TRANSCOM Proceedings, Section 3
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Improvement of Bit Error Rate in Fiber Optic Communications
International Journal of Future Computer and Communication, Vol. 3, No. 4, August 2014 Improvement of Bit Error Rate in Fiber Optic Communications Jahangir Alam S. M., M. Rabiul Alam, Hu Guoqing, and Md. Zakirul Mehrab Abstract—The bit error rate (BER) is the percentage of bits II. BIT ERROR RATE AND SIGNAL-TO-NOISE RATIO that have errors relative to the total number of bits received in a transmission. The different modulation techniques scheme is In telecommunication transmission, the bit error rate (BER) suggested for improvement of BER in fiber optic is the percentage of bits that have errors relative to the total communications. The developed scheme has been tested on number of bits received in a transmission. For example, a optical fiber systems operating with a non-return-to-zero (NRZ) transmission might have a BER of 10-6, meaning that, out of format at transmission rates of up to 10Gbps. Numerical 1,000,000 bits transmitted, one bit was in error. The BER is simulations have shown a noticeable improvement of the system an indication of how often data has to be retransmitted BER after optimization of the suggested processing operation on the detected electrical signals at central wavelengths in the because of an error [1]. Too high a BER may indicate that a region of 1310 nm. slower data rate would actually improve overall transmission time for a given amount of transmitted data since the BER Index Terms—BER, modulation techniques, SNR, NRZ, might be reduced, lowering the number of packets that had to improvement. be present. -
Relating Optical Speech to Speech Acoustics and Visual Speech Perception
UNIVERSITY OF CALIFORNIA Los Angeles Relating Optical Speech to Speech Acoustics and Visual Speech Perception A dissertation submitted in partial satisfaction of the requirements for the degree Doctor of Philosophy in Electrical Engineering by Jintao Jiang 2003 i © Copyright by Jintao Jiang 2003 ii The dissertation of Jintao Jiang is approved. Kung Yao Lieven Vandenberghe Patricia A. Keating Lynne E. Bernstein Abeer Alwan, Committee Chair University of California, Los Angeles 2003 ii Table of Contents Chapter 1. Introduction ............................................................................................... 1 1.1. Audio-Visual Speech Processing ............................................................................ 1 1.2. How to Examine the Relationship between Data Sets ............................................ 4 1.3. The Relationship between Articulatory Movements and Speech Acoustics........... 5 1.4. The Relationship between Visual Speech Perception and Physical Measures ....... 9 1.5. Outline of This Dissertation .................................................................................. 15 Chapter 2. Data Collection and Pre-Processing ...................................................... 17 2.1. Introduction ........................................................................................................... 17 2.2. Background ........................................................................................................... 18 2.3. Recording a Database for the Correlation Analysis............................................. -
Applications of Machine Learning in Cable Access Networks Karthik Sundaresan, Nicolas Metts, Greg White; Albert Cabellos-Aparicio Cablelabs; UPC Barcelonatech
Applications of Machine Learning in Cable Access Networks Karthik Sundaresan, Nicolas Metts, Greg White; Albert Cabellos-Aparicio CableLabs; UPC BarcelonaTech. Abstract proven to be powerful components in the Machine Learning space. Recent advances in Machine Learning algorithms have resulted in an explosion of Potential applications for Machine real-world applications, including self-driving Learning abound in the areas of network cars, face and emotion recognition, automatic technology and applications. It can be used to image captioning, real-time speech intelligently learn the various environments of translation and drug discovery. networks and react to dynamic situations better than a fixed