DOCSLIB.ORG

Code Excited Linear Prediction with Multi-Pulse Codebooks

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Code Excited Linear Prediction with Multi-Pulse Codebooks CODE EXCITED LINEAR PREDICTION WIT& '.' MU~TI-PULSECODEBOOI~S __- --- - - .. .. I. Lei Zhang R.X-Sc.. Iiniversi~yof British / .A THESIS SUBMITTED IZE PARTIAL F1-LFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF in the School Engineering Science 6 I& Zhang 1997 f SIIIOS FKXSER liN1~"'ESITJ' - 3 A11 rights reserved. This work may not be - rcprodu<eed in. whofe or in part. by pltotocopg~ or other means. without the permission of the author. *-. - . T BiMiothqpmtmak ~ationalLibrary P 1*m of Canada du Cana ". l ?F.r Acquisitions and - Acquisitions 6t Bibliographig Services services bibliographiques 395 Wellingtpn Street 395, rue Wellington Ottawa OW KMON4 Onaw ON K1A ON4 Canada Cairada . ... " 4 I T 4 -. .9 Our fie Notre dfBrmce a - i * ' 1 ' C \ . -- , . 2 3 - 0 * I- *. - + * I The author has granted a non- L'auteur a accorde une licence non exclusive licence allowing the - exclusive permettant B la National Library of Canada to Bibliotheque nationale du ~aqa&de reproduce, loan, distribute or sell reprodwe, preter, dishher ou ' copies of this thesis in microform, vendre dks copies de cette these sous paper or electronic' formats. la fonne de microfiche/^, de reproduction sur papier ou sur format I elecfronique. *. F, I The author retains ownership of the L'guteur conserve la propriete du copyright in this thesis. Neither the droit d7autekqui protege cette these. thesis nor substantial extracts fiom it Ni la these ni des extraits substantiels may be printed or otherwise f #+ de celle-cipe doivent etre imprimes reproduced without the author's ou autrement reproduits sans son permission. autorisation. A APPROVAL . Name: -kei Zhang . Degree: Master, of AppliedeScience * TitIe af thesis Code Excited Linear Prediction with Multi-Pulse Code- L book s Examining Committee: Dr.. B. Gru~er,~Chai:man Dr; V. Cuperman Professor, Engineering Science, SFU Senior Supervisor Dr. J. Vaisey Assistant Professor, :Engineering Science, SFU Supervisor D1; J. Cavers fiofessdr, Engineering Science, SFI! Examiner %, May 13, 1997 Date Approved: ' Abstract - d ' f Voice compression is an important ingredient in digital cornmrtnication ancl voice stor- age systems. In the past decade. Code Excited Linear Prediction (CELP)has become - the dominant speech coding algorithm for bit rates between -I kb/s and=16 kb/s. How- ever, for rates around 4 kb/s and beloiz;, CELP loses its competitive eclge to spectral , domain coding. For many applications, an attractive approach for increasing systern + - 1 rapacity whilt maintaining good quality is to allow tthe ),it rate to vary according to the input speech charzicterist ics. The corresponding coclecs are charact crized by their B average hit rate and belong to the$lass of source-coiltrolled variable rate c~clecs. Source-controlled variable rate speech coders have been applied to digital cellular ' P communications ancl to- speech storage systems such as voice mail anel voice response equipment. Replacing the fixed-rate coders 115. variable-rate coders resdts in a signif- *_ icant increase in the Gystem capacity while maintaining the clesirecl quality of servfce. This thesis presents a variable-rate C'ELPpystern which achieves good cornulunica- tions speech quality at an average rate ofaabout3 kb/s based on a one-way converwtioti with 30% silence.. The coclec operates as a source-controlled variable rate coder with rates of 1.9 kh/s for voiced and transition souncls, :3.0 kb/s for unCoicetl sou~~clsand 6'70 b/s for silent -frames. The appropriate coding rate isaselected by analyzing ex.h input speech frame using a frame classifier. The codec uses a n~odrilardesign in'which . - * - the ge"nera1. struct t1r6 and coding algorithm is the same for all rates. A11 configw%t ior~s are based on 'the system with the highest bit-rate. The lower hit-rates are, obtainecl- * by l-arying the franie/snhfrarne sizes. using different Eodehooks for quantization. and in some cases disabling coclec components. The predominant source of quality degradation at rates around 4 kb/s or lower for C'ELP systems is. t he inadeq~iaternocleling of t he pitch lag correlation which rcsults li in noisy reconstructed speech. We aclclress the prohlenl .by usi~~gnew techr~iques * +-&* -including prediction of the +ed codebook target vector and joint optimization of the - adaptire and c&ehodk search. The prediction of the fixed codebook target ' vector is based on fixed codebook selections in previous subframes and a running * estimate for the fundamental frequency. Rpuits are presented which indicltte that the variable rate system at ail average rate of less than 3.2 kb/s, achieves better *q?