Implications of Increasing Man Made Noise Floor Levels on Radio Broadcasting
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Autoencoding Neural Networks As Musical Audio Synthesizers
Proceedings of the 21st International Conference on Digital Audio Effects (DAFx-18), Aveiro, Portugal, September 4–8, 2018 AUTOENCODING NEURAL NETWORKS AS MUSICAL AUDIO SYNTHESIZERS Joseph Colonel Christopher Curro Sam Keene The Cooper Union for the Advancement of The Cooper Union for the Advancement of The Cooper Union for the Advancement of Science and Art Science and Art Science and Art NYC, New York, USA NYC, New York, USA NYC, New York, USA [email protected] [email protected] [email protected] ABSTRACT This training corpus consists of five-octave C Major scales on var- ious synthesizer patches. Once training is complete, we bypass A method for musical audio synthesis using autoencoding neural the encoder and directly activate the smallest hidden layer of the networks is proposed. The autoencoder is trained to compress and autoencoder. This activation produces a magnitude STFT frame reconstruct magnitude short-time Fourier transform frames. The at the output. Once several frames are produced, phase gradient autoencoder produces a spectrogram by activating its smallest hid- integration is used to construct a phase response for the magnitude den layer, and a phase response is calculated using real-time phase STFT. Finally, an inverse STFT is performed to synthesize audio. gradient heap integration. Taking an inverse short-time Fourier This model is easy to train when compared to other state-of-the-art transform produces the audio signal. Our algorithm is light-weight methods, allowing for musicians to have full control over the tool. when compared to current state-of-the-art audio-producing ma- This paper presents improvements over the methods outlined chine learning algorithms. -
Vertical Magnetic Noise in the Voice Frequency Band Within and Above Coal Mines
Report of Investigations 8828 Vertical Magnetic Noise in the Voice Frequency Band Within and Above Coal Mines By John Durkin UNITED STATES DEPARTMENT OF THE INTERIOR William P. Clark, Secretary BUREAU OF MINES Robert C. Horton, Director Library of Congress Cataloging in Publication Data: Durkin, John Vertical magnetic noise in the voice frequency band within and above coal mines. (Report of investigations ; 8828) Bibliography: p. 21-22. Supt. of Docs. no.: I 28.23:8828. 1. Coal mines and mining-Communication systems. 2. Electromag- netic noise. 3. Voice frequency. I. Title. 11. Series: Report of in- vestigations (United States. Bureau of Mines) ; 8828. TN23.U43 [TN344] 622s [622] 83-600 164 CONTENTS Page Abstract ....................................................................... 1 Introduction ................................................................. 2 EM atmospheric noise ........................................................... 2 Noise source ................................................................. 2 Earth-ionosphere waveguide ................................................... 5 Prior atmospheric noise measurements ......................................... 8 Surface vertical magnetic noise .......................................... 9 Underground vertical magnetic noise ...................................... 11 EM vertical magnetic noise field tests ......................................... 12 Noise measuring instrumentation.............................................. 12 Noise data .................................................................. -
Demodulation, Bit-Error Probability and Diversity Arrangements
RADIO SYSTEMS – ETIN15 Lecture no: 6 Demodulation, bit-error probability and diversity arrangements Anders J Johansson, Department of Electrical and Information Technology [email protected] 5 April 2017 1 Contents • Receiver noise calculations [Covered briefly in Chapter 3 of textbook!] • Optimal receiver and bit error probability – Principle of maximum-likelihood receiver – Error probabilities in non-fading channels – Error probabilities in fading channels • Diversity arrangements – The diversity principle – Types of diversity – Spatial (antenna) diversity performance 5 April 2017 2 RECEIVER NOISE 5 April 2017 3 Receiver noise Noise sources The noise situation in a receiver depends on several noise sources Noise picked up Wanted by the antenna signal Output signal Analog Detector with requirement circuits on quality Thermal noise 5 April 2017 4 Receiver noise Equivalent noise source To simplify the situation, we replace all noise sources with a single equivalent noise source. Wanted How do we determine Noise free signal N from the other sources? N Output signal C Analog Detector with requirement circuits on quality Noise free Same “input quality”, signal-to-noise ratio, C/N in the whole chain. 5 April 2017 5 Receiver noise Examples • Thermal noise is caused by random movements of electrons in circuits. It is assumed to be Gaussian and the power is proportional to the temperature of the material, in Kelvin. • Atmospheric noise is caused by electrical activity in the atmosphere, e.g. lightning. This noise is impulsive in its nature and below 20 MHz it is a dominating. • Cosmic noise is caused by radiation from space and the sun is a major contributor. -