algorithm. When it Potential applications for Machine becomes mature, it would greatly accelerate Learning abound in the areas of network the development of autonomic networking. technology, and there are various problem areas in the cable access network which can There are various problem areas in the benefit from Machine Learning techniques. cable access network which can benefit from This paper provides an overview of Machine Machine Learning techniques. For example, Learning algorithms, and how they could be the Proactive Network Maintenance data applied to the applications of interest to cable which represents network conditions obtained operators and equipment suppliers. from CMTS and CM can be processed through Machine Learning algorithms to enable automatic recognition of issues in the cable plant and initiating the needed INTRODUCTION corrective actions. Patterns in network traffic, equipment failures, device reboots or link Recent advances in Machine Learning, and failures can be used to identify the root cause in particular, deep learning, have resulted in of various network problems. -
Improving Opus Low Bit Rate Quality with Neural Speech Synthesis
Improving Opus Low Bit Rate Quality with Neural Speech Synthesis Jan Skoglund1, Jean-Marc Valin2∗ 1Google, San Francisco, CA, USA 2Amazon, Palo Alto, CA, USA [email protected], [email protected] Abstract learned representation set [11]. A typical WaveNet configura- The voice mode of the Opus audio coder can compress wide- tion requires a very high algorithmic complexity, in the order band speech at bit rates ranging from 6 kb/s to 40 kb/s. How- of hundreds of GFLOPS, along with a high memory usage to ever, Opus is at its core a waveform matching coder, and as the hold the millions of model parameters. Combined with the high rate drops below 10 kb/s, quality degrades quickly. As the rate latency, in the hundreds of milliseconds, this renders WaveNet reduces even further, parametric coders tend to perform better impractical for a real-time implementation. Replacing the di- than waveform coders. In this paper we propose a backward- lated convolutional networks with recurrent networks improved compatible way of improving low bit rate Opus quality by re- memory efficiency in SampleRNN [12], which was shown to be synthesizing speech from the decoded parameters. We compare useful for speech coding in [13]. WaveRNN [14] also demon- two different neural generative models, WaveNet and LPCNet. strated possibilities for synthesizing at lower complexities com- WaveNet is a powerful, high-complexity, and high-latency ar- pared to WaveNet. Even lower complexity and real-time opera- chitecture that is not feasible for a practical system, yet pro- tion was recently reported using LPCNet [15]. vides a best known achievable quality with generative models. -
Lecture 16: Linear Prediction-Based Representations
Return to Main LECTURE 16: LINEAR PREDICTION-BASED REPRESENTATIONS Objectives ● Objectives: System View: Analysis/Synthesis ❍ Introduce analysis/synthesis systems The Error Signal ❍ Discuss error analysis and gain-matching Gain Matching ❍ Relate linear prediction coefficients to other spectral representations Transformations: Levinson-Durbin ❍ Introduce reflection and prediction coefficent recursions Reflection Coefficients Transformations ❍ Lattice/ladder filter implementations The Burg Method There is a classic textbook on this subject: Summary: Signal Modeling J.D. Markel and A.H. Gray, Linear Prediction of Speech, Springer-Verlag, New Typical Front End York, New York, USA, ISBN: 0-13-007444-6, 1976. On-Line Resources: This lecture also includes material from two other textbooks: AJR: Linear Prediction LPC10E MAD LPC J. Deller, et. al., Discrete-Time Processing of Speech Signals, MacMillan Publishing Co., ISBN: 0-7803-5386-2, 2000. Filter Design and, L.R. Rabiner and B.W. Juang, Fundamentals of Speech Recognition, Prentice-Hall, Upper Saddle River, New Jersey, USA, ISBN: 0-13-015157-2, 1993. Return to Main Introduction: 01: Organization (html, pdf) Speech Signals: ECE 8463: FUNDAMENTALS OF SPEECH 02: Production (html, pdf) RECOGNITION 03: Digital Models Professor Joseph Picone (html, pdf) Department of Electrical and Computer Engineering Mississippi State University 04: Perception (html, pdf) email: [email protected] phone/fax: 601-325-3149; office: 413 Simrall 05: Masking URL: http://www.isip.msstate.edu/resources/courses/ece_8463 (html, pdf) Modern speech understanding systems merge interdisciplinary technologies from Signal Processing, 06: Phonetics and Phonology Pattern Recognition, Natural Language, and Linguistics into a unified statistical framework. These (html, pdf) systems, which have applications in a wide range of signal processing problems, represent a revolution in Digital Signal Processing (DSP). -
