and Hai Shen Zhou. "Nonlinear Spectral Subtraction Method for Elimination of Aircraft Engine’s Noise from Degraded Speech Signals." Applied Mechanics and Materials 130-134 (October 2011): 1327–30. http://dx.doi.org/10.4028/www.scientific.net/amm.130-134.1327.

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The spectral subtraction is one of the best methods for elimination of approximate cyclical engine’s noise from degraded speech signal. Here we turn to research about the nonlinear spectral subtraction method and its improved model. After studying the nonlinear method we turn to this method that whether it can improve the quality of enhanced speech signal, propose the short-time spectral subtraction, which needs two inputs. The main input is containing the voice that is corrupted by noise. The other input (noise reference input) contains noise related in some way to that of the main input (background noise). Then use the main input’s frequency spectrum subtract the other input’s frequency spectrum. The results of experiment have proved it’s effective.

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13

Seto, Keisuke, Takayoshi Kobayashi, and Eiji Tokunaga. "Algorithm of auto-balancing noise-canceling based on noise correlation for high-speed balancing, high-dynamic range, and robustness against DC-offset drift." Review of Scientific Instruments 93, no. 4 (April 1, 2022): 043105. http://dx.doi.org/10.1063/5.0078967.

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The influence of the light source noise can be reduced by subtracting the signal of the light source noise (reference signal) from that of the probe light (probe signal). Here, it is essential that the intensities of the signals are equated. To equate the intensities, an auto-balancing method is widely employed, where the gain of the probe signal is feedback-controlled, regarding the DC component in the subtraction as an error signal. However, DC-offset drift causes a deviation from the optimal intensity balance. Additionally, the DC component is often several orders of magnitude larger than the sample signal, which requires a high-dynamic range in the circuitry. Furthermore, if the feedback control is too fast, it cancels out the sample signal. In this study, we formulate a noise correlation auto-balancing method, where the correlation of the reference signal and residual noise in the subtraction is employed as the error signal. With this scheme, all the above problems are avoided. The feasibility of the algorithm was demonstrated by a prototype circuitry and signals emulating the probe and reference signals. It did not suffer from the DC-offset drift, while a 44-dB canceling rate with auto-balancing of a 1.3-MHz cutoff frequency was demonstrated. We foresee, such as in pump/probe measurements, that this scheme improves the robustness, dynamic range, and response time required to follow changes in transmittance and the measurement position of the sample while employing a light source that is advantageous in wavelength selectivity, coherence, and cost but is noisy.

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14

Zhang, Yanqi, Adam S. Hines, Guillermo Valdes, and Felipe Guzman. "Investigation and Mitigation of Noise Contributions in a Compact Heterodyne Interferometer." Sensors 21, no. 17 (August 28, 2021): 5788. http://dx.doi.org/10.3390/s21175788.

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We present a noise estimation and subtraction algorithm capable of increasing the sensitivity of heterodyne laser interferometers by one order of magnitude. The heterodyne interferometer is specially designed for dynamic measurements of a test mass in the application of sub-Hz inertial sensing. A noise floor of 3.31×10−11m/Hz at 100 mHz is achieved after applying our noise subtraction algorithm to a benchtop prototype interferometer that showed a noise level of 2.76×10−10m/Hz at 100 mHz when tested in vacuum at levels of 3×10−5 Torr. Based on the previous results, we investigated noise estimation and subtraction techniques of non-linear optical pathlength noise, laser frequency noise, and temperature fluctuations in heterodyne laser interferometers. For each noise source, we identified its contribution and removed it from the measurement by linear fitting or a spectral analysis algorithm. The noise correction algorithm we present in this article can be generally applied to heterodyne laser interferometers.

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15

Li, Hui Ya, Jian Ying Shi, and Jin Xi Men. "Blind Source Separation of Noisy Mixed Speech Signals." Applied Mechanics and Materials 475-476 (December 2013): 291–95. http://dx.doi.org/10.4028/www.scientific.net/amm.475-476.291.

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In this paper, a new method for blind source separation of the noisy mixed speech signals is introduced. Firstly, the adaptive spectral subtraction is adopted to eliminate noise of noisy mixed speech signals. Secondly, the FASTICA algorithm is used to separate denoised mixed speech signals .Finally, wavelet transform is applied to remove the residual noise, and then the estimation of each speech source signal can be got.

