Ultra-Broadband Local Active Noise Control with Remote Acoustic Sensing
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Grey Noise, Dubai Unit 24, Alserkal Avenue Street 8, Al Quoz 1, Dubai
Grey Noise, Dubai Stéphanie Saadé b. 1983, Lebanon Lives and works between Beirut, Paris and Amsterdam Education and Residencies 2018 - 2019 3 Package Deal, 1 year “Interhistoricity” (Scholarship received from the AmsterdamFonds Voor Kunst and the Bureau Broedplaatsen), In partnership with Museum Van Loon Oude Kerk, Castrum Peregrini and Reinwardt Academy, Amsterdam, Netherlands 2018 Studio Cur’art and Bema, Mexico City and Guadalajara 2017 – 2018 Maison Salvan, Labège, France Villa Empain, Fondation Boghossian, Brussels, Belgium 2015 – 2016 Cité Internationale des Arts, Paris, France (Recipient of the French Institute residency program scholarship) 2014 – 2015 Jan Van Eyck Academie, Maastricht, The Netherlands 2013 PROGR, Bern, Switzerland 2010 - 2012 China Academy of Arts, Hangzhou, China (State-funded Scholarship for post- graduate program) 2005 – 2010 École Nationale Supérieure des Beaux-Arts, Paris, France (Diplôme National Supérieur d’Arts Plastiques) Awards 2018 Recipient of the 2018 NADA MIAMI Artadia Art Award Selected Solo Exhibitions 2020 (Upcoming) AKINCI Gallery, Amsterdam, Netherlands 2019 The Travels of Here and Now, Museum Van Loon, Amsterdam, Netherlands with the support of Amsterdams fonds voor de Kunst The Encounter of the First and Last Particles of Dust, Grey Noise, Dubai L’espace De 70 Jours, La Scep, Marseille, France Unit 24, Alserkal Avenue Street 8, Al Quoz 1, Dubai, UAE / T +971 4 3790764 F +971 4 3790769 / [email protected] / www.greynoise.org Grey Noise, Dubai 2018 Solo Presentation at Nada Miami with Counter -
ACCESSORIES and BATTERIES MTX Professional Series Portable Two-Way Radios MTX Series
ACCESSORIES AND BATTERIES MTX Professional Series Portable Two-Way Radios MTX Series Motorola Original® Accessories let you make the most of your MTX Professional Series radio’s capabilities. You made a sound business decision when you chose your Motorola MTX Professional Series Two-Way radio. When you choose performance-matched Motorola Original® accessories, you’re making another good call. That’s because every Motorola Original® accessory is designed, built and rigorously tested to the same quality standards as Motorola radios. So you can be sure that each will be ready to do its job, time after time — helping to increase your productivity. After all, if you take a chance on a mismatched battery, a flimsy headset, or an ill-fitting carry case, your radio may not perform at the moment you have to use it. We’re pleased to bring you this collection of proven Motorola Original® accessories for your important business communication needs. You’ll find remote speaker microphones for more convenient control. Discreet earpiece systems to help ensure privacy and aid surveillance. Carry cases for ease when on the move. Headsets for hands-free operation. Premium batteries to extend work time, and more. They’re all ready to help you work smarter, harder and with greater confidence. Motorola MTX Professional Series radios keep you connected with a wider calling range, faster channel access, greater privacy and higher user and talkgroup capacity. MTX PROFESSIONAL SERIES PORTABLE RADIOS TM Intelligent radio so advanced, it practically thinks for you. MTX Series Portable MTX850 Two-Way Radios are the smart choice for your business - delivering everything you need for great business communication. -
Portable Radio: Headsets XTS 3000, XTS 3500, XTS 5000, XTS 1500, XTS 2500, MT 1500, and PR1500
portable radio: headsets XTS 3000, XTS 3500, XTS 5000, XTS 1500, XTS 2500, MT 1500, and PR1500 Noise-Cancelling Headsets with Boom Microphone Ideal for two-way communication in extremely noisy environments. Dual-Muff, tactical headsets RMN4052A include 2 microphones on outside of earcups which reproduce ambient sound back into headset. Harmful sounds are suppressed to safe levels and low sounds are amplified up to 5 times original level, but never more than 82dB. Has on/off volume control on earcups. Easily replaceable ear seals are filled with a liquid and foam combination to provide excellent sealing and comfort. 