Source-Channel Coding for CELP Speech Coders
2G'S c5 Source-Channel Coding for CELP Speech Coders J.A. Asenstorfer B.Ma.Sc. (Hons),B.Sc.(Hons),8.8',M'E' University of Adelaide Department of Electrical and Electronic Engineering Thesis submitted for the Degree of Doctor of Philosophy. Septemb er 26, L994 A*ond p.] l'ìq t Contents 1 1 Speech Coders for NoisY Channels 1.1 Introduction 1 2 I.2 Thesis Goals and Historical Perspective 2 Linear Predictive Coders Background I I 2.1 Introduction ' 10 2.2 Linear Predictive Models . 2.3 Autocorrelation of SPeech 15 17 2.4 Generic Code Excited Linear Prediction 3 Pitch Estimation Based on Chaos Theory 23 23 3.1 Introduction . 3.1.1 A Novel Approach to Pitch Estimation 25 3.2 The Non-Linear DYnamical Model 26 29 3.3 Determining the DelaY 32 3.4 Speech Database 3.5 Initial Findings 32 3.5.1 Poincaré Sections 34 35 3.5.2 Poincaré Section in Time 39 3.6 SpeechClassifrcation 40 3.6.1 The Zero Set 4l 3.6.2 The Devil's Staircase 42 3.6.3 Orbit Direction Change Indicator 45 3.7 Voicing Estimator 47 3.8 Pitch Tracking 49 3.9 The Pitch Estimation Algorithm ' 51 3.10 Results from the Pitch Estimator 55 3.11 Waveform Substitution 58 3.11.1 Replication of the Last Pitch Period 59 3.1I.2 Unvoiced Substitution 60 3.11.3 SPlicing Coders 61 3.11.4 Memory Considerations for CELP 61 3.12 Conclusion Data Problem 63 4 Vector Quantiser and the Missing 63 4.I Introduction bÐ 4.2 WhY Use Vector Quantisation? a Training Set 66 4.2.1 Generation of a Vector Quantiser Using Results 68 4.2.2 Some Useful Optimal Vector Quantisation ,7r 4.3 Heuristic Argument ùn . -
Etsi Tr 101 290 V1.3.1 (2014-07)
ETSI TR 101 290 V1.3.1 (2014-07) TECHNICAL REPORT Digital Video Broadcasting (DVB); Measurement guidelines for DVB systems 2 ETSI TR 101 290 V1.3.1 (2014-07) Reference RTR/JTC-DVB-340 Keywords broadcasting, digital, DVB, TV, video ETSI 650 Route des Lucioles F-06921 Sophia Antipolis Cedex - FRANCE Tel.: +33 4 92 94 42 00 Fax: +33 4 93 65 47 16 Siret N° 348 623 562 00017 - NAF 742 C Association à but non lucratif enregistrée à la Sous-Préfecture de Grasse (06) N° 7803/88 Important notice The present document can be downloaded from: http://www.etsi.org The present document may be made available in electronic versions and/or in print. The content of any electronic and/or print versions of the present document shall not be modified without the prior written authorization of ETSI. In case of any existing or perceived difference in contents between such versions and/or in print, the only prevailing document is the print of the Portable Document Format (PDF) version kept on a specific network drive within ETSI Secretariat. Users of the present document should be aware that the document may be subject to revision or change of status. Information on the current status of this and other ETSI documents is available at http://portal.etsi.org/tb/status/status.asp If you find errors in the present document, please send your comment to one of the following services: http://portal.etsi.org/chaircor/ETSI_support.asp Copyright Notification No part may be reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying and microfilm except as authorized by written permission of ETSI. -
22Nd International Congress on Acoustics ICA 2016