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16

Neelamani, Ramesh (Neelsh), Anatoly Baumstein, and Warren S. Ross. "Adaptive subtraction using complex-valued curvelet transforms." GEOPHYSICS 75, no. 4 (July 2010): V51—V60. http://dx.doi.org/10.1190/1.3453425.

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We propose a complex-valued curvelet transform-based (CCT-based) algorithm that adaptively subtracts from seismic data those noises for which an approximate template is available. The CCT decomposes a geophysical data set in terms of small reflection pieces, with each piece having a different characteristic frequency, location, and dip. One can precisely change the amplitude and shift the location of each seismic reflection piece in a template by controlling the amplitude and phase of the template's CCT coefficients. Based on these insights, our approach uses the phase and amplitude of the data's and template's CCT coefficients to correct misalignment and amplitude errors in the noise template, thereby matching the adapted template with the actual noise in the seismic data, reflection event-by-event. We also extend our approach to subtract noises that require several templates to be approximated. By itself, the method can only correct small misalignment errors ([Formula: see text] in [Formula: see text] data) in the template; it relies on conventional least-squares (LS) adaptation to correct large-scale misalignment errors, such as wavelet mismatches and bulk shifts. Synthetic and real-data results illustrate that the CCT-based approach improves upon the LS approach and a curvelet-based approach described by Herrmann and Verschuur.

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17

Gong, Shangfu, Fengzhi Xu, and Pengtao Jia. "Video Target Detection In Underground Mine Based On Background Difference And Edge Detection." MATEC Web of Conferences 232 (2018): 02023. http://dx.doi.org/10.1051/matecconf/201823202023.

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In view of the complex environment in the underground mine, the detection of moving targets in surveillance video often had the problems of low detection efficiency and the detection result was greatly affected by noise and shadows. A target extraction method based on fusion background subtraction, inter-frame difference and edge detection was proposed. Firstly, the method used the hybrid gaussian background modeling (GMM) to obtain the accurate background image of the dynamic environment, and the extracted moving targets by using background subtractiont. Then based on the three-frame differential and Canny edge detection, the foreground image and the moving object blob was obtained, which was combined with the background subtraction to eliminate noise and voids, and to avoid missed detection of the moving target. Finally, the shadows in the detection process were removed through pixel ratio and threshold screening, and morphological and connected domain processing were performed. Comparing the improved algorithm with the traditional algorithm, the test results show that the improved algorithm can effectively remove the noise and voids, suppress the shadow, avoid the missed detection target, and have a good detection effect.

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18

Ibrahim, Zuwairie, Ismail Ibrahim, Kamal Khalil, Sophan Wahyudi Nawawi, Muhammad Arif Abdul Rahim, Zulfakar Aspar, and Wan Khairunizam Wan Ahmad. "Noise Elimination for Image Subtraction in Printed Circuit Board Defect Detection Algorithm." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 10, no. 2 (August 5, 2013): 1317–29. http://dx.doi.org/10.24297/ijct.v10i2.7000.

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Image subtraction operation has been frequently used for automated visual inspection of printed circuit board (PCB) defects. Even though the image subtraction operation able to detect all defects occurred on PCB, some unwanted noise could be detected as well. Hence, before the image subtraction operation can be applied to real images of PCB, image registration operation should be done to align a defective PCB image against a template PCB image. This study shows how the image registration operation is incorporated with a thresholding algorithm to eliminate unwanted noise. The results show that all defects occurred on real images of PCB can be correctly detected without interfere by any unwanted noise.

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19

Timmermann, Johannes, Florian Ernst, and Delf Sachau. "Speech enhancement for helicopter headsets with an integrated ANC-system for FPGA-platforms." INTER-NOISE and NOISE-CON Congress and Conference Proceedings 265, no. 5 (February 1, 2023): 2720–30. http://dx.doi.org/10.3397/in_2022_0382.

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During flights, helicopter pilots are exposed to high noise levels caused by rotor, engine and wind. To protect the health of passengers and crew, noise-dampening headsets are used. Modern active noise control (ANC) headset can further reduce the noise exposure for humans in helicopters. Internal or external voice transmission in the helicopter must be adapted to the noisy environment and speech signals are therefore heavily amplified. To improve the quality of communication in helicopters speech and background noise in the transmitted audio signals should be separated. Subsequently the noise components of the signal are eliminated. One established method for this type of speech enhancement is spectral subtraction. In this study, audio files recorded with an artificial head during a helicopter flight are used to evaluate a speech enhancement system with additional ANC capabilities on a rapid prototyping platform. Since both spectral subtraction and the ANC algorithm are computationally intensive, an FPGA is used. The results show a significant enhancement in the quality of the speech signals, which thus lead to improved communication. Furthermore, the enhanced audio signals can be used for voice recognition algorithms.

