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Статті в журналах з теми "Two-microphone method"

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Автор: Grafiati

Опубліковано: 13 вересня 2021

Оновлено: 2 лютого 2022

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1

Schultz, Todd, Mark Sheplak, and Louis N. Cattafesta. "Uncertainty analysis of the two-microphone method." Journal of Sound and Vibration 304, no. 1-2 (July 2007): 91–109. http://dx.doi.org/10.1016/j.jsv.2007.02.015.

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2

Katz, Brian F. G. "Method to resolve microphone and sample location errors in the two-microphone duct measurement method." Journal of the Acoustical Society of America 108, no. 5 (November 2000): 2231–37. http://dx.doi.org/10.1121/1.1314318.

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3

Nakagawa, C. Renato C., Sven Nordholm, and Wei-Yong Yan. "Analysis of Two Microphone Method for Feedback Cancellation." IEEE Signal Processing Letters 22, no. 1 (January 2015): 35–39. http://dx.doi.org/10.1109/lsp.2014.2345571.

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4

Moritz, C., and J. S. Lamancusa. "A two‐microphone method for source strength measurement." Journal of the Acoustical Society of America 87, S1 (May 1990): S11. http://dx.doi.org/10.1121/1.2027877.

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5

Louis, B., G. Glass, B. Kresen, and J. Fredberg. "Airway Area by Acoustic Reflection: The Two-Microphone Method." Journal of Biomechanical Engineering 115, no. 3 (August 1, 1993): 278–85. http://dx.doi.org/10.1115/1.2895487.

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Анотація:

This report deals with noninvasive imaging of airway geometry based upon information contained in acoustic reflections measured at the mouth. Here we describe a new theoretical approach that enables development of a new miniaturized apparatus. Unlike the single-transducer systems used currently, this new strategy is based upon a two-transducer system that is a variant of that suggested originally by Shroeder (1967). We have developed, implemented, and tested computational algorithms necessary to reconstruct airway dimensions from acoustic reflection data using this two-transducer strategy.

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6

Mielnicka-Pate, A. L., and J. Adin Mann III. "Piston Radiation Investigations Using Two-Microphone Sound Intensity Method." Noise Control Engineering Journal 27, no. 2 (1986): 36. http://dx.doi.org/10.3397/1.2827670.

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7

Jiang, Bo, XiaoQin Liu, and Xing Wu. "Phase calibration method for microphone array based on multiple sound sources." INTER-NOISE and NOISE-CON Congress and Conference Proceedings 263, no. 6 (August 1, 2021): 659–69. http://dx.doi.org/10.3397/in-2021-1620.

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Анотація:

In the microphone array, the phase error of each microphone causes a deviation in sound source localization. At present, there is a lack of effective methods for phase error calibration of the entire microphone array. In order to solve this problem, a phase mismatch calculation method based on multiple sound sources is proposed. This method requires collecting data from multiple sound sources in turn, and constructing a nonlinear equation setthrough the signal delay and the geometric relationship between the microphones and the sound source positions. The phase mismatch of each microphone can be solved from the nonlinear equation set. Taking the single frequency signal as an example, the feasibility of the method is verified by experiments in a semi-anechoic chamber. The phase mismatches are compared with the calibration results of exchanging microphone. The difference of the phase error values measured by the two methods is small. The experiment also shows that the accuracy of sound source localization by beamforming is improved. The method is efficient for phase error calibration of arrays with a large number of microphones.

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8

Louis, B., G. M. Glass, and J. J. Fredberg. "Pulmonary airway area by the two-microphone acoustic reflection method." Journal of Applied Physiology 76, no. 5 (May 1, 1994): 2234–40. http://dx.doi.org/10.1152/jappl.1994.76.5.2234.

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Анотація:

We noninvasively assessed airway dimensions from acoustic reflection data measured at the mouth. We recently described a two-transducer system for measurement of the nasal airway. Here we apply this approach to the measurement of the upper airway and trachea. We describe the theoretical implications of breathing on this kind of measurement and propose a new procedure that, unlike single- and dual-transducer systems used currently, does not require the use of He-O2 for inference of geometry of subglottic airways.

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9

Suzuki, Seiichirou, and Takurou Hayashi. "Study on Howling in Active Noise Control. Two-Microphone Method." Transactions of the Japan Society of Mechanical Engineers Series C 59, no. 558 (1993): 515–20. http://dx.doi.org/10.1299/kikaic.59.515.

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10

Jekosch, Simon, and Ennes Sarradj. "An Extension of the Virtual Rotating Array Method Using Arbitrary Microphone Configurations for the Localization of Rotating Sound Sources." Acoustics 2, no. 2 (May 15, 2020): 330–42. http://dx.doi.org/10.3390/acoustics2020019.

