Embedded Lossless Audio Coding Using Linear Prediction and Cascade
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An Introduction to Mpeg-4 Audio Lossless Coding
« ¬ AN INTRODUCTION TO MPEG-4 AUDIO LOSSLESS CODING Tilman Liebchen Technical University of Berlin ABSTRACT encoding process has to be perfectly reversible without loss of in- formation, several parts of both encoder and decoder have to be Lossless coding will become the latest extension of the MPEG-4 implemented in a deterministic way. audio standard. In response to a call for proposals, many com- The MPEG-4 ALS codec uses forward-adaptive Linear Pre- panies have submitted lossless audio codecs for evaluation. The dictive Coding (LPC) to reduce bit rates compared to PCM, leav- codec of the Technical University of Berlin was chosen as refer- ing the optimization entirely to the encoder. Thus, various encoder ence model for MPEG-4 Audio Lossless Coding (ALS), attaining implementations are possible, offering a certain range in terms of working draft status in July 2003. The encoder is based on linear efficiency and complexity. This section gives an overview of the prediction, which enables high compression even with moderate basic encoder and decoder functionality. complexity, while the corresponding decoder is straightforward. The paper describes the basic elements of the codec, points out 2.1. Encoder Overview envisaged applications, and gives an outline of the standardization process. The MPEG-4 ALS encoder (Figure 1) typically consists of these main building blocks: • 1. INTRODUCTION Buffer: Stores one audio frame. A frame is divided into blocks of samples, typically one for each channel. Lossless audio coding enables the compression of digital audio • Coefficients Estimation and Quantization: Estimates (and data without any loss in quality due to a perfect reconstruction quantizes) the optimum predictor coefficients for each of the original signal. -
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 -
Implementing Object-Based Audio in Radio Broadcasting
Object-based Audio in Radio Broadcast Implementing Object-based audio in radio broadcasting Diplomarbeit Ausgeführt zum Zweck der Erlangung des akademischen Grades Dipl.-Ing. für technisch-wissenschaftliche Berufe am Masterstudiengang Digitale Medientechnologien and der Fachhochschule St. Pölten, Masterkalsse Audio Design von: Baran Vlad DM161567 Betreuer/in und Erstbegutachter/in: FH-Prof. Dipl.-Ing Franz Zotlöterer Zweitbegutacher/in:FH Lektor. Dipl.-Ing Stefan Lainer [Wien, 09.09.2019] I Ehrenwörtliche Erklärung Ich versichere, dass - ich diese Arbeit selbständig verfasst, andere als die angegebenen Quellen und Hilfsmittel nicht benutzt und mich auch sonst keiner unerlaubten Hilfe bedient habe. - ich dieses Thema bisher weder im Inland noch im Ausland einem Begutachter/einer Begutachterin zur Beurteilung oder in irgendeiner Form als Prüfungsarbeit vorgelegt habe. Diese Arbeit stimmt mit der vom Begutachter bzw. der Begutachterin beurteilten Arbeit überein. .................................................. ................................................ Ort, Datum Unterschrift II Kurzfassung Die Wissenschaft der objektbasierten Tonherstellung befasst sich mit einer neuen Art der Übermittlung von räumlichen Informationen, die sich von kanalbasierten Systemen wegbewegen, hin zu einem Ansatz, der Ton unabhängig von dem Gerät verarbeitet, auf dem es gerendert wird. Diese objektbasierten Systeme behandeln Tonelemente als Objekte, die mit Metadaten verknüpft sind, welche ihr Verhalten beschreiben. Bisher wurde diese Forschungen vorwiegend -
Mpeg Vbr Slice Layer Model Using Linear Predictive Coding and Generalized Periodic Markov Chains
MPEG VBR SLICE LAYER MODEL USING LINEAR PREDICTIVE CODING AND GENERALIZED PERIODIC MARKOV CHAINS Michael R. Izquierdo* and Douglas S. Reeves** *Network Hardware Division IBM Corporation Research Triangle Park, NC 27709 [email protected] **Electrical and Computer Engineering North Carolina State University Raleigh, North Carolina 27695 [email protected] ABSTRACT The ATM Network has gained much attention as an effective means to transfer voice, video and data information We present an MPEG slice layer model for VBR over computer networks. ATM provides an excellent vehicle encoded video using Linear Predictive Coding (LPC) and for video transport since it provides low latency with mini- Generalized Periodic Markov Chains. Each slice position mal delay jitter when compared to traditional packet net- within an MPEG frame is modeled using an LPC autoregres- works [11]. As a consequence, there has been much research sive function. The selection of the particular LPC function is in the area of the transmission and multiplexing of com- governed by a Generalized Periodic Markov Chain; one pressed video data streams over ATM. chain is defined for each I, P, and B frame type. The model is Compressed video differs greatly from classical packet sufficiently modular in that sequences which exclude B data sources in that it is inherently quite bursty. This is due to frames can eliminate the corresponding Markov Chain. We both temporal and spatial content variations, bounded by a show that the model matches the pseudo-periodic autocorre- fixed picture display rate. Rate control techniques, such as lation function quite well. We present simulation results of CBR (Constant Bit Rate), were developed in order to reduce an Asynchronous Transfer Mode (ATM) video transmitter the burstiness of a video stream. -