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20

Chen, Min, and Chang-Myung Lee. "De-Noising Process in Room Impulse Response with Generalized Spectral Subtraction." Applied Sciences 11, no. 15 (July 26, 2021): 6858. http://dx.doi.org/10.3390/app11156858.

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The generalized spectral subtraction algorithm (GBSS), which has extraordinary ability in background noise reduction, is historically one of the first approaches used for speech enhancement and dereverberation. However, the algorithm has not been applied to de-noise the room impulse response (RIR) to extend the reverberation decay range. The application of the GBSS algorithm in this study is stated as an optimization problem, that is, subtracting the noise level from the RIR while maintaining the signal quality. The optimization process conducted in the measurements of the RIRs with artificial noise and natural ambient noise aims to determine the optimal sets of factors to achieve the best noise reduction results regarding the largest dynamic range improvement. The optimal factors are set variables determined by the estimated SNRs of the RIRs filtered in the octave band. The acoustic parameters, the reverberation time (RT), and early decay time (EDT), and the dynamic range improvement of the energy decay curve were used as control measures and evaluation criteria to ensure the reliability of the algorithm. The de-noising results were compared with noise compensation methods. With the achieved optimal factors, the GBSS contributes to a significant effect in terms of dynamic range improvement and decreases the estimation errors in the RTs caused by noise levels.

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21

Paranjape, Raman B., Tamer F. Rabie, and Rangaraj M. Rangayyan. "Image restoration by adaptive-neighborhood noise subtraction." Applied Optics 33, no. 14 (May 10, 1994): 2861. http://dx.doi.org/10.1364/ao.33.002861.

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22

Shaw, A., and B. M. Moores. "Noise transfer in screen-film subtraction radiography." Physics in Medicine and Biology 30, no. 3 (March 1, 1985): 229–38. http://dx.doi.org/10.1088/0031-9155/30/3/003.

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23

Wenzel, Ann, and Ib Sewerin. "Sources of noise in digital subtraction radiography." Oral Surgery, Oral Medicine, Oral Pathology 71, no. 4 (April 1991): 503–8. http://dx.doi.org/10.1016/0030-4220(91)90441-e.

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24

E, NASSER, AHAMED T., and MUSHIRA D. "MINIMIMZING STRUCTURE NOISE BY DIGITAL SUBTRACTION RADIOGRAPHY." International Conference on Aerospace Sciences and Aviation Technology 12, ASAT CONFERENCE (May 1, 2007): 1–9. http://dx.doi.org/10.21608/asat.2007.24131.

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25

Fitzgerald, Tracy S., and Beth A. Prieve. "COAE Thresholds." Journal of Speech, Language, and Hearing Research 40, no. 5 (October 1997): 1164–76. http://dx.doi.org/10.1044/jslhr.4005.1164.

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Although research has demonstrated that click-evoked otoacoustic emissions (COAEs) elicited by high-level stimuli are useful for identifying hearing loss, the ability of COAEs to predict behavioral thresholds has not been adequately tested. Results of studies comparing COAE thresholds and behavioral thresholds have been equivocal, perhaps due to the need for a more rigorous approach to COAE threshold estimation. The present study was designed to address several methodological concerns in COAE threshold testing, particularly the effects of two methods of stimulus presentation on COAE testing and threshold calculation. In an attempt to make COAE threshold estimation consistent across participants, COAE threshold calculations were based on mean noise floor levels across participants. COAE and noise floor levels were measured in 15 participants using both equal-amplitude clicks and a subtraction method. Broadband COAEs were analyzed into 1/3 octave bands, so that input/output functions could be examined and COAE thresholds could be calculated for each 1/3 octave band. Comparison of the two stimulus methods indicated several differences. Mean noise floor levels for the equal-amplitude method were approximately 6 dB lower than those measured for the subtraction method across frequency. In many cases COAEs evoked using the equal-amplitude method were higher in amplitude than those evoked using the subtraction method. COAE thresholds measured using the equal-amplitude click stimuli were significantly lower than those measured using the subtraction method. The significantly higher thresholds obtained using the subtraction method may be attributed in part to the reduction of COAE amplitude by the subtraction procedure, and not merely to the higher noise level. Slopes of the input/output functions were not significantly different between the two stimulus methods. These results suggest that the equal-amplitude method is preferable for COAE threshold testing because lower noise floor and larger amplitude COAEs may be obtained in the same test time.