~alitythan fixed ate standard codecs with rates irkhe range 4 - 1.8 kbjs on the m. P speech database tested. To C;lncc and &bo. fhc ctnt~rof nsy uwi-ld. d I would like to thank my advisor. Dr. Vladimir C'upernlan,\ or his steady support and guida ice throughout the course of this reseealso want td thank qveryone in the I speecbiab for their friendship, antl for lentlirig me a helping hand when needed. I sincerely thank my parents for taking great care of Grace, antl leaviilg me no ' chance of doirig a better joh in the future, and ai~ofor so many other things ... Finally, 8 thariks to Yubo who has seen the best and the worst sides .of me.. antl who has always* L b been a spurce of support and comfort for the past ti(o Fears. * . ... Abstract ................; ........., ................... .= ..................... 11 i T & 7 Acknowledgements .............. ; .......................................... vi .'+esL e 9 * List of 'Ia5Ps ......................................................: .-.......... x List .~fFigures ......................................................... .'. .. xii .. .* ... List of ~bbreviatiork...................................................... arll 1 Introduction ........: .............................. :................... 1 1.1 . Thesis Objectives ............................................... 2 .. 1.2 Thesis Organization .............................................. 3 2 Speech Coding .......................................................... 4 1 Iritroduction .................................................. .I . * 2.2 Siglal C'onlpression Techniques ................................... 6 2.2 Scalar Quantization ....... ............. .- . : ............. 6 2.2 Vector Quantization ......................... I. .......... -r 22 Linear Prediction in Speech C'ocling .........................- S 2.2.i ..\ltern~tiveRepresentation of LPC. C'orfficieilts .............. 12 2. Spccch C'oding Systems ........................................ 13 2.3. I Analysis-S\..nt hesis C'oders .........: ....................... 14 2.32 \Vaveform Coders ....................................... l'i \ = ,* r ? vii I 1 . , . d ! ~uiti~ulseExcited Linear Predicti 3.1 . MPLPC Algorithm ............ i 3.1.1 Multipulse Excitation Model ................................, , 21 3.1.2- MPLPC Pulse Search ...................;' .................. 23 i :3.2 Other MPLPC: Coding Techniques. ..............; .................ir 24 3.3 Regular Pulse Excitation Coding .:........... .'., ..................- 25 3.4 MPLPC based Systems ..............: ...... 1 .................. 27 Code Excited Linear Prediction ................ .,I.. .................. I 29 1 Introduction .................................................. 29 * 2 Excitation Coclel>ooks ...................... !. ................... 32 4.2. Stochastic Codebook ................................. .I.. :$2 4.2. Adaptive Codehook ................: .................... 33 4.3 ZIR-ZSR Decornposit ion .................................... : ... 13.1 I -1.3:1 Codebook Search ................. .' ..................... 3.5 4.1 Linear Prediction .Analysis and Quantization ...................... 117 -1.6 C'ELP Systems ......................... ;. ..................... 39 1.6.1 The DoD 4.8 kh/s Speech Coding Stanclad ................. :19 , I -1.6.:3 LD-C'ELP ....................... .I....................... -I0 Variable-Rate Speech Coding .....,.........I. ......................... 42 -54 Voice Activity Detection ............... .I. ........................ 4:3 1 - I 5.2 Active Speech C'kssification ............ :. ............ , .......... 45 I A Low-Rate Variable Rate CELP C~der.I. .......................... -19 6.1 System Overview 50 2 System Configuration 52 2 Bit Allocations ............................... .. ...... ,. 52 . 6.2.2 Voicecl/Transit ion Coding ................................. 53 t, 6.2.3 linvoicetl Coding .............. 1 ......................... 3:% 6.2. Silence Coding ..........................i .............. 3 ... 'b - ... ,P 6 .I%.' - f rC - . m v s ,,, * # 63.5 variable Rate Operatio11 ..................... - 6.3 Excitat,ion Generation and Encoding ......: ....... ., -. 2% 6.3.1 ;\I ulti-pulse ~ixedcodehook ~esi~n1 ....... 6.3.2 - Predicted Vector .................., ................... .': 55 -" 6.3.3 .Joint C'odebook Search and Gain Quantization ............... a I 6.3.4 Gain Quantizer ...................... : ..................... 59 * 6.13.5 Low Complexity Codebook. Search ......:...... 1. ........... 61 . 9 :* . 6.4 C'odec Components .............................................* 6% 6.4.1 Frame Classifier ......................................... 62 Z s I 6.4.2 LPC Analysis and Quantization ............ c ..:........... 67 + 6.4.:3 Pitch Estimation ........................................ 68 4.Adaptive C'odehook ...................................... 69 6.Fixed C'odebook ...................................