Application Note Template
correction. of TOI measurements with noise performance the improved show examples Measurement presented. is correction a noise of means by improvements range Dynamic explained. are analyzer spectrum of a factors the limiting correction. The basic requirements and noise with measurements spectral about This application note provides information | | | Products: Note Application Correction Noise with Range Dynamic Improved R&S R&S R&S FSQ FSU FSG Application Note Kay-Uwe Sander Nov. 2010-1EF76_0E Table of Contents Table of Contents 1 Overview ................................................................................. 3 2 Dynamic Range Limitations .................................................. 3 3 Signal Processing - Noise Correction .................................. 4 3.1 Evaluation of the noise level.......................................................................4 3.2 Details of the noise correction....................................................................5 4 Measurement Examples ........................................................ 7 4.1 Extended dynamic range with noise correction .......................................7 4.2 Low level measurements on noise-like signals ........................................8 4.3 Measurements at the theoretical limits....................................................10 5 Literature............................................................................... 11 6 Ordering Information ........................................................... 11 1EF76_0E Rohde & Schwarz -
Reliability of Atmospheric Radio Noise Predictions L
JOURNAL OF RESEARCH of the National Bureau of Standards- D. Radio Propagation Vol. 650 , No.6, November- December 1961 Reliability of Atmospheric Radio Noise Predictions l John R. Herman Contribution from AVCO Corporation, Research and Advanced Development Division, Geophysics Section, 201 Lowell Street, Wilmington, Mass. (Received May 15, 1961) Measured radio noise values are compared with the corresponding International Radio Consultative Committee [C.C.I.R., 1957) predicted values at four noise measuring stations. Five freq uencies between 0.013 and 10.0 Mc/s are considered. The stations selected for this ~t ud y include Balboa, Panama, near t wo major radio noise centers, and Byrd Station, Antarctic, remote from. atmosp heric radio noise sources. It is fouod that the predicted and measured noise levels are in good agreement except at some places and times, where large discrepancies occur. :Most of the disagreements are found at places where the predi ctions are based on extrapolations of data measured at other stations. R easons for the disagree men ts are disc u sed. 1. Introduction Becausc the success or failure of communications by radio waves depends upon the signal-to-noise i ratio at the receiver, it has become necessary to obtain some knowledge of the noise levels to be expected. Atmospherically-generated radio noise levels vary with time of day, scason, and geographi cal lo cation. Prediction of tbe variations on a worldwide basis have been published in Radio Propagation Unit (R.P.U.) Technical R eport No.5 [1945 1947] and National Bureau of Standards Circular 462 [1948] in terms of t he minimum field stl' e ngth~ r ~ quir e ~ to maintain int ell i~ibl e. -
Signal to Noise Ratio (SNR Or S/N) SNR Is the Ratio of the Signal Power to Noise Power
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY KAKINADA KAKINADA – 533 001 , ANDHRA PRADESH GATE Coaching Classes as per the Direction of Ministry of Education GOVERNMENT OF ANDHRA PRADESH Analog Communication 26-05-2020 to 06-07-2020 Prof. Ch. Srinivasa Rao Dept. of ECE, JNTUK-UCE Vizianagaram Analog Communication-Day 7, 01-06-2020 Presentation Outline Noise: • Noise and its types • Thermal Noise • Parameters of Noise • Noise in Baseband Communication systems • Gaussian process and NB Noise • Problems 02-06-2020 Prof.Ch.Srinivasa Rao, JNTUK UCEV 2 Learning Outcomes • At the end of this Session, Student will be able to: • LO 1 : Fundamental aspects of Noise • LO 2 : Different Noise types and its Paraneters • LO 3 : Gaussian Process and ND Noise 02-06-2020 Prof.Ch.Srinivasa Rao, JNTUK UCEV 3 Noise Definitions of Noise: • Noise is unwanted signal that affects wanted signal. • Noise can broadly be defined as any unknown signal that affects the recovery of the desired signal. • Noise is an unwanted signal, which interferes with the original message signal and corrupts the parameters of the message signal. This alteration in the communication process, leads to the message getting altered. It most likely enters at the channel or the receiver. Effect of noise • Degrades system performance (Analog and digital) • Receiver cannot distinguish signal from noise • Efficiency of communication system reduces Most common examples of noise are: • Hiss sound in radio receivers • Buzz sound amidst of telephone conversations • Flicker in television receivers, Noise Sources External Internal Noise Noise Atmospheric Industrial Extraterrestrial Solar Noise Cosmic Noise External Noise : • It is due to Man- made and natural resources • Sources over which we have no control such as thunders, snow fall, lightning etc. -