3-foot coiled cord assembly included. Adapter Cable RKN4095A required. RMN4052A Tactical Headband Style Headset. Grey, Noise reduction = 24dB. RMN4053A Tactical Hardhat Mount Headset. Grey, Noise reduction = 22dB. RKN4095A Adapter Cable with In-Line Push-to-Talk For use with RMN4051B/RMN4052A/RMN4053A. REPLACEMENTEARSEALS RLN4923B Replacement Earseals for RMN4051B/RMN4052A/RMN4053A Includes 2 snap-in earseals. Hardhat Mount Headset With Noise-Cancelling Boom Microphone RMN4051B Ideal for two-way communication in high-noise environments, this headset mounts easily to hard- hats with slots, or can be worn alone. Easily replaceable ear seals are filled with a liquid and foam combination to provide excellent sealing and comfort. 3-foot coiled cord assembly included. Noise reduction of 22dB. Separate In-line PTT Adapter Cable RKN4095A required. Hardhat not included. RMN4051B* Two-way, Hardhat Mount Headset with Noise-Cancelling Boom Microphone. Black. RKN4095A Adapter cable with In-Line PTT. For use with RMN4051B/RMN4052A/RMN4053A. Medium Weight Headsets Medium weight headsets offer high-clarity sound, with the additional hearing protection necessary to provide consistent, clear, two-way radio communication in harsh, noisy environments or situations. -
LPM Woofer Sample Report
HTML Report - Klippel GmbH Page 1 of 10 KLIPPEL ANALYZER SYSTEM Report with comments Linear Parameter Measurement (LPM) Driver Name: w2017 midrange Driver Comment: Measurement: LPM south free air Measurement Measureslinear parameters of woofers. Comment : Driver connected to output SPEAKER 2. Overview The Introductory Report illustrates the powerful features of the Klippel Analyzer module dedicated to the measurement of the linear speaker parameters. Additional comments are added to the results of a practical measurement applied to the speaker specified above. After presenting short information to the measurement technique the report comprises the following results • linear speaker parameters + mechanical creep factor • electrical impedance response • mechanical transfer response (voltage to voice coil displacement) • acoustical transfer response (voltage to SPL) • time signals of the speaker variables during measurement • spectra of the speaker variables (fundamental, distortion, noise floor) • summary on the signal statistics (peak value, SNR, headroom,…). MEASUREMENT TECHNIQUE The measurement module identifies the electrical and mechanical parameters (Thiele-Small parameters) of electro-dynamical transducers. The electrical parameters are determined by measuring terminal voltage u(t) and current i(t) and exploiting the electrical impedance Z(f)=U(f)/I(f). The mechanical parameters can either be identified using a laser displacement sensor or by a second (comparative) measurement where the driver is placed in a test enclosure or an additional mass is attached to it. For the first method the displacement of the driver diaphragm is measured in order to exploit the function Hx(f)= X(f)/U(f). So the identification dispenses with a second measurement and avoids problems due to leakage of the test enclosure or mass attachment. -
An Introduction to Opensound Navigator™
WHITEPAPER 2016 An introduction to OpenSound Navigator™ HIGHLIGHTS OpenSound Navigator™ is a new speech-enhancement algorithm that preserves speech and reduces noise in complex environments. It replaces and exceeds the role of conventional directionality and noise reduction algorithms. Key technical advances in OpenSound Navigator: • Holistic system that handles all acoustical environments from the quietest to the noisiest and adapts its response to the sound preference of the user – no modes or mode switch. • Integrated directional and noise reduction action – rebalance the sound scene, preserve speech in all directions, and selectively reduce noise. • Two-microphone noise estimate for a “spatially-informed” estimate of the environmental noise, enabling fast and accurate noise reduction. The speed and accuracy of the algorithm enables selective noise reduction without isolating the talker of interest, opening up new possibilities for many audiological benefits. Nicolas Le Goff,Ph.D., Senior Research Audiologist, Oticon A/S Jesper Jensen, Ph.D., Senior Scientist, Oticon A/S; Professor, Aalborg University Michael Syskind Pedersen, Ph.D., Lead Developer, Oticon A/S Susanna Løve Callaway, Au.D., Clinical Research Audiologist, Oticon A/S PAGE 2 WHITEPAPER – 2016 – OPENSOUND NAVIGATOR The daily challenge of communication To make sense of a complex acoustical mixture, the brain in noise organizes the sound entering the ears into different Sounds occur around us virtually all the time and they auditory “objects” that can then be focused on or put in start and stop unpredictably. We constantly monitor the background. The formation of these auditory objects these changes and choose to interact with some of the happens by assembling sound elements that have similar sounds, for instance, when engaging in a conversation features (e.g., Bregman, 1990). -