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26

Gabrielson, Thomas B., B. J. Merchant, Dominique Rodrigues, and Chad M. Smith. "Measurement of infrasound sensor self-noise." Journal of the Acoustical Society of America 152, no. 4 (October 2022): A164. http://dx.doi.org/10.1121/10.0015896.

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Self-noise is a critical performance characteristic of sensors intended for weak-signal detection and localization; however, the lower the self-noise, the more challenging the measurement. A recent international sensor-characterization exercise coordinated by the Provisional Technical Secretariat of the Comprehensive Nuclear-Test-Ban Treaty Organization included self-noise measurement of several infrasound sensors from 0.01 Hz to 10 Hz. Of three common methods for self-noise assessment—isolation of the sensor from external excitation, subtraction of common (coherent) components among several sensors, or de-activation of the sense mechanism—the first two were used in this exercise and the results highlight important measurement issues. In the infrasonic frequency range, isolation from external excitation is challenging. A sealed, thick-walled chamber attenuates ambient noise but heating from dissipation of power in the chamber interior can induce convection with large, low-frequency pressure fluctuations in the chamber. Capping the inlet(s) of a sensor creates a small, closed volume with strong coupling between temperature and pressure fluctuations. Subtraction of coherent components shared by co-located sensors can be effective in reducing the influence of ambient excitation; however, the process may be frustrated by errors in subtraction of large, nearly equal components or by unexpected electrical coupling.

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27

Hu, Wenkai, Yichao Li, Yougang Wang, Fengquan Wu, Bo Zhang, Ming Zhu, Shifan Zuo, et al. "1/f noise analysis for FAST H i intensity mapping drift-scan experiment." Monthly Notices of the Royal Astronomical Society 508, no. 2 (October 2, 2021): 2897–909. http://dx.doi.org/10.1093/mnras/stab2728.

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ABSTRACT We investigate the 1/f noise of the Five-hundred-meter Aperture Spherical Telescope (FAST) receiver system using drift-scan data from an intensity mapping pilot survey. All the 19 beams have 1/f fluctuations with similar structures. Both the temporal and the 2D power spectrum densities are estimated. The correlations directly seen in the time series data at low frequency f are associated with the sky signal, perhaps due to a coupling between the foreground and the system response. We use singular value decomposition (SVD) to subtract the foreground. By removing the strongest components, the measured 1/f noise power can be reduced significantly. With 20 modes subtraction, the knee frequency of the 1/f noise in a 10-MHz band is reduced to $1.8 \times 10^{-3}\, {\rm Hz}$, well below the thermal noise over 500-s time-scale. The 2D power spectra show that the 1/f-type variations are restricted to a small region in the time-frequency space and the correlations in frequency can be suppressed with SVD modes subtraction. The residual 1/f noise after the SVD mode subtraction is uncorrelated in frequency, and a simple noise diode frequency-independent calibration of the receiver gain at 8-s interval does not affect the results. The 1/f noise can be important for H i intensity mapping, we estimate that the 1/f noise has a knee frequency (fk) ∼ 6 × 10−4 Hz, and time and frequency correlation spectral indices (α) ∼ 0.65, (β) ∼ 0.8 after the SVD subtraction of 30 modes. This can bias the H i power spectrum measurement by 10 per cent.

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28

Liu, Yu Hong, Dong Mei Zhou, and Zhan Jun Jiang. "Improved Spectral Subtraction Speech Enhancement Algorithm." Advanced Materials Research 760-762 (September 2013): 536–41. http://dx.doi.org/10.4028/www.scientific.net/amr.760-762.536.

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Анотація:

The paper addresses the problems of speech distortion and residual musical noise introduced by conventional spectral subtraction (SS) method for speech enhancement. In this paper, we propose a modified SS algorithm for speech enhancement based on the masking properties of human auditory system. The algorithm computes the parameters α and β dynamically according to the masking thresholds of the critical frequency segments for each speech frame. Simulation results show that the proposed algorithm is superior to the conventional SS method, not only in the improvement of output SNR, but in the reduction of the speech distortion and residual musical noise.