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Анотація:

The characterization of rotating aeroacoustic sources using microphone array methods has been proven to be a useful tool. One technique to identify rotating sources is the virtual rotating array method. The method interpolates the pressure time data signals between the microphones in a stationary array to compensate the motion of the rotating sources. One major drawback of the method is the requirement of ring array geometries that are centred around the rotating axis. This contribution extends the virtual rotating array method to arbitrary microphone configurations. Two different ways to interpolate the time signals between the microphone locations are proposed. The first method constructs a mesh between the microphone positions using Delaunay-triangulation and interpolates over the mesh faces using piecewise linear functions. The second one is a meshless technique which is based on radial basis function interpolation. The methods are tested on synthetic array data from a benchmark test case as well as on experimental data obtained with a spiral array and a five-bladed fan.

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11

Liu, Haitao, Thia Kirubarajan, and Qian Xiao. "Arbitrary Microphone Array Optimization Method Based on TDOA for Specific Localization Scenarios." Sensors 19, no. 19 (October 7, 2019): 4326. http://dx.doi.org/10.3390/s19194326.

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Анотація:

Various microphone array geometries (e.g., linear, circular, square, cubic, spherical, etc.) have been used to improve the positioning accuracy of sound source localization. However, whether these array structures are optimal for various specific localization scenarios is still a subject of debate. This paper addresses a microphone array optimization method for sound source localization based on TDOA (time difference of arrival). The geometric structure of the microphone array is established in parametric form. A triangulation method with TDOA was used to build the spatial sound source location model, which consists of a group of nonlinear multivariate equations. Through reasonable transformation, the nonlinear multivariate equations can be converted to a group of linear equations that can be approximately solved by the weighted least square method. Then, an optimization model based on particle swarm optimization (PSO) algorithm was constructed to optimize the geometric parameters of the microphone array under different localization scenarios combined with the spatial sound source localization model. In the optimization model, a reasonable fitness evaluation function is established which can comprehensively consider the positioning accuracy and robustness of the microphone array. In order to verify the array optimization method, two specific localization scenarios and two array optimization strategies for each localization scenario were constructed. The optimal array structure parameters were obtained through numerical iteration simulation. The localization performance of the optimal array structures obtained by the method proposed in this paper was compared with the optimal structures proposed in the literature as well as with random array structures. The simulation results show that the optimized array structure gave better positioning accuracy and robustness under both specific localization scenarios. The optimization model proposed could solve the problem of array geometric structure design based on TDOA and could achieve the customization of microphone array structures under different specific localization scenarios.

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12

Gibiat, V., and F. Laloë. "Acoustical impedance measurements by the two‐microphone‐three‐calibration (TMTC) method." Journal of the Acoustical Society of America 88, no. 6 (December 1990): 2533–45. http://dx.doi.org/10.1121/1.399975.

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13

KITAMURA, Toshiya, Kenichi MITSUI, Daisuke ITO, and Shinji YAMADA. "Measurement of Low Frequency Sound Absorption Coeffcient with Two-Microphone Method." Proceedings of the Symposium on Environmental Engineering 2000.10 (2000): 92–94. http://dx.doi.org/10.1299/jsmeenv.2000.10.92.

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14

Khan, Muhammad Kamran Javed, Nizam Ud Din, Seho Bae, and Juneho Yi. "Interactive Removal of Microphone Object in Facial Images." Electronics 8, no. 10 (October 2, 2019): 1115. http://dx.doi.org/10.3390/electronics8101115.

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Анотація:

Removing a specific object from an image and replacing the hole left behind with visually plausible backgrounds is a very intriguing task. While recent deep learning based object removal methods have shown promising results on this task for some structured scenes, none of them have addressed the problem of object removal in facial images. The objective of this work is to remove microphone object in facial images and fill hole with correct facial semantics and fine details. To make our solution practically useful, we present an interactive method called MRGAN, where the user roughly provides the microphone region. For filling the hole, we employ a Generative Adversarial Network based image-to-image translation approach. We break the problem into two stages: inpainter and refiner. The inpainter estimates coarse prediction by roughly filling in the microphone region followed by the refiner which produces fine details under the microphone region. We unite perceptual loss, reconstruction loss and adversarial loss as joint loss function for generating a realistic face and similar structure to the ground truth. Because facial image pairs with or without microphone do not exist, we have trained our method on a synthetically generated microphone dataset from CelebA face images and evaluated on real world microphone images. Our extensive evaluation shows that MRGAN performs better than state-of-the-art image manipulation methods on real microphone images although we only train our method using the synthetic dataset created. Additionally, we provide ablation studies for the integrated loss function and for different network arrangements.

