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Journal articles on the topic 'Acoustics- Signal processing'

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Author: Grafiati

Published: 6 September 2023

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1

Brown, David A., Paul J. Gendron, and John R. Buck. "Graduate education in acoustic engineering, transduction, and signal processing University of Massachusetts Dartmouth." Journal of the Acoustical Society of America 152, no. 4 (October 2022): A123. http://dx.doi.org/10.1121/10.0015756.

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The University of Massachusetts Dartmouth has an established graduate program of study with a concentration in Applied Acoustics leading to the M.S. and Ph.D. degree in Electrical Engineering. The program offers courses and research opportunities in the area of electroacoustic transduction, underwater acoustics, and signal processing. Courses include the Fundamentals of Acoustics, Random Signals, Underwater Acoustics, Introduction to Transducers, Electroacoustic Transduction, Medical Ultrasonics, Digital Signal Processing, Detection Theory, and Estimation Theory. The ECE department established the university’s indoor underwater acoustic test and calibration facility which is one of the largest academic facilities supporting undergraduate and graduate thesis and sponsored research. The department has collaborations with many marine acoustic related companies including nearby Naval Undersea Warfare Center in Newport, RI and Woods Hole Oceanographic Institute in Cape Cod, MA. The presentation will highlight recent theses and dissertations, course offerings, and industry and government collaborations that support acoustical engineering, transduction, and signal processing.

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2

Gaudette, Jason E., and James A. Simmons. "Linear time-invariant (LTI) modeling for aerial and underwater acoustics." Journal of the Acoustical Society of America 153, no. 3_supplement (March 1, 2023): A95. http://dx.doi.org/10.1121/10.0018285.

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Most newcomers to acoustic signal processing understand that linear time-invariant (LTI) filters can remove out-of-band noise from time series signals. What many acoustics researchers may not realize is that LTI models can be applied much more broadly, including to non-linear and time-variant systems. This presentation covers an overview of the autoregressive (AR), moving-average (MA), and autoregressive moving-average (ARMA) family of LTI models and their many useful applications in acoustics. Examples include analytic time-frequency processing of multi-component echolocation signals, fractional-delay filtering for acoustic time series simulations, broadband acoustic array beamforming, adaptive filtering for noise cancelation, and system identification for acoustic equalizers (i.e., flattening the frequency response of a source-receiver pair). This talk serves as a brief tutorial and inspiration for researchers who want to expand their use of signal processing, especially those in the fields of animal bioacoustics, aerial acoustics, and underwater acoustics.

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3

Candy, James V. "Signal processing in acoustics." Journal of the Acoustical Society of America 106, no. 3 (1999): 1207. http://dx.doi.org/10.1121/1.428244.

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4

Preisig, James. "Signal processing: Ubiquitous in acoustics." Journal of the Acoustical Society of America 139, no. 4 (April 2016): 2005. http://dx.doi.org/10.1121/1.4949887.

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5

Barnard, Andrew, and Daniel A. Russell. "The graduate program in acoustics at Penn State." Journal of the Acoustical Society of America 152, no. 4 (October 2022): A124. http://dx.doi.org/10.1121/10.0015762.

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The Graduate Program in Acoustics at Penn State offers graduate degrees (M.Eng., M.S., Ph.D.) in Acoustics, with courses and research opportunities in a wide variety of subfields. Our 820 alumni are employed around the world in a wide variety of military and government labs, academic institutions, consulting firms, and consumer audio and related industries. Our 40+ faculty from several disciplines conduct research and teach courses in structural acoustics, nonlinear acoustics, architectural acoustics, signal processing, aeroacoustics, biomedical ultrasound, transducers, computational acoustics, noise and vibration control, acoustic metamaterials, psychoacoustics, and underwater acoustics. Course offerings include fundamentals of acoustics and vibration, electroacoustic transducers, signal processing, acoustics in fluid media, sound and structure interaction, digital signal processing, experimental techniques, acoustic measurements and data analysis, ocean acoustics, architectural acoustics, noise control engineering, nonlinear acoustics, outdoor sound propagation, computational acoustics, biomedical ultrasound, flow induced noise, spatial sound and three-dimensional audio, and the acoustics of musical instruments. This poster highlights faculty research areas, laboratory facilities, student demographics, successful graduates, and recent enrollment and employment trends for the Graduate Program in Acoustics at Penn State.