Load more

Recommended publications
  • Data Compression: Dictionary-Based Coding 2 / 37 Dictionary-Based Coding Dictionary-Based Coding
    Dictionary-based Coding already coded not yet coded search buffer look-ahead buffer cursor (N symbols) (L symbols) We know the past but cannot control it. We control the future but... Last Lecture Last Lecture: Predictive Lossless Coding Predictive Lossless Coding Simple and effective way to exploit dependencies between neighboring symbols / samples Optimal predictor: Conditional mean (requires storage of large tables) Affine and Linear Prediction Simple structure, low-complex implementation possible Optimal prediction parameters are given by solution of Yule-Walker equations Works very well for real signals (e.g., audio, images, ...) Efficient Lossless Coding for Real-World Signals Affine/linear prediction (often: block-adaptive choice of prediction parameters) Entropy coding of prediction errors (e.g., arithmetic coding) Using marginal pmf often already yields good results Can be improved by using conditional pmfs (with simple conditions) Heiko Schwarz (Freie Universität Berlin) — Data Compression: Dictionary-based Coding 2 / 37 Dictionary-based Coding Dictionary-Based Coding Coding of Text Files Very high amount of dependencies Affine prediction does not work (requires linear dependencies) Higher-order conditional coding should work well, but is way to complex (memory) Alternative: Do not code single characters, but words or phrases Example: English Texts Oxford English Dictionary lists less than 230 000 words (including obsolete words) On average, a word contains about 6 characters Average codeword length per character would be limited by 1
    [Show full text]
  • Video Codec Requirements and Evaluation Methodology
    Video Codec Requirements 47pt 30pt and Evaluation Methodology Color::white : LT Medium Font to be used by customers and : Arial www.huawei.com draft-filippov-netvc-requirements-01 Alexey Filippov, Huawei Technologies 35pt Contents Font to be used by customers and partners : • An overview of applications • Requirements 18pt • Evaluation methodology Font to be used by customers • Conclusions and partners : Slide 2 Page 2 35pt Applications Font to be used by customers and partners : • Internet Protocol Television (IPTV) • Video conferencing 18pt • Video sharing Font to be used by customers • Screencasting and partners : • Game streaming • Video monitoring / surveillance Slide 3 35pt Internet Protocol Television (IPTV) Font to be used by customers and partners : • Basic requirements: . Random access to pictures 18pt Random Access Period (RAP) should be kept small enough (approximately, 1-15 seconds); Font to be used by customers . Temporal (frame-rate) scalability; and partners : . Error robustness • Optional requirements: . resolution and quality (SNR) scalability Slide 4 35pt Internet Protocol Television (IPTV) Font to be used by customers and partners : Resolution Frame-rate, fps Picture access mode 2160p (4K),3840x2160 60 RA 18pt 1080p, 1920x1080 24, 50, 60 RA 1080i, 1920x1080 30 (60 fields per second) RA Font to be used by customers and partners : 720p, 1280x720 50, 60 RA 576p (EDTV), 720x576 25, 50 RA 576i (SDTV), 720x576 25, 30 RA 480p (EDTV), 720x480 50, 60 RA 480i (SDTV), 720x480 25, 30 RA Slide 5 35pt Video conferencing Font to be used by customers and partners : • Basic requirements: . Delay should be kept as low as possible 18pt The preferable and maximum delay values should be less than 100 ms and 350 ms, respectively Font to be used by customers .