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15

Tran, Linh Thi Thuc, Sven Erik Nordholm, Henning Schepker, Hai Huyen Dam, and Simon Doclo. "Two-Microphone Hearing Aids Using Prediction Error Method for Adaptive Feedback Control." IEEE/ACM Transactions on Audio, Speech, and Language Processing 26, no. 5 (May 2018): 909–23. http://dx.doi.org/10.1109/taslp.2018.2798822.

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16

Chu, W. T. "Extension of the two‐microphone transfer function method for impedance tube measurements." Journal of the Acoustical Society of America 80, no. 1 (July 1986): 347–48. http://dx.doi.org/10.1121/1.394154.

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17

Post, John T. "Error in the measurement of acoustic impedance by the two‐microphone method." Journal of the Acoustical Society of America 114, no. 4 (October 2003): 2380. http://dx.doi.org/10.1121/1.4777594.

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18

Kundegorski, Mikolaj, Philip J. B. Jackson, and Bartosz Ziółko. "Two-Microphone Dereverberation for Automatic Speech Recognition of Polish." Archives of Acoustics 39, no. 3 (March 1, 2015): 411–20. http://dx.doi.org/10.2478/aoa-2014-0045.

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Анотація:

Abstract Reverberation is a common problem for many speech technologies, such as automatic speech recognition (ASR) systems. This paper investigates the novel combination of precedence, binaural and statistical independence cues for enhancing reverberant speech, prior to ASR, under these adverse acoustical conditions when two microphone signals are available. Results of the enhancement are evaluated in terms of relevant signal measures and accuracy for both English and Polish ASR tasks. These show inconsistencies between the signal and recognition measures, although in recognition the proposed method consistently outperforms all other combinations and the spectral-subtraction baseline.

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19

Zhang, Yu Hua, Li Min Jia, and Zhong Li. "Far Field Noise Suppression Method in McWiLL Intercom Based on Double Uni-Directional Microphone." Advanced Materials Research 267 (June 2011): 104–8. http://dx.doi.org/10.4028/www.scientific.net/amr.267.104.

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Анотація:

To satisfy McWiLL communication requirement in noisy environment, a far field noise suppression method based on double uni-direction microphone in McWiLL intercom was studied. The method arranges two uni-direction microphones rationally and uses analog noise cancelling processor to accomplish surrounding noise reduction in McWiLL intercom in noisy environment. To verify validity of the method, several contrast experiments using diagnostic rhyme test method were done. Experiments results show that the far field noise suppression method based on double uni-direction microphone is effective for surrounding noise reduction.

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20

Bentler, Ruth, Catherine Palmer, and Gustav H. Mueller. "Evaluation of a Second-Order Directional Microphone Hearing Aid: I. Speech Perception Outcomes." Journal of the American Academy of Audiology 17, no. 03 (March 2006): 179–89. http://dx.doi.org/10.3766/jaaa.17.3.4.

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Анотація:

This clinical trial was undertaken to evaluate the benefit obtained from hearing aids employing second-order adaptive directional microphone technology, used in conjunction with digital noise reduction. Data were collected for 49 subjects across two sites. New and experienced hearing aid users were fit bilaterally with behind-the-ear hearing aids using the National Acoustics Laboratory—Nonlinear version 1 (NAL-NL1) prescriptive method with manufacturer default settings for various parameters of signal processing (e.g., noise reduction, compression, etc.). Laboratory results indicated that (1) for the stationary noise environment, directional microphones provided better speech perception than omnidirectional microphones, regardless of the number of microphones; and (2) for the moving noise environment, the three-microphone option (whether in adaptive or fixed mode) and the two-microphone option in its adaptive mode resulted in better performance than the two-microphone fixed mode, or the omnidirectional modes.

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21

Jayamani, Elammaran, Pushparaj Ezhumalai, Sinin Hamdan, and M. Rezaur Rahman. "Investigation on Sound Absorption Coefficients of Betel Nut Fiber Reinforced Polymer Matrix Composites." Applied Mechanics and Materials 465-466 (December 2013): 901–5. http://dx.doi.org/10.4028/www.scientific.net/amm.465-466.901.