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29

Butler, Karl E., and R. Don Russell. "Subtraction of powerline harmonics from geophysical records." GEOPHYSICS 58, no. 6 (June 1993): 898–903. http://dx.doi.org/10.1190/1.1443474.

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Harmonic noise generated by power lines and electric railways has plagued geophysicists for decades. The noise occurs as electric and magnetic fields at the fundamental frequency of power transmission (typically 60 Hz in North America) and its harmonics. It may be recorded directly during time‐domain measurements of electric and magnetic felds, or indirectly, by geophone cables during the acquisition of seismic data.

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30

Li, Sheng, Jian Qi Wang, and Xi Jing Jing. "The Application of Nonlinear Spectral Subtraction Method on Millimeter Wave Conducted Speech Enhancement." Mathematical Problems in Engineering 2010 (2010): 1–12. http://dx.doi.org/10.1155/2010/371782.

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A nonlinear multiband spectral subtraction method is investigated in this study to reduce the colored electronic noise in millimeter wave (MMW) radar conducted speech. Because the over-subtraction factor of each Bark frequency band can be adaptively adjusted, the nonuniform effects of colored noise in the spectrum of the MMW radar speech can be taken into account in the enhancement process. Both the results of the time-frequency distribution analysis and perceptual evaluation test suggest that a better whole-frequency noise reduction effect is obtained, and the perceptually annoying musical noise was efficiently reduced, with little distortion to speech information as compared to the other standard speech enhancement algorithm.

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31

Marois, Christian, Carlos Correia, Jean-Pierre Véran, and Thayne Currie. "TLOCI: A Fully Loaded Speckle Killing Machine." Proceedings of the International Astronomical Union 8, S299 (June 2013): 48–49. http://dx.doi.org/10.1017/s1743921313007813.

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AbstractA new high-contrast imaging subtraction algorithm (TLOCI) is presented to maximize a planet signal-to-noise ratio. The technique uses an input spectrum and template PSFs to optimize the reference image coefficient determination to minimize the flux contamination via self-subtraction (thus maximizing its throughput wavelength per wavelength) of any planet that have a similar spectrum to the template spectrum in the image, while trying, at the same time, to maximize the speckle noise subtraction. The optimization is performed by a correlation matrix conditioning. Using laboratory Gemini Planet Imager data, the new algorithm is shown to be superior to the simple/double difference, polynomial fit and original LOCI algorithm.

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32

Larsen, Jakob Juul. "Model-based subtraction of spikes from surface nuclear magnetic resonance data." GEOPHYSICS 81, no. 4 (July 2016): WB1—WB8. http://dx.doi.org/10.1190/geo2015-0442.1.

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Surface nuclear magnetic resonance (surface NMR) has progressed significantly in recent years due to advances in instrumentation. In particular, the introduction of multichannel surface NMR instruments has been effective in improving the signal-to-noise ratio. The current methodology for noise reduction with multichannel instruments is, however, inadequate in complex noise environments, and there is a need for improved signal processing. We have evaluated a study of impulsive noise (spikes) in surface NMR data acquired with a Numis Poly instrument. We have determined how the spectral content can be used to classify spikes as originating from electric fences or sferics. Measurements of spikes from two electric fences were evaluated. The spikes were highly deterministic and can be modeled as impulsive excitations of the band-pass filter in the surface NMR receiver system. We investigated the feasibility of a model-based approach for subtraction of electric fence spikes. Model-based subtraction was shown to be possible, but it is limited by accidental fitting of the NMR signal in its current embodiment. We evaluated an example of a surface NMR data set in which subtraction of powerline harmonic noise and electric fence spike noise removed all coherence in the multichannel data, and the consequences for further noise reduction using multichannel methods were developed.

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33

Wang, Jing Fang. "Real-Time Speech Enhancement by Adaptive Spectral Subtraction Method." Applied Mechanics and Materials 556-562 (May 2014): 3774–78. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.3774.

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Non-stationary noise and strong background noise is difficult to extract the actual audio signal problem, an adaptive spectral reduction algorithm is proposed. A dynamic threshold algorithm is devised, iterative update mechanism and the specific implementation are contrived in the clean speech spectrum and noise spectrum estimating. Simulation experiments show that the algorithm can effectively de-noising filter, significantly improve the intelligibility of speech recognition system performance and read, and the method is robust in different noise environments and SNR. The algorithm complexity low, the computational cost is small, real-time, easy to implement, so that the effectiveness and real-time dual meet.