    [Show full text]
  • Digital Speech Processing— Lecture 17
    Digital Speech Processing— Lecture 17 Speech Coding Methods Based on Speech Models 1 Waveform Coding versus Block Processing • Waveform coding – sample-by-sample matching of waveforms – coding quality measured using SNR • Source modeling (block processing) – block processing of signal => vector of outputs every block – overlapped blocks Block 1 Block 2 Block 3 2 Model-Based Speech Coding • we’ve carried waveform coding based on optimizing and maximizing SNR about as far as possible – achieved bit rate reductions on the order of 4:1 (i.e., from 128 Kbps PCM to 32 Kbps ADPCM) at the same time achieving toll quality SNR for telephone-bandwidth speech • to lower bit rate further without reducing speech quality, we need to exploit features of the speech production model, including: – source modeling – spectrum modeling – use of codebook methods for coding efficiency • we also need a new way of comparing performance of different waveform and model-based coding methods – an objective measure, like SNR, isn’t an appropriate measure for model- based coders since they operate on blocks of speech and don’t follow the waveform on a sample-by-sample basis – new subjective measures need to be used that measure user-perceived quality, intelligibility, and robustness to multiple factors 3 Topics Covered in this Lecture • Enhancements for ADPCM Coders – pitch prediction – noise shaping • Analysis-by-Synthesis Speech Coders – multipulse linear prediction coder (MPLPC) – code-excited linear prediction (CELP) • Open-Loop Speech Coders – two-state excitation
    [Show full text]
  • Audio Coding for Digital Broadcasting
    Recommendation ITU-R BS.1196-7 (01/2019) Audio coding for digital broadcasting BS Series Broadcasting service (sound) ii Rec. ITU-R BS.1196-7 Foreword The role of the Radiocommunication Sector is to ensure the rational, equitable, efficient and economical use of the radio- frequency spectrum by all radiocommunication services, including satellite services, and carry out studies without limit of frequency range on the basis of which Recommendations are adopted. The regulatory and policy functions of the Radiocommunication Sector are performed by World and Regional Radiocommunication Conferences and Radiocommunication Assemblies supported by Study Groups. Policy on Intellectual Property Right (IPR) ITU-R policy on IPR is described in the Common Patent Policy for ITU-T/ITU-R/ISO/IEC referenced in Resolution ITU-R 1. Forms to be used for the submission of patent statements and licensing declarations by patent holders are available from http://www.itu.int/ITU-R/go/patents/en where the Guidelines for Implementation of the Common Patent Policy for ITU-T/ITU-R/ISO/IEC and the ITU-R patent information database can also be found. Series of ITU-R Recommendations (Also available online at http://www.itu.int/publ/R-REC/en) Series Title BO Satellite delivery BR Recording for production, archival and play-out; film for television BS Broadcasting service (sound) BT Broadcasting service (television) F Fixed service M Mobile, radiodetermination, amateur and related satellite services P Radiowave propagation RA Radio astronomy RS Remote sensing systems S Fixed-satellite service SA Space applications and meteorology SF Frequency sharing and coordination between fixed-satellite and fixed service systems SM Spectrum management SNG Satellite news gathering TF Time signals and frequency standards emissions V Vocabulary and related subjects Note: This ITU-R Recommendation was approved in English under the procedure detailed in Resolution ITU-R 1.
    [Show full text]
  • 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.............................................