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This research investigates the sound absorption coefficients of betel nut fibers (Areca Fibers) reinforced with thermoplastic (Polypropylene) and thermoset (Unsaturated polyester) matrix composites with different fiber loadings and frequencies. In this research we used 5%, 10%, 15%, and 20% of betel nut fibers in the polymer matrix and the test frequencies are from 400 Hz to 1800 Hz. There are two standardized methods used for measuring the normal incidence sound absorption coefficient of composites namely, standing wave method (ISO 10534-1) and two fixed microphone method (ISO 10534-2). From this research, betel nut fibers reinforced with polymer matrix composites have good sound absorption coefficients at high frequency although the overall sound absorption coefficient is quiet low. The Increase in fiber loading increases the sound absorption coefficients of composites. The types of polymer did not have significant influences on sound absorption coefficients. Both methods of measurement show the same results where the two fixed microphone method is much quicker than standing wave method.

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22

Poort, K. L., and J. J. Fredberg. "Airway Area by Acoustic Reflection: A Corrected Derivation for the Two-Microphone Method." Journal of Biomechanical Engineering 121, no. 6 (December 1, 1999): 663–65. http://dx.doi.org/10.1115/1.2800872.

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A corrected derivation is provided for the relationship between the impulse response of a wave tube termination and pressure signals measured at two different locations within the tube. This derivation yields exactly the same final result as was reported previously by Louis et al. (1993), despite the omission of the active source term in that earlier derivation. This technique has become the basis of an important medical diagnostic technology. This report revises and corrects the earlier theory upon which that technology rests.

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23

Trethewey, Martin W. "Application of the Selective Two-Microphone Acoustic Intensity Method for Noise Source Identification." Noise Control Engineering Journal 30, no. 1 (1988): 22. http://dx.doi.org/10.3397/1.2827700.

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24

Munjal, M. L., and A. G. Doige. "The two-microphone method incorporating the effects of mean flow and acoustic damping." Journal of Sound and Vibration 137, no. 1 (February 1990): 135–38. http://dx.doi.org/10.1016/0022-460x(90)90722-c.

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25

Kurniawan, Fajri, Mohd Shafry Mohd. Rahim, Mohammed S. Khalil, and Muhammad Khurram Khan. "Statistical Based Audio Forensic on Identical Microphones." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 5 (October 1, 2016): 2211. http://dx.doi.org/10.11591/ijece.v6i5.12022.

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Анотація:

<p>Microphone forensics has become a challenging field due to the proliferation of recording devices and explosion in video/audio recording. Video or audio recording helps a criminal investigator to analyze the scene and to collect evidences. In this regards, a robust method is required to assure the originality of some recordings. In this paper, we focus on digital audio forensics and study how to identify the microphone model. Defining microphone model will allow the investigators to conclude integrity of some recordings. We perform statistical analysis on the recording that is collected from two microphones of the same model. Experimental results and analysis indicate that the signal of sound recording of identical microphone is not exactly same and the difference is up to 1% - 3%.</p>

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26

Kurniawan, Fajri, Mohd Shafry Mohd. Rahim, Mohammed S. Khalil, and Muhammad Khurram Khan. "Statistical Based Audio Forensic on Identical Microphones." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 5 (October 1, 2016): 2211. http://dx.doi.org/10.11591/ijece.v6i5.pp2211-2218.

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Анотація:

<p>Microphone forensics has become a challenging field due to the proliferation of recording devices and explosion in video/audio recording. Video or audio recording helps a criminal investigator to analyze the scene and to collect evidences. In this regards, a robust method is required to assure the originality of some recordings. In this paper, we focus on digital audio forensics and study how to identify the microphone model. Defining microphone model will allow the investigators to conclude integrity of some recordings. We perform statistical analysis on the recording that is collected from two microphones of the same model. Experimental results and analysis indicate that the signal of sound recording of identical microphone is not exactly same and the difference is up to 1% - 3%.</p>

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27

Wei, Zhengyu, Hong Hou, Nansha Gao, Yunke Huang, and Jianhua Yang. "Measurement of Sound Absorption Using a Single Fixed Microphone in a Circular Pulse-Tube." Acta Acustica united with Acustica 105, no. 6 (November 1, 2019): 1228–36. http://dx.doi.org/10.3813/aaa.919399.

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Анотація:

This study presents a method for measuring the normal incidence sound absorption coefficient of acoustical materials by separating the incident and first reflected waves in the time domain using a pulse-tube with only a single microphone whose position is fixed. Based on the pulse generation technique, the effect of the characteristics of tube termination can be eliminated, and the drive signal used for the measurement is obtained. A moveable piston is used to move the sample to a certain position in the tube, which renders the recorded incident and first reflected waves separated. As a validation of the proposed method, two different materials are investigated. Good agreement is found between the proposed method and both the well-established two-microphone transfer function (TMF) method and the verified pulse separation method.