Lossless Audio Coding Using Adaptive Linear Prediction
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by ScholarBank@NUS LOSSLESS AUDIO CODING USING ADAPTIVE LINEAR PREDICTION SU XIN RONG (B.Eng., SJTU, PRC) A THESIS SUBMITTED FOR THE DEGREE OF MASTER OF ENGINEERING DEPARTMENT OF ELECTRICAL AND COMPUTER ENGINEERING NATIONAL UNIVERSITY OF SINGAPORE 2005 ACKNOWLEDGEMENTS First of all, I would like to take this opportunity to express my deepest gratitude to my supervisor Dr. Huang Dong Yan from Institute for Infocomm Research for her continuous guidance and help, without which this thesis would not have been possible. I would also like to specially thank my supervisor Assistant Professor Nallanathan Arumugam from NUS for his continuous support and help. Finally, I would like to thank all the people who might help me during the project. ii TABLE OF CONTENTS ACKNOWLEDGEMENTS .............................................................................................. ii TABLE OF CONTENTS ................................................................................................. iii SUMMARY ........................................................................................................................vi LIST OF TABLES .......................................................................................................... viii LIST OF FIGURES ...........................................................................................................ix CHAPTER 1 INTRODUCTION......................................................................................................... -
Preview - Click Here to Buy the Full Publication
This is a preview - click here to buy the full publication IEC 62481-2 ® Edition 2.0 2013-09 INTERNATIONAL STANDARD colour inside Digital living network alliance (DLNA) home networked device interoperability guidelines – Part 2: DLNA media formats INTERNATIONAL ELECTROTECHNICAL COMMISSION PRICE CODE XH ICS 35.100.05; 35.110; 33.160 ISBN 978-2-8322-0937-0 Warning! Make sure that you obtained this publication from an authorized distributor. ® Registered trademark of the International Electrotechnical Commission This is a preview - click here to buy the full publication – 2 – 62481-2 © IEC:2013(E) CONTENTS FOREWORD ......................................................................................................................... 20 INTRODUCTION ................................................................................................................... 22 1 Scope ............................................................................................................................. 23 2 Normative references ..................................................................................................... 23 3 Terms, definitions and abbreviated terms ....................................................................... 30 3.1 Terms and definitions ............................................................................................ 30 3.2 Abbreviated terms ................................................................................................. 34 3.4 Conventions ......................................................................................................... -
Codifica Audio Lossless
UNIVERSITA` DEGLI STUDI DI PADOVA DIPARTIMENTO DI INGEGNERIA DELL’INFORMAZIONE Tesi di Laurea in INGEGNERIA DELL’INFORMAZIONE Codifica audio lossless Relatore Candidato Prof. Giancarlo Calvagno Mattia Bonomo Anno Accademico 2009/2010 UNIVERSITA` DEGLI STUDI DI PADOVA DIPARTIMENTO DI INGEGNERIA DELL’INFORMAZIONE Tesi di Laurea in INGEGNERIA DELL’INFORMAZIONE Codifica audio lossless Relatore Candidato Prof. Giancarlo Calvagno Mattia Bonomo Anno Accademico 2009/2010 Ai miei nonni. Indice Ringraziamenti 7 1 Introduzione 9 2 Principi della compressione lossless 13 2.1 Blocking e Framing ......................... 14 2.2 Interchannel decorrelation ..................... 17 2.3 Intrachannel decorrelation ..................... 19 2.4 Entropy Coding ........................... 26 3 Comparativa codec lossless 33 3.1 Standard MPEG .......................... 34 4 MPEG-4ALS 35 4.1 Blocking ............................... 36 4.2 Accesso casuale ........................... 36 4.3 Predizione .............................. 37 4.3.1 Predizione LPC ....................... 37 4.3.2 RLS-LMS .......................... 