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6

Havelock, David I. "History of signal processing in acoustics." Journal of the Acoustical Society of America 111, no. 5 (2002): 2368. http://dx.doi.org/10.1121/1.4778007.

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7

Candy, James. "Hot topics: Signal processing in acoustics." Journal of the Acoustical Society of America 111, no. 5 (2002): 2408. http://dx.doi.org/10.1121/1.4778211.

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8

Chambers, David H. "Overview of Signal Processing in Acoustics." Journal of the Acoustical Society of America 129, no. 4 (April 2011): 2408. http://dx.doi.org/10.1121/1.3587847.

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9

Gaumond, Charles F. "Hot topics: Signal processing in acoustics." Journal of the Acoustical Society of America 118, no. 3 (September 2005): 1972. http://dx.doi.org/10.1121/1.4781810.

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10

Culver, Richard L. "Overview of Signal Processing in Acoustics." Journal of the Acoustical Society of America 135, no. 4 (April 2014): 2182. http://dx.doi.org/10.1121/1.4877101.

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11

Davies, H., and D. J. McNeil. "Digital signal processing in acoustics. I." Physics Education 20, no. 6 (November 1, 1985): 278–80. http://dx.doi.org/10.1088/0031-9120/20/6/003.

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12

Davies, H., and D. J. McNeill. "Digital signal processing in acoustics. II." Physics Education 21, no. 5 (September 1, 1986): 300–306. http://dx.doi.org/10.1088/0031-9120/21/5/009.

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13

Kudo, Masaki. "Acoustic signal processing apparatus." Journal of the Acoustical Society of America 95, no. 6 (June 1994): 3687. http://dx.doi.org/10.1121/1.409884.

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14

Birdsall, Theodore G., Kurt Metzger, and Matthew A. Dzieciuch. "Signals, signal processing, and general results." Journal of the Acoustical Society of America 96, no. 4 (October 1994): 2343–52. http://dx.doi.org/10.1121/1.410106.

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15

Dewhurst, David J. "Signal processing." Journal of the Acoustical Society of America 89, no. 5 (May 1991): 2481. http://dx.doi.org/10.1121/1.400842.

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16

Vorländer, Michael. "Virtual Acoustics." Archives of Acoustics 39, no. 3 (March 1, 2015): 307–18. http://dx.doi.org/10.2478/aoa-2014-0036.

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Abstract Virtual Reality (VR) systems are used in engineering, architecture, design and in applications of biomedical research. The component of acoustics in such VR systems enables the creation of audio-visual stimuli for applications in room acoustics, building acoustics, automotive acoustics, environmental noise control, machinery noise control, and hearing research. The basis is an appropriate acoustic simulation and auralization technique together with signal processing tools. Auralization is based on time-domain modelling of the components of sound source characterization, sound propagation, and on spatial audio technology. Whether the virtual environment is considered sufficiently accurate or not, depends on many perceptual factors, and on the pre-conditioning and immersion of the user in the virtual environment. In this paper the processing steps for creation of Virtual Acoustic Environments and the achievable degree of realism are briefly reviewed. Applications are discussed in examples of room acoustics, archeological acoustics, aircraft noise, and audiology.

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17

Baggeroer, Arthur B. "Sonar signal processing and shallow water acoustics." Journal of the Acoustical Society of America 93, no. 4 (April 1993): 2270. http://dx.doi.org/10.1121/1.406604.

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18

Chambers, David H., Brian E. Anderson, Brian G. Ferguson, Kam W. Lo, and Michael J. Roan. "Signal Processing in Physical and Engineering Acoustics." Acoustics Today 7, no. 3 (2011): 17. http://dx.doi.org/10.1121/1.3658271.

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19

Culver, R. Lee, and Ning Xiang. "Advanced methods of signal processing in acoustics." Journal of the Acoustical Society of America 136, no. 4 (October 2014): 2222. http://dx.doi.org/10.1121/1.4900067.