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28

SUKSIRI, Bandhit, and Masahiro FUKUMOTO. "A Highly Efficient Wideband Two-Dimensional Direction Estimation Method with L-Shaped Microphone Array." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E102.A, no. 11 (November 1, 2019): 1457–72. http://dx.doi.org/10.1587/transfun.e102.a.1457.

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29

Bodén, Hans, and Mats Åbom. "Influence of errors on the two‐microphone method for measuring acoustic properties in ducts." Journal of the Acoustical Society of America 79, no. 2 (February 1986): 541–49. http://dx.doi.org/10.1121/1.393542.

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30

Blevins, Matthew G., Joshua Thede, and Lily M. Wang. "Uncertainty of normal-incidence absorption coefficient measurements using the two-microphone cross-spectral method." Journal of the Acoustical Society of America 134, no. 5 (November 2013): 4004. http://dx.doi.org/10.1121/1.4830606.

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31

LI, J., M. AKAGI, and Y. SUZUKI. "A Two-Microphone Noise Reduction Method in Highly Non-stationary Multiple-Noise-Source Environments." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E91-A, no. 6 (June 1, 2008): 1337–46. http://dx.doi.org/10.1093/ietfec/e91-a.6.1337.

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32

Wang, Dong-xia, Mao-song Jiang, Fang-lin Niu, Yu-dong Cao та Cheng-xu Zhou. "Speech Enhancement Control Design Algorithm for Dual-Microphone Systems Using β-NMF in a Complex Environment". Complexity 2018 (9 вересня 2018): 1–13. http://dx.doi.org/10.1155/2018/6153451.

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Анотація:

Single-microphone speech enhancement algorithms by using nonnegative matrix factorization can only utilize the temporal and spectral diversity of the received signal, making the performance of the noise suppression degrade rapidly in a complex environment. Microphone arrays have spatial selection and high signal gain, so it applies to the adverse noise conditions. In this paper, we present a new algorithm for speech enhancement based on two microphones with nonnegative matrix factorization. The interchannel characteristic of each nonnegative matrix factorization basis can be modeled by the adopted method, such as the amplitude ratios and the phase differences between channels. The results of the experiment confirm that the proposed algorithm is superior to other dual-microphone speech enhancement algorithms.

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33

Palmer, Catherine, Ruth Bentler, and Gustav H. Mueller. "Evaluation of a Second-Order Directional Microphone Hearing Aid: II. Self-Report Outcomes." Journal of the American Academy of Audiology 17, no. 03 (March 2006): 190–201. http://dx.doi.org/10.3766/jaaa.17.3.5.

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Анотація:

This clinical trial was undertaken to evaluate the subjective benefit obtained from hearing aids employing automatic switching second-order adaptive directional microphone technology, used in conjunction with digital noise reduction, as compared to a fixed directional microphone or omnidirectional microphone response with the same digital noise reduction. Data were collected for 49 participants across two sites. Both new and experienced hearing aid users were fit bilaterally with behind-the-ear hearing aids using the NAL-NL1 (National Acoustics Laboratory—Nonlinear version 1) prescriptive method with manufacturer default settings for various signal processing (e.g., noise reduction, compression parameters, etc.). During ten days of hearing aid use, participants responded to daily journal questions. Subjective ratings for each of the three hearing aid responses (omnidirectional, automatic-adaptive directional, and automatic-fixed directional) were similar. Overall preference for a microphone condition was equally distributed between no preference, omnidirectional, and automatic adaptive and/or fixed directional.

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34

Li, Ran, Tao Feng, Jing Wang, and Yao Wu. "Measurement Method on the Absorption Coefficient of the Material with Large Dimension Based on Two-Microphone." Applied Mechanics and Materials 490-491 (January 2014): 1584–87. http://dx.doi.org/10.4028/www.scientific.net/amm.490-491.1584.

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Анотація:

This paper is researching on methods to get the absorption coefficient of the material with large dimension. The absorption coefficient can be got by the source mirror method which the FRF of two pressure signals near the material surface in point source field is obtained. The influence of the factors such as the source height, the material size, horizontal distance and positions of two microphones on the accuracy was analyzed by experiments and the indirect boundary element method. The results from this method discussed in this paper are basically the same with that from the impedance tube.

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35

Jing, Xiaodong, and Xiaofeng Sun. "Flat-response sound source technique for using the two-microphone method in an impedance tube." Journal of the Acoustical Society of America 122, no. 5 (2007): 2519. http://dx.doi.org/10.1121/1.2781581.

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36

Hall, Hubert S., Joseph Vignola, John Judge, and Diego Turo. "Exploration into the sources of error in the two-microphone transfer function impedance tube method." Journal of the Acoustical Society of America 136, no. 4 (October 2014): 2209. http://dx.doi.org/10.1121/1.4900012.