37 4.3.3 Predizione a lungo termine (LTP) ............. 41 4.4 Codifica Entropica ......................... 41 5 MPEG4-SLS 43 5.1 IntMCDT .............................. 44 5.1.1 Finestramento ........................ 45 5.1.2 DCT-IV ........................... 45 5.1.3 Modellazione del rumore .................. 46 5.2 Error Mapping ........................... 46 5 5.3 Codifica Entropica ......................... 48 5.3.1 Codifica Bit-Plane ..................... 48 5.3.2 Codifica a bassa energia .................. 51 6 Analisi sulle prestazioni di MPEG4-ALS 53 6.1 MPEG4-ALS ............................ 53 7 Parte sperimentale 57 7.1 Descrizione del progetto ...................... 57 7.1.1 Divisione in blocchi .................... 58 7.1.2 Inter-channel coding .................... 58 7.1.3 Algoritmo MEQP ...................... 59 7.1.4 Codifica entropica ..................... 60 7.1.5 Multiplexing ........................ 60 7.2 Simulazioni ............................ -
The Standard for Lossless Audio Coding
한국음향학 회지 제 28권 제 7호 pp. 618-629 (2009) MPEG-4 ALS - The Standard for Lossless Audio Coding Tilman Liebchen* *LG Electronics, Berlin, Germany (접수일자 : 2009년 9월 2일 ; 채택일자 : 2009년 9월 15일 ) The MPEG-4 Audio Lossless Coding (ALS) standard belongs to the family MPEG-4 audio coding standards. In contrast to lossy codecs such as AAC, which merely strive to preserve the subjective audio quality, lossless coding preserves every sin이 e bit of the original audio data. The ALS core codec is based on forward-adaptive linear prediction, which combines remarkable compression with low complexity. Additional features include long-term prediction, multichannel coding, and compression of floating-point audio material. This paper describes the basic elements of the ALS codec with a focus on prediction, entropy coding, and related tools, and points out the most important applications of this standardized lossless audio format. Keywords: Audio Coding, Lossless, Compression, Linear Prediction, MPEG, Standard, ALS ASK subject classification* New Media (13) I. Introduction The first version of the MPEG4 ALS standard was published in 2006 [2], Since then, some corrigenda have Lossless audio coding enables the compression of been issued, and additional tools such as reference digital audio data without any loss in quality due to software [3] and conformance bitstreams [4] have a perfect (i.e. bit-identical) reconstruction of the been standardized and released. The latest description of MPEG4 ALS has been fully integrated into the 4th original signal. On the other hand, modem perceptual audio coding standards such as MP3 or AAC are always Edition of the MPEG4 audio standard [5], which was lossy, since they never fully preserve the original ultimately published in 2009. -
Speech Compression
information Review Speech Compression Jerry D. Gibson Department of Electrical and Computer Engineering, University of California, Santa Barbara, CA 93118, USA; [email protected]; Tel.: +1-805-893-6187 Academic Editor: Khalid Sayood Received: 22 April 2016; Accepted: 30 May 2016; Published: 3 June 2016 Abstract: Speech compression is a key technology underlying digital cellular communications, VoIP, voicemail, and voice response systems. We trace the evolution of speech coding based on the linear prediction model, highlight the key milestones in speech coding, and outline the structures of the most important speech coding standards. Current challenges, future research directions, fundamental limits on performance, and the critical open problem of speech coding for emergency first responders are all discussed. Keywords: speech coding; voice coding; speech coding standards; speech coding performance; linear prediction of speech 1. Introduction Speech coding is a critical technology for digital cellular communications, voice over Internet protocol (VoIP), voice response applications, and videoconferencing systems. In this paper, we present an abridged history of speech compression, a development of the dominant speech compression techniques, and a discussion of selected speech coding standards and their performance. We also discuss the future evolution of speech compression and speech compression research. We specifically develop the connection between rate distortion theory and speech compression, including rate distortion bounds for speech codecs. We use the terms speech compression, speech coding, and voice coding interchangeably in this paper. The voice signal contains not only what is said but also the vocal and aural characteristics of the speaker. As a consequence, it is usually desired to reproduce the voice signal, since we are interested in not only knowing what was said, but also in being able to identify the speaker. -
Input Formats & Codecs