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20

Yonovitz, Al, Herbert Joe, and Joshua Yonovitz. "Digital signal processing in forensic acoustics cases." Journal of the Acoustical Society of America 132, no. 3 (September 2012): 1971. http://dx.doi.org/10.1121/1.4755270.

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21

Xiang, Ning. "Model-based Bayesian signal processing in acoustics." Journal of the Acoustical Society of America 143, no. 3 (March 2018): 1863. http://dx.doi.org/10.1121/1.5036111.

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22

Chambers, David H. "Hot topics of signal processing in acoustics." Journal of the Acoustical Society of America 129, no. 4 (April 2011): 2541. http://dx.doi.org/10.1121/1.3588447.

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23

Xiang, Ning, and David Chambers. "Hot topics of signal processing in acoustics." Journal of the Acoustical Society of America 125, no. 4 (April 2009): 2651. http://dx.doi.org/10.1121/1.4784152.

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24

Putra A, I. Nengah, Nuri Nur Cahyono, Gesit Pratiknyo, and M. Sigit Purwanto. "DESIGN OF DETECTING ACOUSTIC WAVES AT THE EXERCISE SMART MINE USING ACOUSTIC SENSOR." JOURNAL ASRO 11, no. 2 (April 20, 2020): 64. http://dx.doi.org/10.37875/asro.v11i2.270.

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The Indonesian Navy is a unit of defense of the Republic of Indonesia (NKRI). Sea mines are explosive devices placed in waters to destroy ships. Acoustics is a branch of physics that deals with all mechanical waves in liquids, gases and solids, such as vibration, sound and ultrasonic. Arduino has made microcontroller technology more accessible and easier for even novice users. Because of this, there are special functions in audio or acoustic processing techniques aimed at using Arduino. The application of acoustic signals in the military field of the Navy, in the identification of vessels caught by the microphone receiver. The movement of objects that are caught by the microphone will be analyzed to conclude the object. From the results of the sound characteristics raised by the object, it will give a different picture for different variations of the object. Processing acoustic signals using DSP will make it easier to infer the captured acoustic signal. The circuit board used is a microphone signal amplifier and signal conditioner, for processing the audio signal obtained from the microphone and to produce output data which is the result of the process using Arduino. Analog-to-digital converter (ADC) of the microcontroller works in the voltage range from 0.0 V to +3.3 V or +5.0 V. Toachieve good sampling results, that the signal peak is close to the maximum value of the ADC. Additionally, voltage amplitudes above this threshold can damage the controller input port or produce unwanted harmonics. Therefore, a signal pre-amp circuit is needed to guarantee the appropriate audio sample signal as well as to protect the system. The test results obtained from the system analyzer frequency and signal meters can be used to identify acoustic signals. Keyword: Singnal Acoustic/michophone, controller, Arduino Uno, pre-amp frequency analyzer

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25

Birdsall, Theodore G. "Acoustical Society of America Silver Medal in Signal Processing in Acoustics." Journal of the Acoustical Society of America 130, no. 4 (October 2011): 2483–86. http://dx.doi.org/10.1121/1.3655729.

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26

Sullivan, Edmund J. "Acoustical Society of America Silver Medal in Signal Processing in Acoustics." Journal of the Acoustical Society of America 128, no. 4 (October 2010): 2397–400. http://dx.doi.org/10.1121/1.3511521.

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27

Kulkarni, Abhijit. "AUDIO SIGNAL PROCESSING." Journal of the Acoustical Society of America 133, no. 4 (2013): 2514. http://dx.doi.org/10.1121/1.4800116.

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28

Wang, Wen. "Audio signal processing." Journal of the Acoustical Society of America 128, no. 5 (2010): 3275. http://dx.doi.org/10.1121/1.3525338.

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29

Kulkarni, Abhijit. "Audio signal processing." Journal of the Acoustical Society of America 126, no. 1 (2009): 518. http://dx.doi.org/10.1121/1.3182994.

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30

Rajan, Jebu Jacob. "Signal processing system." Journal of the Acoustical Society of America 122, no. 1 (2007): 27. http://dx.doi.org/10.1121/1.2756477.