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37

Arnela, Marc, and Oriol Guasch. "Finite element computation of elliptical vocal tract impedances using the two-microphone transfer function method." Journal of the Acoustical Society of America 133, no. 6 (June 2013): 4197–209. http://dx.doi.org/10.1121/1.4803889.

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38

Egolf, David P., Brett T. Haley, Kenneth M. Bauer, Henry C. Howell, and Vernon D. Larson. "Experimental determination of cascade parameters of a hearing‐aid microphone via the two‐load method." Journal of the Acoustical Society of America 83, no. 6 (June 1988): 2439–46. http://dx.doi.org/10.1121/1.396323.

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39

Guo, Feng, Jingchang Huang, Xin Zhang, Yongbo Cheng, Huawei Liu, and Baoqing Li. "A Two-Stage Detection Method for Moving Targets in the Wild Based on Microphone Array." IEEE Sensors Journal 15, no. 10 (October 2015): 5795–803. http://dx.doi.org/10.1109/jsen.2015.2448734.

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40

Zhao, Hai Jun, and Zhao Xiang Deng. "Measurement of the Incident Sound Power in Ducts and Effect Factors Analysis." Key Engineering Materials 467-469 (February 2011): 841–46. http://dx.doi.org/10.4028/www.scientific.net/kem.467-469.841.

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Based on two-microphone transfer function method, the measuring method of incident sound power in duct is introduced, The special test equipment is built, and the test and measurement program of incident sound power in duct is written, and effect factors of measurement are analyzed. Results are shown that temperature, flow velocity, microphone spacing deviation and complex reflection coefficient influences less on the program. Transmission loss of perforated tube muffler with no flow is measured using the program, comparison is carried out with results of finite element method, they agrees with well, and with flow sound power of flow regeneration from the muffler is also done, an important foundation is provided for quantitative study on occurring mechanism flow regenerated noise and enhancing attenuation performance of muffler.

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41

Liu, Ching-Feng, Wei-Siang Ciou, Peng-Ting Chen, and Yi-Chun Du. "A Real-Time Speech Separation Method Based on Camera and Microphone Array Sensors Fusion Approach." Sensors 20, no. 12 (June 22, 2020): 3527. http://dx.doi.org/10.3390/s20123527.

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In the context of assisted human, identifying and enhancing non-stationary speech targets speech in various noise environments, such as a cocktail party, is an important issue for real-time speech separation. Previous studies mostly used microphone signal processing to perform target speech separation and analysis, such as feature recognition through a large amount of training data and supervised machine learning. The method was suitable for stationary noise suppression, but relatively limited for non-stationary noise and difficult to meet the real-time processing requirement. In this study, we propose a real-time speech separation method based on an approach that combines an optical camera and a microphone array. The method was divided into two stages. Stage 1 used computer vision technology with the camera to detect and identify interest targets and evaluate source angles and distance. Stage 2 used beamforming technology with microphone array to enhance and separate the target speech sound. The asynchronous update function was utilized to integrate the beamforming control and speech processing to reduce the effect of the processing delay. The experimental results show that the noise reduction in various stationary and non-stationary noise environments were 6.1 dB and 5.2 dB respectively. The response time of speech processing was less than 10ms, which meets the requirements of a real-time system. The proposed method has high potential to be applied in auxiliary listening systems or machine language processing like intelligent personal assistant.

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42

Kuk, Francis, Eric Seper, Chi-Chuen Lau, and Petri Korhonen. "Tracking of Noise Tolerance to Measure Hearing Aid Benefit." Journal of the American Academy of Audiology 28, no. 08 (September 2017): 698–707. http://dx.doi.org/10.3766/jaaa.16053.