Receiver Sensitivity and Equivalent Noise Bandwidth Receiver Sensitivity and Equivalent Noise Bandwidth
11/08/2016 Receiver Sensitivity and Equivalent Noise Bandwidth Receiver Sensitivity and Equivalent Noise Bandwidth Parent Category: 2014 HFE By Dennis Layne Introduction Receivers often contain narrow bandpass hardware filters as well as narrow lowpass filters implemented in digital signal processing (DSP). The equivalent noise bandwidth (ENBW) is a way to understand the noise floor that is present in these filters. To predict the sensitivity of a receiver design it is critical to understand noise including ENBW. This paper will cover each of the building block characteristics used to calculate receiver sensitivity and then put them together to make the calculation. Receiver Sensitivity Receiver sensitivity is a measure of the ability of a receiver to demodulate and get information from a weak signal. We quantify sensitivity as the lowest signal power level from which we can get useful information. In an Analog FM system the standard figure of merit for usable information is SINAD, a ratio of demodulated audio signal to noise. In digital systems receive signal quality is measured by calculating the ratio of bits received that are wrong to the total number of bits received. This is called Bit Error Rate (BER). Most Land Mobile radio systems use one of these figures of merit to quantify sensitivity. To measure sensitivity, we apply a desired signal and reduce the signal power until the quality threshold is met. SINAD SINAD is a term used for the Signal to Noise and Distortion ratio and is a type of audio signal to noise ratio. In an analog FM system, demodulated audio signal to noise ratio is an indication of RF signal quality. -
Rolling Bearings Fault Diagnosis and Classi Cation Based on Optimal Wavelet Subband Selection and Deep Neural Network
Rolling bearings fault diagnosis and classication based on optimal wavelet subband selection and deep neural network Rakesh Kumar Jha ( [email protected] ) Rajiv Gandhi Proudyogiki Vishwavidyalaya https://orcid.org/0000-0002-6568-8083 Preety D Swami Rajiv Gandhi Proudyogiki Vishwavidyalaya Research Article Keywords: Autoencoder, fault diagnosis, Shannon entropy, softmax classier, stationary wavelet transform. Posted Date: March 23rd, 2021 DOI: https://doi.org/10.21203/rs.3.rs-342393/v1 License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License Rolling bearings fault diagnosis and classification based on optimal wavelet subband selection and deep neural network Rakesh Kumar Jha1, a *, Preety D Swami1,b 1 Department of Electronics & Communication Engineering, RGPV, Bhopal- 462033, India *a email-address: [email protected] b email-address: [email protected] Abstract Time-frequency analysis plays a vital role in fault diagnosis of nonstationary vibration signals acquired from mechanical systems. However, the practical applications face the challenges of continuous variation in speed and load. Apart from this, the disturbances introduced by noise are inevitable. This paper aims to develop a robust method for fault identification in bearings under varying speed, load and noisy conditions. An Optimal Wavelet Subband Deep Neural Network (OWS-DNN) technique is proposed that automatically extracts features from an optimal wavelet subband selected on the basis of Shannon entropy. After denoising the optimal subband, the optimal subbands are dimensionally reduced by the encoder section of an autoencoder. The output of the encoder can be considered as data features. Finally, softmax classifier is employed to classify the encoder output. -
AN10062 Phase Noise Measurement Guide for Oscillators
Phase Noise Measurement Guide for Oscillators Contents 1 Introduction ............................................................................................................................................. 1 2 What is phase noise ................................................................................................................................. 2 3 Methods of phase noise measurement ................................................................................................... 3 4 Connecting the signal to a phase noise analyzer ..................................................................................... 4 4.1 Signal level and thermal noise ......................................................................................................... 4 4.2 Active amplifiers and probes ........................................................................................................... 4 4.3 Oscillator output signal types .......................................................................................................... 5 4.3.1 Single ended LVCMOS ........................................................................................................... 5 4.3.2 Single ended Clipped Sine ..................................................................................................... 5 4.3.3 Differential outputs ............................................................................................................... 6 5 Setting up a phase noise analyzer ........................................................................................................... -