AN279: Estimating Period Jitter from Phase Noise
AN279 ESTIMATING PERIOD JITTER FROM PHASE NOISE 1. Introduction This application note reviews how RMS period jitter may be estimated from phase noise data. This approach is useful for estimating period jitter when sufficiently accurate time domain instruments, such as jitter measuring oscilloscopes or Time Interval Analyzers (TIAs), are unavailable. 2. Terminology In this application note, the following definitions apply: Cycle-to-cycle jitter—The short-term variation in clock period between adjacent clock cycles. This jitter measure, abbreviated here as JCC, may be specified as either an RMS or peak-to-peak quantity. Jitter—Short-term variations of the significant instants of a digital signal from their ideal positions in time (Ref: Telcordia GR-499-CORE). In this application note, the digital signal is a clock source or oscillator. Short- term here means phase noise contributions are restricted to frequencies greater than or equal to 10 Hz (Ref: Telcordia GR-1244-CORE). Period jitter—The short-term variation in clock period over all measured clock cycles, compared to the average clock period. This jitter measure, abbreviated here as JPER, may be specified as either an RMS or peak-to-peak quantity. This application note will concentrate on estimating the RMS value of this jitter parameter. The illustration in Figure 1 suggests how one might measure the RMS period jitter in the time domain. The first edge is the reference edge or trigger edge as if we were using an oscilloscope. Clock Period Distribution J PER(RMS) = T = 0 T = TPER Figure 1. RMS Period Jitter Example Phase jitter—The integrated jitter (area under the curve) of a phase noise plot over a particular jitter bandwidth. -
A Deep Learning Approach to Active Noise Control
A Deep Learning Approach to Active Noise Control Hao Zhang1, DeLiang Wang1;2 1Department of Computer Science and Engineering, The Ohio State University, USA 2Center for Cognitive and Brain Sciences, The Ohio State University, USA fzhang.6720, [email protected] Abstract Reference Error Microphone �(�) Microphone �(�) We formulate active noise control (ANC) as a supervised �(�) �(�) �(�) learning problem and propose a deep learning approach, called �(�) deep ANC, to address the nonlinear ANC problem. A convo- lutional recurrent network (CRN) is trained to estimate the real Cancelling Loudspeaker �(�) and imaginary spectrograms of the canceling signal from the reference signal so that the corresponding anti-noise can elimi- ANC nate or attenuate the primary noise in the ANC system. Large- scale multi-condition training is employed to achieve good gen- Figure 1: Diagram of a single-channel feedforward ANC sys- eralization and robustness against a variety of noises. The deep tem, where P (z) and S(z) denote the frequency responses of ANC method can be trained to achieve active noise cancella- primary path and secondary path, respectively. tion no matter whether the reference signal is noise or noisy speech. In addition, a delay-compensated strategy is intro- tional link artificial neural network to handle the nonlinear ef- duced to address the potential latency problem of ANC sys- fect in ANC. Other nonlinear adaptive models such as radial tems. Experimental results show that the proposed method is basis function networks [12], fuzzy neural networks [13], and effective for wide-band noise reduction and generalizes well to recurrent neural networks [14] have been developed to further untrained noises. -
Active Noise Control in a Three-Dimensional Space Jihe Yang Iowa State University