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31

Katayama, Takashi. "Signal processing apparatus, signal processing method, program and recording medium." Journal of the Acoustical Society of America 125, no. 5 (2009): 3483. http://dx.doi.org/10.1121/1.3139560.

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32

Ahmed, Umair, Fakhre Ali, and Ian Jennions. "Signal Processing of Acoustic Data for Condition Monitoring of an Aircraft Ignition System." Machines 10, no. 9 (September 19, 2022): 822. http://dx.doi.org/10.3390/machines10090822.

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Degradation of the ignition system can result in startup failure in an aircraft’s auxiliary power unit. In this paper, a novel acoustics-based solution that can enable condition monitoring of an APU ignition system was proposed. In order to support the implementation of this research study, the experimental data set from Cranfield University’s Boeing 737-400 aircraft was utilized. The overall execution of the approach comprised background noise suppression, estimation of the spark repetition frequency and its fluctuation, spark event segmentation, and feature extraction, in order to monitor the state of the ignition system. The methodology successfully demonstrated the usefulness of the approach in terms of detecting inconsistencies in the behavior of the ignition exciter, as well as detecting trends in the degradation of spark acoustic characteristics. The identified features proved to be robust against non-stationary background noise, and were also found to be independent of the acoustic path between the igniter and microphone locations, qualifying an acoustics-based approach to be practically viable.

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33

Cluett, Seth. "Interactions among acoustics, digital signal processing, and movement." Journal of the Acoustical Society of America 108, no. 5 (November 2000): 2538. http://dx.doi.org/10.1121/1.4743402.

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34

Culver, R. L. "Signal processing in acoustics: Why not get involved?" Journal of the Acoustical Society of America 132, no. 3 (September 2012): 1900. http://dx.doi.org/10.1121/1.4754968.

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35

Candy, James V. "Model‐based signal processing in acoustics: An overview." Journal of the Acoustical Society of America 101, no. 5 (May 1997): 3155. http://dx.doi.org/10.1121/1.419141.

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36

Stephens, David B., and Håvard Vold. "Order tracking signal processing for open rotor acoustics." Journal of Sound and Vibration 333, no. 16 (August 2014): 3818–30. http://dx.doi.org/10.1016/j.jsv.2014.04.005.

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37

Candy, James V. "Signal Processing in Acoustics: Science or Science Fiction?" Acoustics Today 4, no. 3 (2008): 6. http://dx.doi.org/10.1121/1.2994726.

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38

Russell, Daniel A., and Andrew Barnard. "Graduate education in acoustics at a distance from Penn State." Journal of the Acoustical Society of America 152, no. 4 (October 2022): A125. http://dx.doi.org/10.1121/10.0015763.

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The Graduate Program in Acoustics at Penn State has been providing access to graduate level education in Acoustics for remote students across the country and around the world for more than 35 years. This poster summarizes the distance education Acoustics program from Penn State by showcasing student demographics, capstone paper topics, enrollment statistics and trends, and the success of our graduates. Our distance education program is offered in conjunction with our resident graduate program—course lectures are broadcast as a live stream over Zoom from a hybrid multimedia classroom allowing remote students to engage with faculty and students during live lectures; archived recordings are available for offline viewing afterward. Courses offered for distance education students include: fundamentals of acoustics and vibration, electroacoustic transducers, signal processing, acoustics in fluid media, sound and structure interaction, digital signal processing, aerodynamic noise, acoustic measurements and data analysis, ocean acoustics, architectural acoustics, noise control engineering, nonlinear acoustics, outdoor sound propagation, computational acoustics, flow induced noise, spatial sound and 3D audio, marine bioacoustics, and acoustics of musical instruments. Distance Education students can earn the M.Eng. in Acoustics degree remotely by completing 30 credits of coursework and writing a capstone paper.

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39

Klepka, Andrzej, Wieslaw Jerzy Staszewski, Kajetan Dziedziech, and Francesco Aymerich. "Non-Linear Vibro-Acoustic Wave Modulations - Analysis of Different Types of Low-Frequency Excitation." Key Engineering Materials 569-570 (July 2013): 924–31. http://dx.doi.org/10.4028/www.scientific.net/kem.569-570.924.