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AbstractThe benefits offered by noise reduction (NR) features on a hearing aid had been studied traditionally using test conditions that set the hearing aids into a stable state of performance. While adequate, this approach does not allow the differentiation of two NR algorithms that differ in their timing characteristics (i.e., activation and stabilization time).The current study investigated a new method of measuring noise tolerance (Tracking of Noise Tolerance [TNT]) as a means to differentiate hearing aid technologies. The study determined the within-session and between-session reliability of the procedure. The benefits provided by various hearing aid conditions (aided, two NR algorithms, and a directional microphone algorithm) were measured using this procedure. Performance on normal-hearing listeners was also measured for referencing.A single-blinded, repeated-measures design was used.Thirteen experienced hearing aid wearers with a bilaterally symmetrical (≤10 dB) mild-to-moderate sensorineural hearing loss participated in the study. In addition, seven normal-hearing listeners were tested in the unaided condition.Participants tracked the noise level that met the criterion of tolerable noise level (TNL) in the presence of an 85 dB SPL continuous discourse passage. The test conditions included an unaided condition and an aided condition with combinations of NR and microphone modes within the UNIQUE hearing aid (omnidirectional microphone, no NR; omnidirectional microphone, NR; directional microphone, no NR; and directional microphone, NR) and the DREAM hearing aid (omnidirectional microphone, no NR; omnidirectional microphone, NR). Each tracking trial lasted 2 min for each hearing aid condition. Normal-hearing listeners tracked in the unaided condition only. Nine of the 13 hearing-impaired listeners returned after 3 mo for retesting in the unaided and aided conditions with the UNIQUE hearing aid. The individual TNL was estimated for each participant for all test conditions. The TNT index was calculated as the difference between 85 dB SPL and the TNL.The TNT index varied from 2.2 dB in the omnidirectional microphone, no NR condition to −4.4 dB in the directional microphone, NR on condition. Normal-hearing listeners reported a TNT index of −5.7 dB using this procedure. The averaged improvement in TNT offered by the NR algorithm on the UNIQUE varied from 2.1 dB when used with a directional microphone to 3.0 dB when used with the omnidirectional microphone. The time course of the NR algorithm was different between the UNIQUE and the DREAM hearing aids, with the UNIQUE reaching a stable TNL sooner than the DREAM. The averaged improvement in TNT index from the UNIQUE directional microphone was 3.6 dB when NR was activated and 4.4 dB when NR was deactivated. Together, directional microphone and NR resulted in a total TNT improvement of 6.5 dB. The test–retest reliability of the procedure was high, with an intrasession 95% confidence interval (CI) of 2.2 dB and an intersession 95% CI of 4.2 dB.The effect of the NR and directional microphone algorithms was measured to be 2–3 and 3.6–4.4 dB, respectively, using the TNT procedure. Because of its tracking property and reliability, this procedure may hold promise in differentiating among some hearing aid features that also differ in their time course of action.

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43

Su, Xiao Fei, and Xin Hong Zhang. "Two-Dimensional FIR Filter Design on Wideband Speech Enhancement in Microphone Array." Applied Mechanics and Materials 39 (November 2010): 260–66. http://dx.doi.org/10.4028/www.scientific.net/amm.39.260.

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Due to complexity of sound environment, the performance of speech enhancement is degraded greatly based on traditional beam-forming when directivity of arrival of sound and number of sound are not been estimated precisely. In this paper, according to characteristics of handle-communicated equipment, a method on suppression of environmental noise and disturbance is provided. Beam-forming on speech wideband in desired angle is realized by designing a two-dimensional FIR filter which has fan structure in pass-band and wedge-shaped structure in transition-band. The filter can suppress noise and disturbance out of main-lobe desired. The simulations demonstrated that the filter designed is effective.

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44

MINAMINO, Tomoya, Hiroshi UEHARA, Takayuki KOIZUMI, Nobutaka TSUJIUCHI, and Satoshi MORITA. "302 Noise source separation of co-generation system by using FRF estimation with two microphone method." Proceedings of Conference of Kansai Branch 2008.83 (2008): _3–2_. http://dx.doi.org/10.1299/jsmekansai.2008.83._3-2_.

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45

Murphy, W. J., A. Tubis, and G. R. Long. "Measurement of the local power reflection coefficient in the ear canal using the two‐microphone method." Journal of the Acoustical Society of America 86, S1 (November 1989): S43. http://dx.doi.org/10.1121/1.2027508.

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46

Ghan, Justin, Ben S. Cazzolato, and Scott D. Snyder. "Expression for the estimation of time-averaged acoustic energy density using the two-microphone method (L)." Journal of the Acoustical Society of America 113, no. 5 (May 2003): 2404–7. http://dx.doi.org/10.1121/1.1567273.

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47

Ghan, Justin, Ben Cazzolato, and Scott Snyder. "Statistical errors in the estimation of time-averaged acoustic energy density using the two-microphone method." Journal of the Acoustical Society of America 115, no. 3 (March 2004): 1179–84. http://dx.doi.org/10.1121/1.1639334.

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48

Gombots, Stefan, Jonathan Nowak, and Manfred Kaltenbacher. "Sound source localization – state of the art and new inverse scheme." e & i Elektrotechnik und Informationstechnik 138, no. 3 (March 25, 2021): 229–43. http://dx.doi.org/10.1007/s00502-021-00881-6.