Iowa State University Capstones, Theses and Retrospective Theses and Dissertations Dissertations 1994 Active noise control in a three-dimensional space Jihe Yang Iowa State University Follow this and additional works at: https://lib.dr.iastate.edu/rtd Part of the Acoustics, Dynamics, and Controls Commons, and the Physics Commons Recommended Citation Yang, Jihe, "Active noise control in a three-dimensional space " (1994). Retrospective Theses and Dissertations. 10660. https://lib.dr.iastate.edu/rtd/10660 This Dissertation is brought to you for free and open access by the Iowa State University Capstones, Theses and Dissertations at Iowa State University Digital Repository. It has been accepted for inclusion in Retrospective Theses and Dissertations by an authorized administrator of Iowa State University Digital Repository. For more information, please contact [email protected]. U'M'I MICROFILMED 1994 INFORMATION TO USERS This manuscript has been reproduced from the microfilm master. UMI films the text directly from the original or copy submitted. Thus, some thesis and dissertation copies are in typewriter face, while others may be from any type of computer printer. The quality of this reproduction is dependent upon the quality of the copy submitted. Broken or indistinct print, colored or poor quality illustrations and photographs, print bleedthrough, substandard margins, and improper alignment can adversely affect reproduction. In the unlikely event that the author did not send UMI a complete manuscript and there are missing pages, these vrill be noted. Also, if unauthorized copyright material had to be removed, a note will indicate the deletion. Oversize materials (e.g., maps, drawings, charts) are reproduced by sectioning the original, beginning at the upper left-hand comer and continuing from left to right in equal sections with small overlaps. -
Signal-To-Noise Ratio and Dynamic Range Definitions
Signal-to-noise ratio and dynamic range definitions The Signal-to-Noise Ratio (SNR) and Dynamic Range (DR) are two common parameters used to specify the electrical performance of a spectrometer. This technical note will describe how they are defined and how to measure and calculate them. Figure 1: Definitions of SNR and SR. The signal out of the spectrometer is a digital signal between 0 and 2N-1, where N is the number of bits in the Analogue-to-Digital (A/D) converter on the electronics. Typical numbers for N range from 10 to 16 leading to maximum signal level between 1,023 and 65,535 counts. The Noise is the stochastic variation of the signal around a mean value. In Figure 1 we have shown a spectrum with a single peak in wavelength and time. As indicated on the figure the peak signal level will fluctuate a small amount around the mean value due to the noise of the electronics. Noise is measured by the Root-Mean-Squared (RMS) value of the fluctuations over time. The SNR is defined as the average over time of the peak signal divided by the RMS noise of the peak signal over the same time. In order to get an accurate result for the SNR it is generally required to measure over 25 -50 time samples of the spectrum. It is very important that your input to the spectrometer is constant during SNR measurements. Otherwise, you will be measuring other things like drift of you lamp power or time dependent signal levels from your sample. -
Image Denoising by Autoencoder: Learning Core Representations
Image Denoising by AutoEncoder: Learning Core Representations Zhenyu Zhao College of Engineering and Computer Science, The Australian National University, Australia, [email protected] Abstract. In this paper, we implement an image denoising method which can be generally used in all kinds of noisy images. We achieve denoising process by adding Gaussian noise to raw images and then feed them into AutoEncoder to learn its core representations(raw images itself or high-level representations).We use pre- trained classifier to test the quality of the representations with the classification accuracy. Our result shows that in task-specific classification neuron networks, the performance of the network with noisy input images is far below the preprocessing images that using denoising AutoEncoder. In the meanwhile, our experiments also show that the preprocessed images can achieve compatible result with the noiseless input images. Keywords: Image Denoising, Image Representations, Neuron Networks, Deep Learning, AutoEncoder. 