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34

Karam, Marc, Hasan F. Khazaal, Heshmat Aglan, and Cliston Cole. "Noise Removal in Speech Processing Using Spectral Subtraction." Journal of Signal and Information Processing 05, no. 02 (2014): 32–41. http://dx.doi.org/10.4236/jsip.2014.52006.

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35

Jaroslavceva, Jekaterina, Naoki Wake, Kazuhiro Sasabuchi, and Katsushi Ikeuchi. "Robot Ego‐Noise Suppression with Labanotation‐Template Subtraction." IEEJ Transactions on Electrical and Electronic Engineering 17, no. 3 (November 24, 2021): 407–15. http://dx.doi.org/10.1002/tee.23523.

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36

Moser, Andreas, Natacha F. Supper, Andreas Berger, David T. Margulies, and Eric E. Fullerton. "Noise subtraction in antiferromagnetically coupled magnetic recording media." Applied Physics Letters 86, no. 26 (June 27, 2005): 262501. http://dx.doi.org/10.1063/1.1949725.

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37

Moeller, R. P., та W. K. Burns. "106-μm all-fiber gyroscope with noise subtraction". Optics Letters 16, № 23 (1 грудня 1991): 1902. http://dx.doi.org/10.1364/ol.16.001902.

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38

Wright, G. A., K. W. Taylor, and J. A. Rowlands. "Noise in stenosis measurement using digital subtraction angiography." Medical Physics 12, no. 6 (November 1985): 705–12. http://dx.doi.org/10.1118/1.595652.

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39

Laroche, Gaétan, Jean Giroux, Alain Bordeleau, and Jean-Marc Garneau. "Investigation of Electrical and Optical Subtractions Using Two Input-Port and Two Output-Port FT-IR Spectrometers." Applied Spectroscopy 48, no. 3 (March 1994): 356–62. http://dx.doi.org/10.1366/0003702944028362.

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Two FT-IR spectrometers, each using two input ports and two output ports, have been used to minimize the effect of background noise and source fluctuation noise in infrared emission spectra of various sources. Blackbody sources, propane/air flames, and infrared flares have been studied, and spectra were recorded in the spectral region ranging from 1.7 to 5 μm. With the use of the two input-port and one output-port configurations, it was found that real-time optical subtraction could generate 80% background-noise-free spectra. When the spectrometers were operated in the one input-port and two output-port configurations, spectra that were free of source fluctuation noise were obtained with the use of real-time electrical subtraction of signals measured at both detectors. New signal processing techniques have thus been developed. An increase in the signal-to-fluctuation-noise ratio by a factor of seven has been observed in the interferograms, which in turn leads to a 2 × increase of the signal-to-noise ratio in the corresponding spectra. During this signal processing sequence requiring the use of two analog-to-digital converters (ADC) (one for each detector channel), intensity information was then lost, so that no calibrated spectra could be measured. However, with the use of a single-channel ADC, it was shown that, by a process of simply subtracting signals recorded from both detectors operated under similar amplifier gain, fluctuation noise could be partly removed and intensity information could also be retained. In conjunction with the high scanning velocity of the interferometer (60 scans/s at a 16-cm−1 resolution), this technique has proven to be very useful in measuring emission spectra of highly fluctuating infrared sources, such as flares.

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40

Dai, Peng, Ing Yann Soon, and Rui Tao. "Direct Recovery of Clean Speech Using a Hybrid Noise Suppression Algorithm for Robust Speech Recognition System." ISRN Signal Processing 2012 (December 26, 2012): 1–9. http://dx.doi.org/10.5402/2012/306305.

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A new log-power domain feature enhancement algorithm named NLPS is developed. It consists of two parts, direct solution of nonlinear system model and log-power subtraction. In contrast to other methods, the proposed algorithm does not need prior speech/noise statistical model. Instead, it works by direct solution of the nonlinear function derived from the speech recognition system. Separate steps are utilized to refine the accuracy of estimated cepstrum by log-power subtraction, which is the second part of the proposed algorithm. The proposed algorithm manages to solve the speech probability distribution function (PDF) discontinuity problem caused by traditional spectral subtraction series algorithms. The effectiveness of the proposed filter is extensively compared using the standard database, AURORA2. The results show that significant improvement can be achieved by incorporating the proposed algorithm. The proposed algorithm reaches a recognition rate of over 86% for noisy speech (average from SNR 0 dB to 20 dB), which means a 48% error reduction over the baseline Mel-frequency Cepstral Coefficient (MFCC) system.