Page intentionaly left blank 22nd International Congress on Acoustics ICA 2016 PROCEEDINGS Editors: Federico Miyara Ernesto Accolti Vivian Pasch Nilda Vechiatti X Congreso Iberoamericano de Acústica XIV Congreso Argentino de Acústica XXVI Encontro da Sociedade Brasileira de Acústica 22nd International Congress on Acoustics ICA 2016 : Proceedings / Federico Miyara ... [et al.] ; compilado por Federico Miyara ; Ernesto Accolti. - 1a ed . - Gonnet : Asociación de Acústicos Argentinos, 2016. Libro digital, PDF Archivo Digital: descarga y online ISBN 978-987-24713-6-1 1. Acústica. 2. Acústica Arquitectónica. 3. Electroacústica. I. Miyara, Federico II. Miyara, Federico, comp. III. Accolti, Ernesto, comp. CDD 690.22 ISBN 978-987-24713-6-1 © Asociación de Acústicos Argentinos Hecho el depósito que marca la ley 11.723 Disclaimer: The material, information, results, opinions, and/or views in this publication, as well as the claim for authorship and originality, are the sole responsibility of the respective author(s) of each paper, not the International Commission for Acoustics, the Federación Iberoamaricana de Acústica, the Asociación de Acústicos Argentinos or any of their employees, members, authorities, or editors. Except for the cases in which it is expressly stated, the papers have not been subject to peer review. The editors have attempted to accomplish a uniform presentation for all papers and the authors have been given the opportunity to correct detected formatting non-compliances Hecho en Argentina Made in Argentina Asociación de Acústicos Argentinos, AdAA Camino Centenario y 5006, Gonnet, Buenos Aires, Argentina http://www.adaa.org.ar Proceedings of the 22th International Congress on Acoustics ICA 2016 5-9 September 2016 Catholic University of Argentina, Buenos Aires, Argentina ICA 2016 has been organised by the Ibero-american Federation of Acoustics (FIA) and the Argentinian Acousticians Association (AdAA) on behalf of the International Commission for Acoustics. -
Samia Dawood Shakir
MODELLING TALKING HUMAN FACES Thesis submitted to Cardiff University in candidature for the degree of Doctor of Philosophy. Samia Dawood Shakir School of Engineering Cardiff University 2019 ii DECLARATION This work has not been submitted in substance for any other degree or award at this or any other university or place of learning, nor is being submitted concurrently in candidature for any degree or other award. Signed . (candidate) Date . STATEMENT 1 This thesis is being submitted in partial fulfillment of the requirements for the degree of PhD. Signed . (candidate) Date . STATEMENT 2 This thesis is the result of my own independent work/investigation, except where otherwise stated, and the thesis has not been edited by a third party beyond what is permitted by Cardiff Universitys Policy on the Use of Third Party Editors by Research Degree Students. Other sources are acknowledged by explicit references. The views expressed are my own. Signed . (candidate) Date . STATEMENT 3 I hereby give consent for my thesis, if accepted, to be available online in the Universitys Open Access repository and for inter-library loan, and for the title and summary to be made available to outside organisations. Signed . (candidate) Date . ABSTRACT This thesis investigates a number of new approaches for visual speech synthesis using data-driven methods to implement a talking face. The main contributions in this thesis are the following. The ac- curacy of shared Gaussian process latent variable model (SGPLVM) built using the active appearance model (AAM) and relative spectral transform-perceptual linear prediction (RASTAPLP) features is im- proved by employing a more accurate AAM. -
Digital Quality Index (DQI) Assessing Downstream Digital Signal Quality
VIAVI Solutions Application Note Digital Quality Index (DQI) Assessing Downstream Digital Signal Quality Overview Most downstream channels are transported on digital quadrature amplitude modulation (QAM) carriers. How are intermittent transport issues detected within those downstream digital services, and are customer quality expectations being met? A patented VIAVI measurement called DQI (Digital Quality Index) concentrates on the condition of the raw information on the physical path and immediately detects intermittent issues and sustained issues within the stream. In comparison, the typical measurement used to check for these issues on a digital QAM carrier is pre and post forward error correction (FEC) bit error ratio (BER), which relies on actual errors and is relatively slow to detect many issues. Drawback of