1 Introduction 1.1 Image Denoising Image is the object that stores and reflects visual perception. Images are also important information carriers today. Acquisition channel and artificial editing are the two main ways that corrupt observed images. The goal of image restoration techniques [1] is to restore the original image from a noisy observation of it. Image denoising is common image restoration problems that are useful by to many industrial and scientific applications. Image denoising prob- lems arise when an image is corrupted by additive white Gaussian noise which is common result of many acquisition channels. The white Gaussian noise can be harmful to many image applications. Hence, it is of great importance to remove Gaussian noise from images. -
Active Noise Control Over Space: a Subspace Method for Performance Analysis
applied sciences Article Active Noise Control over Space: A Subspace Method for Performance Analysis Jihui Zhang 1,* , Thushara D. Abhayapala 1 , Wen Zhang 1,2 and Prasanga N. Samarasinghe 1 1 Audio & Acoustic Signal Processing Group, College of Engineering and Computer Science, Australian National University, Canberra 2601, Australia; [email protected] (T.D.A.); [email protected] (W.Z.); [email protected] (P.N.S.) 2 Center of Intelligent Acoustics and Immersive Communications, School of Marine Science and Technology, Northwestern Polytechnical University, Xi0an 710072, China * Correspondence: [email protected] Received: 28 February 2019; Accepted: 20 March 2019; Published: 25 March 2019 Abstract: In this paper, we investigate the maximum active noise control performance over a three-dimensional (3-D) spatial space, for a given set of secondary sources in a particular environment. We first formulate the spatial active noise control (ANC) problem in a 3-D room. Then we discuss a wave-domain least squares method by matching the secondary noise field to the primary noise field in the wave domain. Furthermore, we extract the subspace from wave-domain coefficients of the secondary paths and propose a subspace method by matching the secondary noise field to the projection of primary noise field in the subspace. Simulation results demonstrate the effectiveness of the proposed algorithms by comparison between the wave-domain least squares method and the subspace method, more specifically the energy of the loudspeaker driving signals, noise reduction inside the region, and residual noise field outside the region. We also investigate the ANC performance under different loudspeaker configurations and noise source positions. -
Information Theory
Information Theory Professor John Daugman University of Cambridge Computer Science Tripos, Part II Michaelmas Term 2016/17 H(X,Y) I(X;Y) H(X|Y) H(Y|X) H(X) H(Y) 1 / 149 Outline of Lectures 1. Foundations: probability, uncertainty, information. 2. Entropies defined, and why they are measures of information. 3. Source coding theorem; prefix, variable-, and fixed-length codes. 4. Discrete channel properties, noise, and channel capacity. 5. Spectral properties of continuous-time signals and channels. 6. Continuous information; density; noisy channel coding theorem. 7. Signal coding and transmission schemes using Fourier theorems. 8. The quantised degrees-of-freedom in a continuous signal. 9. Gabor-Heisenberg-Weyl uncertainty relation. Optimal \Logons". 10. Data compression codes and protocols. 11. Kolmogorov complexity. Minimal description length. 12. Applications of information theory in other sciences. Reference book (*) Cover, T. & Thomas, J. Elements of Information Theory (second edition). Wiley-Interscience, 2006 2 / 149 Overview: what is information theory? Key idea: The movements and transformations of information, just like those of a fluid, are constrained by mathematical and physical laws. These laws have deep connections with: I probability theory, statistics, and combinatorics I thermodynamics (statistical physics) I spectral analysis, Fourier (and other) transforms I sampling theory, prediction, estimation theory I electrical engineering (bandwidth; signal-to-noise ratio) I complexity theory (minimal description length) I signal processing, representation, compressibility As such, information theory addresses and answers the two fundamental questions which limit all data encoding and communication systems: 1. What is the ultimate data compression? (answer: the entropy of the data, H, is its compression limit.) 2.