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AbstractAcoustic source localization techniques in combination with microphone array measurements have become an important tool for noise reduction tasks. A common technique for this purpose is acoustic beamforming, which can be used to determine the source locations and source distribution. Advantages are that common algorithms such as conventional beamforming, functional beamforming or deconvolution techniques (e.g., Clean-SC) are robust and fast. In most cases, however, a simple source model is applied and the Green’s function for free radiation is used as transfer function between source and microphone. Additionally, without any further signal processing, only stationary sound sources are covered. To overcome the limitation of stationary sound sources, two approaches of beamforming for rotating sound sources are presented, e.g., in an axial fan.Regarding the restrictions concerning source model and boundary conditions, an inverse method is proposed in which the wave equation in the frequency domain (Helmholtz equation) is solved with the corresponding boundary conditions using the finite element method. The inverse scheme is based on minimizing a Tikhonov functional matching measured microphone signals with simulated ones. This method identifies the amplitude and phase information of the acoustic sources so that the prevailing sound field can be with a high degree of accuracy.

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49

Yang, Boquan, Shengguo Shi, and Desen Yang. "Acoustic source localization using the open spherical microphone array in the low-frequency range." MATEC Web of Conferences 283 (2019): 04001. http://dx.doi.org/10.1051/matecconf/201928304001.

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Recently, spherical microphone arrays (SMA) have become increasingly significant for source localization and identification in three dimension due to its spherical symmetry. However, conventional Spherical Harmonic Beamforming (SHB) based on SMA has limitations, such as poor resolution and high side-lobe levels in image maps. To overcome these limitations, this paper employs the iterative generalized inverse beamforming algorithm with a virtual extrapolated open spherical microphone array. The sidelobes can be suppressed and the main-lobe can be narrowed by introducing the two iteration processes into the generalized inverse beamforming (GIB) algorithm. The instability caused by uncertainties in actual measurements, such as measurement noise and configuration problems in the process of GIB, can be minimized by iteratively redefining the form of regularization matrix and the corresponding GIB localization results. In addition, the poor performance of microphone arrays in the low-frequency range due to the array aperture can be improved by using a virtual extrapolated open spherical array (EA), which has a larger array aperture. The virtual array is obtained by a kind of data preprocessing method through the regularization matrix algorithm. Both results from simulations and experiments show the feasibility and accuracy of the method.

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50

van der Woerd, Benjamin, Min Wu, Vijay Parsa, Philip C. Doyle, and Kevin Fung. "Evaluation of Acoustic Analyses of Voice in Nonoptimized Conditions." Journal of Speech, Language, and Hearing Research 63, no. 12 (December 14, 2020): 3991–99. http://dx.doi.org/10.1044/2020_jslhr-20-00212.

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Objectives This study aimed to evaluate the fidelity and accuracy of a smartphone microphone and recording environment on acoustic measurements of voice. Method A prospective cohort proof-of-concept study. Two sets of prerecorded samples (a) sustained vowels (/a/) and (b) Rainbow Passage sentence were played for recording via the internal iPhone microphone and the Blue Yeti USB microphone in two recording environments: a sound-treated booth and quiet office setting. Recordings were presented using a calibrated mannequin speaker with a fixed signal intensity (69 dBA), at a fixed distance (15 in.). Each set of recordings (iPhone—audio booth, Blue Yeti—audio booth, iPhone—office, and Blue Yeti—office), was time-windowed to ensure the same signal was evaluated for each condition. Acoustic measures of voice including fundamental frequency ( f o ), jitter, shimmer, harmonic-to-noise ratio (HNR), and cepstral peak prominence (CPP), were generated using a widely used analysis program (Praat Version 6.0.50). The data gathered were compared using a repeated measures analysis of variance. Two separate data sets were used. The set of vowel samples included both pathologic ( n = 10) and normal ( n = 10), male ( n = 5) and female ( n = 15) speakers. The set of sentence stimuli ranged in perceived voice quality from normal to severely disordered with an equal number of male ( n = 12) and female ( n = 12) speakers evaluated. Results The vowel analyses indicated that the jitter, shimmer, HNR, and CPP were significantly different based on microphone choice and shimmer, HNR, and CPP were significantly different based on the recording environment. Analysis of sentences revealed a statistically significant impact of recording environment and microphone type on HNR and CPP. While statistically significant, the differences across the experimental conditions for a subset of the acoustic measures (viz., jitter and CPP) have shown differences that fell within their respective normative ranges. Conclusions Both microphone and recording setting resulted in significant differences across several acoustic measurements. However, a subset of the acoustic measures that were statistically significant across the recording conditions showed small overall differences that are unlikely to have clinical significance in interpretation. For these acoustic measures, the present data suggest that, although a sound-treated setting is ideal for voice sample collection, a smartphone microphone can capture acceptable recordings for acoustic signal analysis.

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