    [Show full text]
  • (A/V Codecs) REDCODE RAW (.R3D) ARRIRAW
    What is a Codec? Codec is a portmanteau of either "Compressor-Decompressor" or "Coder-Decoder," which describes a device or program capable of performing transformations on a data stream or signal. Codecs encode a stream or signal for transmission, storage or encryption and decode it for viewing or editing. Codecs are often used in videoconferencing and streaming media solutions. A video codec converts analog video signals from a video camera into digital signals for transmission. It then converts the digital signals back to analog for display. An audio codec converts analog audio signals from a microphone into digital signals for transmission. It then converts the digital signals back to analog for playing. The raw encoded form of audio and video data is often called essence, to distinguish it from the metadata information that together make up the information content of the stream and any "wrapper" data that is then added to aid access to or improve the robustness of the stream. Most codecs are lossy, in order to get a reasonably small file size. There are lossless codecs as well, but for most purposes the almost imperceptible increase in quality is not worth the considerable increase in data size. The main exception is if the data will undergo more processing in the future, in which case the repeated lossy encoding would damage the eventual quality too much. Many multimedia data streams need to contain both audio and video data, and often some form of metadata that permits synchronization of the audio and video. Each of these three streams may be handled by different programs, processes, or hardware; but for the multimedia data stream to be useful in stored or transmitted form, they must be encapsulated together in a container format.
    [Show full text]
  • Image Compression Using Discrete Cosine Transform Method
    Qusay Kanaan Kadhim, International Journal of Computer Science and Mobile Computing, Vol.5 Issue.9, September- 2016, pg. 186-192 Available Online at www.ijcsmc.com International Journal of Computer Science and Mobile Computing A Monthly Journal of Computer Science and Information Technology ISSN 2320–088X IMPACT FACTOR: 5.258 IJCSMC, Vol. 5, Issue. 9, September 2016, pg.186 – 192 Image Compression Using Discrete Cosine Transform Method Qusay Kanaan Kadhim Al-Yarmook University College / Computer Science Department, Iraq [email protected] ABSTRACT: The processing of digital images took a wide importance in the knowledge field in the last decades ago due to the rapid development in the communication techniques and the need to find and develop methods assist in enhancing and exploiting the image information. The field of digital images compression becomes an important field of digital images processing fields due to the need to exploit the available storage space as much as possible and reduce the time required to transmit the image. Baseline JPEG Standard technique is used in compression of images with 8-bit color depth. Basically, this scheme consists of seven operations which are the sampling, the partitioning, the transform, the quantization, the entropy coding and Huffman coding. First, the sampling process is used to reduce the size of the image and the number bits required to represent it. Next, the partitioning process is applied to the image to get (8×8) image block. Then, the discrete cosine transform is used to transform the image block data from spatial domain to frequency domain to make the data easy to process.
    [Show full text]
  • MC14SM5567 PCM Codec-Filter
    Product Preview Freescale Semiconductor, Inc. MC14SM5567/D Rev. 0, 4/2002 MC14SM5567 PCM Codec-Filter The MC14SM5567 is a per channel PCM Codec-Filter, designed to operate in both synchronous and asynchronous applications. This device 20 performs the voice digitization and reconstruction as well as the band 1 limiting and smoothing required for (A-Law) PCM systems. DW SUFFIX This device has an input operational amplifier whose output is the input SOG PACKAGE CASE 751D to the encoder section. The encoder section immediately low-pass filters the analog signal with an RC filter to eliminate very-high-frequency noise from being modulated down to the pass band by the switched capacitor filter. From the active R-C filter, the analog signal is converted to a differential signal. From this point, all analog signal processing is done differentially. This allows processing of an analog signal that is twice the amplitude allowed by a single-ended design, which reduces the significance of noise to both the inverted and non-inverted signal paths. Another advantage of this differential design is that noise injected via the power supplies is a common mode signal that is cancelled when the inverted and non-inverted signals are recombined. This dramatically improves the power supply rejection ratio. After the differential converter, a differential switched capacitor filter band passes the analog signal from 200 Hz to 3400 Hz before the signal is digitized by the differential compressing A/D converter. The decoder accepts PCM data and expands it using a differential D/A converter. The output of the D/A is low-pass filtered at 3400 Hz and sinX/X compensated by a differential switched capacitor filter.