Current Methods Most digital field test equipment today incorporates the same digital hardware components that reception equipment uses. These components use numerous correction technologies to adjust the demodulated carrier correcting problems that were the result of transmission issues on the RF physical path. These technologies make the digital reception a superior quality service versus the older analog methods. Test instruments monitor these technologies to achieve a quantitative view of how hard the reception hardware is working to correct the physical path issues. The resultant measurements become an indicator of potential issues, not actual carrier quality. In some cases, the technologies can mask the issues since their intent is to correct issues before the user sees a problem. Utilizing the same technologies for test purposes is not as responsive as the old analog test measurements. What is the Goal of Field Testing? The goal of field testing is to identify network issues before services are affected and to fix the problem before the customer experiences any degradation of service. -
MODULATION ERROR RATIO by RON HRANAC
Originally appeared in the January 2007 issue of Communications Technology. MODULATION ERROR RATIO By RON HRANAC Awhile back I went target shooting with a friend. While at the range, it occurred to me that what is also known as plinking is a little like modulation error ratio (MER) used to characterize, say, the 64- and 256-QAM (quadrature amplitude modulation) digitally modulated signals we transmit to our customers. OK, before you start to wonder whether I’ve had too much coffee today, bear with me as I discuss this somewhat off-the-wall analogy. Similarities A typical target used at the range comprises a set of concentric circles printed on a piece of paper. The center of the target is called the bull’s-eye, which carries the highest point value. The further away from the bull’s-eye, the lower the assigned points. Ideally, one would always hit the bull’s-eye and get the maximum possible score. In the real world, this seldom happens. Instead, one or two shots might hit at or near the bull’s-eye, and most of the rest hit somewhere in the circles surrounding the center of the target. For a person who is a decent shot, plinking usually results in a fairly uniform “fuzzy cloud” of holes in and around the bull’s-eye. The smaller the diameter of this cloud and the closer it is to the bull’s-eye, the higher the score. Factors affecting how close to the bull’s-eye the shots land include the quality and accuracy of the firearm, type of ammunition used, weather conditions if outdoors, ambient lighting, and the distance to the target. -
DSP-Based Implementation of a Digital Radio Demodulator on the Ultra-Low Power Processor Coolflux BSP
Proyecto de Sistemas Informáticos. Curso 2009-2010. DSP-Based Implementation of a Digital Radio Demodulator on the ultra-low power processor CoolFlux BSP Authors: Elena Pérez-Toril Gracia Álvaro Martínez López Project Supervisor: Luis Piñuel Moreno Departamento de Arquitectura de Computadores y Automática. Facultad de Informática. Universidad Complutense de Madrid. Contents 1 Preliminary concepts in DAB 2 1.1 Signal Modulation . 2 1.2 Multipath Propagation . 5 1.3 QPSK (Quadrature Phase Shift Keying) . 10 1.4 FDM (Frequency Division Multiplexing) . 13 1.5 Orthogonal Frequency-Division Multiplexing (OFDM) . 14 1.6 Fast Fourier Transform (FFT) . 23 1.6.1 Overview . 23 1.6.2 Digital Implementation . 24 1.6.3 The Discrete Fourier Transform . 24 1.6.4 Computation of the DFT . 25 1.6.5 The Fast Fourier Transform . 26 1.6.6 Applications to DAB . 27 1.6.7 Complex Numbers . 28 1.6.8 Negative Frequencies . 29 1.6.9 Linearity of the transforms . 30 1.6.10 Combination of I and Q . 31 1.6.11 Addition of the Guard Interval . 33 1.7 Finite Impulse Response Filters(FIR) . 34 1.7.1 How to characterize digital FIR filters . 34 1.8 Error Measures . 36 i Contents 1.8.1 Bit Error Rate . 36 1.8.2 Modulation Error Rate . 37 1.9 Errors Detection and Correction . 38 1.9.1 Viterbi Decoder . 38 1.9.2 De-puncturing . 46 1.10 Digital Signals Processors (DSP) . 47 2 CoolFlux BSP Architecture 49 2.1 Overview . 49 2.2 Registers . 50 2.3 The Data Path .