Input Formats & Codecs Pivotshare offers upload support to over 99.9% of codecs and container formats. Please note that video container formats are independent codec support. Input Video Container Formats (Independent of codec) 3GP/3GP2 ASF (Windows Media) AVI DNxHD (SMPTE VC-3) DV video Flash Video Matroska MOV (Quicktime) MP4 MPEG-2 TS, MPEG-2 PS, MPEG-1 Ogg PCM VOB (Video Object) WebM Many more... Unsupported Video Codecs Apple Intermediate ProRes 4444 (ProRes 422 Supported) HDV 720p60 Go2Meeting3 (G2M3) Go2Meeting4 (G2M4) ER AAC LD (Error Resiliant, Low-Delay variant of AAC) REDCODE Supported Video Codecs 3ivx 4X Movie Alaris VideoGramPiX Alparysoft lossless codec American Laser Games MM Video AMV Video Apple QuickDraw ASUS V1 ASUS V2 ATI VCR-2 ATI VCR1 Auravision AURA Auravision Aura 2 Autodesk Animator Flic video Autodesk RLE Avid Meridien Uncompressed AVImszh AVIzlib AVS (Audio Video Standard) video Beam Software VB Bethesda VID video Bink video Blackmagic 10-bit Broadway MPEG Capture Codec Brooktree 411 codec Brute Force & Ignorance CamStudio Camtasia Screen Codec Canopus HQ Codec Canopus Lossless Codec CD Graphics video Chinese AVS video (AVS1-P2, JiZhun profile) Cinepak Cirrus Logic AccuPak Creative Labs Video Blaster Webcam Creative YUV (CYUV) Delphine Software International CIN video Deluxe Paint Animation DivX ;-) (MPEG-4) DNxHD (VC3) DV (Digital Video) Feeble Files/ScummVM DXA FFmpeg video codec #1 Flash Screen Video Flash Video (FLV) / Sorenson Spark / Sorenson H.263 Forward Uncompressed Video Codec fox motion video FRAPS: -
Advanced Speech Compression VIA Voice Excited Linear Predictive Coding Using Discrete Cosine Transform (DCT)
International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075, Volume-2 Issue-3, February 2013 Advanced Speech Compression VIA Voice Excited Linear Predictive Coding using Discrete Cosine Transform (DCT) Nikhil Sharma, Niharika Mehta Abstract: One of the most powerful speech analysis techniques LPC makes coding at low bit rates possible. For LPC-10, the is the method of linear predictive analysis. This method has bit rate is about 2.4 kbps. Even though this method results in become the predominant technique for representing speech for an artificial sounding speech, it is intelligible. This method low bit rate transmission or storage. The importance of this has found extensive use in military applications, where a method lies both in its ability to provide extremely accurate high quality speech is not as important as a low bit rate to estimates of the speech parameters and in its relative speed of computation. The basic idea behind linear predictive analysis is allow for heavy encryptions of secret data. However, since a that the speech sample can be approximated as a linear high quality sounding speech is required in the commercial combination of past samples. The linear predictor model provides market, engineers are faced with using other techniques that a robust, reliable and accurate method for estimating parameters normally use higher bit rates and result in higher quality that characterize the linear, time varying system. In this project, output. In LPC-10 vocal tract is represented as a time- we implement a voice excited LPC vocoder for low bit rate speech varying filter and speech is windowed about every 30ms. -
Critical Assessment of Advanced Coding Standards for Lossless Audio Compression
TONNY HIDAYAT et al: A CRITICAL ASSESSMENT OF ADVANCED CODING STANDARDS FOR LOSSLESS .. A Critical Assessment of Advanced Coding Standards for Lossless Audio Compression Tonny Hidayat Mohd Hafiz Zakaria, Ahmad Naim Che Pee Department of Information Technology Faculty of Information and Communication Technology Universitas Amikom Yogyakarta Universiti Teknikal Malaysia Melaka Yogyakarta, Indonesia Melaka, Malaysia [email protected] [email protected], [email protected] Abstract - Image data, text, video, and audio data all require compression for storage issues and real-time access via computer networks. Audio data cannot use compression technique for generic data. The use of algorithms leads to poor sound quality, small compression ratios and algorithms are not designed for real-time access. Lossless audio compression has achieved observation as a research topic and business field of the importance of the need to store data with excellent condition and larger storage charges. This article will discuss and analyze the various lossless and standardized audio coding algorithms that concern about LPC definitely due to its reputation and resistance to compression that is audio. However, another expectation plans are likewise broke down for relative materials. Comprehension of LPC improvements, for example, LSP deterioration procedures is additionally examined in this paper. Keywords - component; Audio; Lossless; Compression; coding. I. INTRODUCTION Compression is to shrink / compress the size. Data compression is a technique to minimize the data so that files can be obtained with a size smaller than the original file size. Compression is needed to minimize the data storage (because the data size is smaller than the original), accelerate information transmission, and limit bandwidth prerequisites.