    [Show full text]
  • TMS320C6000 U-Law and A-Law Companding with Software Or The
    Application Report SPRA634 - April 2000 TMS320C6000t µ-Law and A-Law Companding with Software or the McBSP Mark A. Castellano Digital Signal Processing Solutions Todd Hiers Rebecca Ma ABSTRACT This document describes how to perform data companding with the TMS320C6000 digital signal processors (DSP). Companding refers to the compression and expansion of transfer data before and after transmission, respectively. The multichannel buffered serial port (McBSP) in the TMS320C6000 supports two companding formats: µ-Law and A-Law. Both companding formats are specified in the CCITT G.711 recommendation [1]. This application report discusses how to use the McBSP to perform data companding in both the µ-Law and A-Law formats. This document also discusses how to perform companding of data not coming into the device but instead located in a buffer in processor memory. Sample McBSP setup codes are provided for reference. In addition, this application report discusses µ-Law and A-Law companding in software. The appendix provides software companding codes and a brief discussion on the companding theory. Contents 1 Design Problem . 2 2 Overview . 2 3 Companding With the McBSP. 3 3.1 µ-Law Companding . 3 3.1.1 McBSP Register Configuration for µ-Law Companding. 4 3.2 A-Law Companding. 4 3.2.1 McBSP Register Configuration for A-Law Companding. 4 3.3 Companding Internal Data. 5 3.3.1 Non-DLB Mode. 5 3.3.2 DLB Mode . 6 3.4 Sample C Functions. 6 4 Companding With Software. 6 5 Conclusion . 7 6 References . 7 TMS320C6000 is a trademark of Texas Instruments Incorporated.
    [Show full text]
  • Lossless Compression of Audio Data
    CHAPTER 12 Lossless Compression of Audio Data ROBERT C. MAHER OVERVIEW Lossless data compression of digital audio signals is useful when it is necessary to minimize the storage space or transmission bandwidth of audio data while still maintaining archival quality. Available techniques for lossless audio compression, or lossless audio packing, generally employ an adaptive waveform predictor with a variable-rate entropy coding of the residual, such as Huffman or Golomb-Rice coding. The amount of data compression can vary considerably from one audio waveform to another, but ratios of less than 3 are typical. Several freeware, shareware, and proprietary commercial lossless audio packing programs are available. 12.1 INTRODUCTION The Internet is increasingly being used as a means to deliver audio content to end-users for en­ tertainment, education, and commerce. It is clearly advantageous to minimize the time required to download an audio data file and the storage capacity required to hold it. Moreover, the expec­ tations of end-users with regard to signal quality, number of audio channels, meta-data such as song lyrics, and similar additional features provide incentives to compress the audio data. 12.1.1 Background In the past decade there have been significant breakthroughs in audio data compression using lossy perceptual coding [1]. These techniques lower the bit rate required to represent the signal by establishing perceptual error criteria, meaning that a model of human hearing perception is Copyright 2003. Elsevier Science (USA). 255 AU rights reserved. 256 PART III / APPLICATIONS used to guide the elimination of excess bits that can be either reconstructed (redundancy in the signal) orignored (inaudible components in the signal).
    [Show full text]
  • 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.
    [Show full text]
  • Bit Rate Adaptation Using Linear Quadratic Optimization for Mobile Video Streaming
    applied sciences Article Bit Rate Adaptation Using Linear Quadratic Optimization for Mobile Video Streaming Young-myoung Kang 1 and Yeon-sup Lim 2,* 1 Network Business, Samsung Electronics, Suwon 16677, Korea; [email protected] 2 Department of Convergence Security Engineering, Sungshin Women’s University, Seoul 02844, Korea * Correspondence: [email protected]; Tel.: +82-2-920-7144 Abstract: Video streaming application such as Youtube is one of the most popular mobile applications. To adjust the quality of video for available network bandwidth, a streaming server provides multiple representations of video of which bit rate has different bandwidth requirements. A streaming client utilizes an adaptive bit rate (ABR) scheme to select a proper representation that the network can support. However, in mobile environments, incorrect decisions of an ABR scheme often cause playback stalls that significantly degrade the quality of user experience, which can easily happen due to network dynamics. In this work, we propose a control theory (Linear Quadratic Optimization)- based ABR scheme to enhance the quality of experience in mobile video streaming. Our simulation study shows that our proposed ABR scheme successfully mitigates and shortens playback stalls while preserving the similar quality of streaming video compared to the state-of-the-art ABR schemes. Keywords: dynamic adaptive streaming over HTTP; adaptive bit rate scheme; control theory 1. Introduction Video streaming constitutes a large fraction of current Internet traffic. In particular, video streaming accounts for more than 65% of world-wide mobile downstream traffic [1]. Commercial streaming services such as YouTube and Netflix are implemented based on Citation: Kang, Y.-m.; Lim, Y.-s.
    [Show full text]