Preview Only
AES-6id-2006 (r2011) AES information document for digital audio - Personal computer audio quality measurements Published by Audio Engineering Society, Inc. Copyright © 2006 by the Audio Engineering Society Preview only Abstract This document focuses on the measurement of audio quality specifications in a PC environment. Each specification listed has a definition and an example measurement technique. Also included is a detailed description of example test setups to measure each specification. An AES standard implies a consensus of those directly and materially affected by its scope and provisions and is intended as a guide to aid the manufacturer, the consumer, and the general public. An AES information document is a form of standard containing a summary of scientific and technical information; originated by a technically competent writing group; important to the preparation and justification of an AES standard or to the understanding and application of such information to a specific technical subject. An AES information document implies the same consensus as an AES standard. However, dissenting comments may be published with the document. The existence of an AES standard or AES information document does not in any respect preclude anyone, whether or not he or she has approved the document, from manufacturing, marketing, purchasing, or using products, processes, or procedures not conforming to the standard. Attention is drawn to the possibility that some of the elements of this AES standard or information document may be the subject of patent rights. AES shall not be held responsible for identifying any or all such patents. This document is subject to periodicwww.aes.org/standards review and users are cautioned to obtain the latest edition and printing. -
Noise Reduction by Cross-Correlation
Application Note Noise reduction by parallel Zurich cross-correlation measurements Instruments Applications: Nanoelectronics, Materials Characterization, Low-Temperature Physics, Transport Measurements, DC SQUID Products: MFLI, MFLI-DIG, MDS Release date: May 2019 Introduction 4 10 Studying noise processes can reveal fundamental infor- mation about a physical system; for example, its tem- perature [1, 2, 3],the quantization of charge carriers [4], 3 10 or the nature of many-body behavior [5]. Noise mea- surements are also critical for applications such as sensing, where the intrinsic noise of a detector defines its ultimate sensitivity. In this note, we will describe 100 a generic method for measuring the electronic noise of a device when the spectral density of its intrinsic noise is less than that of the measurement apparatus. 10 We implement the method using a pair of MFLI Lock-in Amplifiers, both with the Digitizer option MF-DIG. To demonstrate the measurement, we measure the 1 noise of a Superconducting Quantum Interference De- 100 101 102 103 104 105 106 vice (SQUID) magnetometer. Typically, SQUIDs are op- erated by supplying a DC bias current and measuring Figure 1. The voltage noise density at the Voltage Input of an MFLI a voltage and, as such, the limiting factor in the mea- Lock-in Amplifier plotted for available Input ranges [6]. surement is usually the noise floor of the voltage pre- amplifier. For a SQUID with gain (transfer function)p VΦ = 1 mV/Φ0 and targetp sensitivity of S = 1 μΦ0/ Hz, larly at low frequencies. One way of resolving signals a noise floor of 1 nV/ Hz is required. -
Uncovering Interference at a Co-Located 800 Mhz P25 Public Safety Communication System Case Study
Case Study Uncovering Interference at a Co-Located 800 MHz P25 Public Safety Communication System RTSA Spectrum Analyzer Key to Uncovering Hard-to-Find Cause Overview RF communication systems have become a critical part of modern life. These systems range from tiny Bluetooth® devices to nationwide, high-speed data systems to public safety critical infrastructure. Because these communication systems share common RF spectrum, interference between them is inevitable. Interference between systems can range from intermittent outages that can cause dropped calls to complete blockage of communication. Resolving these issues requires testing of the system with a test RF receiver or spectrum analyzer to detect and locate the source of interference within the system. 1 Challenge A public safety organization in northern Nevada was experiencing intermittent receive interference alarms on its 800 MHz P25 public safety communications system. An ordinary swept-tuned spectrum analyzer was used in an attempt to locate the interfering signals, however, none were found. After determining it was not a software issue, it was decided that the cause of the issue was likely to be an interfering signal with short duration or even a hopping signal that was being missed by the spectrum analyzer. A spectrum analyzer with real-time spectrum analysis (RTSA) was needed to be utilized to look for any interfering signals. Solution Anritsu’s Field Master Pro™ MS2090A solution with real-time spectrum analyzer option was selected to locate the interfering signal (Figure 1). The Field Master Pro MS2090A has an optional real-time mode that can accurately measure the amplitude of a single spectrum event as short as 2 µs, and detect a single event as short as 5 ns.