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Signal processing method based on wavelet transform used in non-linear acoustic test is presented in the paper. The method is applied for sidebands identification in response signal acquired during vibro-acoustic modulation test of impacted carbon fiber reinforced plate (CFRP). The plate was impacted with known energy using drop-weight testing machine. The modulation effect in investigated specimen results from the interaction of low and high frequency excitation with damage. The paper investigates different than mono-harmonic low-frequency excitation usually used in non-linear acoustics tests. Application of aperiodic low-frequency excitation signal allows to omit the modal test, where natural frequency of the structure are estimated. However, this requires the use of dedicated signal processing methods.

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40

Hawley, Scott H. "Development tools for deep learning models of acoustical signal processing." Journal of the Acoustical Society of America 151, no. 4 (April 2022): A230. http://dx.doi.org/10.1121/10.0011154.

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We present a survey of available frameworks for developing acoustical signal processing models based on deep neural networks. Given that this is a dynamic space with new frameworks, libraries, and even companies appearing on timescales measured in months, we provide an up-to-date assessment of the strength, popularity, and near-future directions of several tools and platforms available for research and product deployment for deep learning models of audio signal processing. Similarly, those new to these spaces may be unaware of software systems that will allow them to obtain and interrogate results more quickly and easily, while also integrating the nearly state-of-the-art optimization methods. Included tools, packages and platforms include PyTorch, Tensorflow, Keras, JAX, fastai, PyTorch Lightning, Julia, nbdev, HuggingFace, Weights and Biases, and Gradio. Examples will be drawn from the speaker's recent research publications in musical signal processing and computer vision applied to musical acoustics, as well as recent work by others. The goal of the talk is to provide acoustics researchers, educators, students with a set of helpful possibilities for pursuing and improving their understanding, research practices, and communications.

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41

Bates, Bruce J. "Acoustic Signal Processing for Ocean Exploration." Journal of the Acoustical Society of America 95, no. 4 (April 1994): 2294. http://dx.doi.org/10.1121/1.408610.

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42

Bellisario, Afarin O. "Digital revolution in acoustic signal processing." Journal of the Acoustical Society of America 95, no. 5 (May 1994): 2900. http://dx.doi.org/10.1121/1.409317.

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43

Yamaguchi, Hirohisa, and Yoshito Higa. "Digital signal processing acoustic speaker system." Journal of the Acoustical Society of America 116, no. 3 (2004): 1320. http://dx.doi.org/10.1121/1.1809884.

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44

Dzieciuch, Matthew A., Kurt Metzger, and Theodore G. Birdsall. "HIFE signal processing results." Journal of the Acoustical Society of America 90, no. 4 (October 1991): 2348. http://dx.doi.org/10.1121/1.402147.

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45

Honda, Masaaki, and Takehiro Moriya. "Speech signal processing system." Journal of the Acoustical Society of America 89, no. 1 (January 1991): 491. http://dx.doi.org/10.1121/1.400434.

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46

Fujita, Shinichi. "Stereophonic signal processing circuit." Journal of the Acoustical Society of America 89, no. 6 (June 1991): 3034. http://dx.doi.org/10.1121/1.400815.

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47

Katoh, Mitsumi. "Tone signal processing device." Journal of the Acoustical Society of America 83, no. 4 (April 1988): 1715. http://dx.doi.org/10.1121/1.395874.

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48

Richardson, Anthony M., and Loren W. Nolte. "Uncertain field signal processing." Journal of the Acoustical Society of America 89, no. 4B (April 1991): 1999. http://dx.doi.org/10.1121/1.2029836.

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49

Blackmer, David E. "Audio signal processing system." Journal of the Acoustical Society of America 77, no. 6 (June 1985): 2213. http://dx.doi.org/10.1121/1.392370.

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50

Kane, Joji, and Akira Nohara. "Voice signal processing device." Journal of the Acoustical Society of America 95, no. 5 (May 1994): 2794. http://dx.doi.org/10.1121/1.409793.

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