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41

Czap, Laszlo, and Judit Pinter. "Noise Reduction in Voice Controlled Logistic Systems." Applied Mechanics and Materials 309 (February 2013): 260–67. http://dx.doi.org/10.4028/www.scientific.net/amm.309.260.

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The most comfortable way of human communication is speech, which is a possible channel of human-machine interface as well. Moreover, a voice driven system can be controlled with busy hands. Performance of a speech recognition system is highly decayed by presence of noise. Logistic systems typically work in noisy environment, so noise reduction is crucial in industrial speech processing systems. Traditional noise reduction procedures (e.g. Wiener and Kalman filters) are effective on stationary or Gaussian noise. The noise of a real workplace can be captured by an additional microphone: The voice microphone takes both speech and noise, while the noise mike takes only the noise signal. Because of the phase shift of the two signals, simple subtraction in time domain is ineffective. In this paper, we discuss a spectral representation modeling the noise and voice signals. A frequency spectrum based noise cancellation method is proposed and verified in real industrial environment.

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42

Rama Rao, Ch V., M. B. Rama Murthy, and K. Srinivasa Rao. "Noise Reduction Using mel-Scale Spectral Subtraction with Perceptually Defined Subtraction Parameters- A New Scheme." Signal & Image Processing : An International Journal 2, no. 1 (March 22, 2011): 135–49. http://dx.doi.org/10.5121/sipij.2011.2110.

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43

Chen, Min, and Chang-Myung Lee. "The Optimal Determination of the Truncation Time of Non-Exponential Sound Decays." Buildings 12, no. 5 (May 23, 2022): 697. http://dx.doi.org/10.3390/buildings12050697.

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The noise effects in the room impulse response (RIR) make the decay range of the integrated impulse response insufficient for reliable determination of reverberation time (RT). One of the preferred techniques to minimize noise effects is based on noise subtraction, RIR truncation, and correction for the truncation. The success of RT estimation through the method depends critically on the accurate estimation of the truncation time (TT). However, noise fluctuation and RIR irregularities can lead to discrepancies in the determined TT from the optimal value. The general goal of this paper is to improve RT estimates. An iterative procedure based on a non-exponential decay model consisting of a double-slope decay term and a noise term is presented to estimate the TT accurately. The model parameters are generated until the iterative procedure converges to a minimum difference between the energy decay curve (EDC) generated by the model and the Schroeder decay function. The decay rates of the EDCs with added pink noise levels are compared to those of the EDCs with low background noise. In addition, the detected TTs and the corresponding RTs are compared with the existing method and the noise compensation method (subtraction–truncation–correction method).

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44

Davis, Derek, Thomas Massinger, Andrew Lundgren, Jennifer C. Driggers, Alex L. Urban, and Laura Nuttall. "Improving the sensitivity of Advanced LIGO using noise subtraction." Classical and Quantum Gravity 36, no. 5 (February 13, 2019): 055011. http://dx.doi.org/10.1088/1361-6382/ab01c5.

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45

Hu, H. T., and C. Yu. "Adaptive noise spectral estimation for spectral subtraction speech enhancement." IET Signal Processing 1, no. 3 (September 1, 2007): 156–63. http://dx.doi.org/10.1049/iet-spr:20070008.

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46

Pardede, Hilman, Kalamullah Ramli, Yohan Suryanto, Nur Hayati, and Alfan Presekal. "Speech Enhancement for Secure Communication Using Coupled Spectral Subtraction and Wiener Filter." Electronics 8, no. 8 (August 14, 2019): 897. http://dx.doi.org/10.3390/electronics8080897.

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The encryption process for secure voice communication may degrade the speech quality when it is applied to the speech signals before encoding them through a conventional communication system such as GSM or radio trunking. This is because the encryption process usually includes a randomization of the speech signals, and hence, when the speech is decrypted, it may perceptibly be distorted, so satisfactory speech quality for communication is not achieved. To deal with this, we could apply a speech enhancement method to improve the quality of decrypted speech. However, many speech enhancement methods work by assuming noise is present all the time, so the voice activity detector (VAD) is applied to detect the non-speech period to update the noise estimate. Unfortunately, this assumption is not valid for the decrypted speech. Since the encryption process is applied only when speech is detected, distortions from the secure communication system are characteristically different. They exist when speech is present. Therefore, a noise estimator that is able to update noise even when speech is present is needed. However, most noise estimator techniques only adapt to slow changes of noise to avoid over-estimation of noise, making them unsuitable for this task. In this paper, we propose a speech enhancement technique to improve the quality of speech from secure communication. We use a combination of the Wiener filter and spectral subtraction for the noise estimator, so our method is better at tracking fast changes of noise without over-estimating them. Our experimental results on various communication channels indicate that our method is better than other popular noise estimators and speech enhancement methods.

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47

Bahr, Christopher J., and William C. Horne. "Subspace-based background subtraction applied to aeroacoustic wind tunnel testing." International Journal of Aeroacoustics 16, no. 4-5 (July 2017): 299–325. http://dx.doi.org/10.1177/1475472x17718885.

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A subspace-based form of background subtraction is presented and applied to aeroacoustic wind tunnel data. A variant of this method has seen use in other fields such as climatology and medical imaging. The technique is based on an eigenvalue decomposition of the background noise cross-spectral matrix. Simulated results indicate similar performance to conventional background subtraction when the subtracted spectra are weaker than the true contaminating background levels. Superior performance is observed when the subtracted spectra are stronger than the true contaminating background levels, and when background data do not match between measurements. Experimental results show limited success in recovering signal behavior for data in which conventional background subtraction fails. The results also demonstrate the subspace subtraction technique’s ability to maintain a physical coherence relationship in the modified cross-spectral matrix. Deconvolution results from microphone phased array data indicate that array integration methods are largely insensitive to subtraction type, and that background subtraction with appropriate background data is an effective alternative to diagonal removal.

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48

Haiter-Neto, F., and A. Wenzel. "Noise in subtraction images made from pairs of bitewing radiographs: a comparison between two subtraction programs." Dentomaxillofacial Radiology 34, no. 6 (November 2005): 357–61. http://dx.doi.org/10.1259/dmfr/15631269.

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49

Hao, Lei, Shuai Cao, Pengfei Zhou, Lei Chen, Yi Zhang, Kai Li, Dongdong Xie, and Yijun Geng. "Denoising Method Based on Spectral Subtraction in Time-Frequency Domain." Advances in Civil Engineering 2021 (July 8, 2021): 1–12. http://dx.doi.org/10.1155/2021/6621596.

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In view of the key problem that a large amount of noise in seismic data can easily induce false anomalies and interpretation errors in seismic exploration, the time-frequency spectrum subtraction (TF-SS) method is adopted into data processing to reduce random noise in seismic data. On this basis, the main frequency information of seismic data is calculated and used to optimize the filtering coefficients. According to the characteristics of effective signal duration between seismic data and voice data, the time-frequency spectrum selection method and filtering coefficient are modified. In addition, simulation tests were conducted by using different S/R, which indicates the effectiveness of the TF-SS in removing the random noise.

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50

Anderson, Richard G., and George A. McMechan. "Noise‐adaptive filtering of seismic shot records." GEOPHYSICS 53, no. 5 (May 1988): 638–49. http://dx.doi.org/10.1190/1.1442498.

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Ambient noise can obscure reflections on deep crustal seismic data. We use a spectral subtraction method to attenuate stationary noise. Our procedure, called noise‐adaptive filtering, is to Fourier transform the noise before the first arrivals, subtract the amplitude spectrum of the noise from the amplitude spectrum of the noisy data, and inverse Fourier transform. The phase spectrum is not corrected, but the method attenuates noise if the phase shift between the signal and noise is random. The algorithm can be implemented as a frequency filter, as a frequency‐wavenumber filter, or as two separate frequency and wavenumber filters. Noise‐adaptive filtering is often superior to conventional frequency or frequency‐wavenumber filtering because it adapts to spatial variations in the noise without parameter testing. Noise‐adaptive filters can achieve noise rejection ratios of up to 45 dB; their dynamic range is about 25 dB. These filters work best when the input signal‐to‐noise ratio is on the order of 0 dB and there are significant differences between the frequency‐wavenumber amplitude spectra of the signal and noise. Application of the method to field data can enhance events that are not visible in the input data.

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