Audio Signal Processing and Coding
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Packetcable™ 2.0 Codec and Media Specification PKT-SP-CODEC
PacketCable™ 2.0 Codec and Media Specification PKT-SP-CODEC-MEDIA-I10-120412 ISSUED Notice This PacketCable specification is the result of a cooperative effort undertaken at the direction of Cable Television Laboratories, Inc. for the benefit of the cable industry and its customers. This document may contain references to other documents not owned or controlled by CableLabs. Use and understanding of this document may require access to such other documents. Designing, manufacturing, distributing, using, selling, or servicing products, or providing services, based on this document may require intellectual property licenses from third parties for technology referenced in this document. Neither CableLabs nor any member company is responsible to any party for any liability of any nature whatsoever resulting from or arising out of use or reliance upon this document, or any document referenced herein. This document is furnished on an "AS IS" basis and neither CableLabs nor its members provides any representation or warranty, express or implied, regarding the accuracy, completeness, noninfringement, or fitness for a particular purpose of this document, or any document referenced herein. 2006-2012 Cable Television Laboratories, Inc. All rights reserved. PKT-SP-CODEC-MEDIA-I10-120412 PacketCable™ 2.0 Document Status Sheet Document Control Number: PKT-SP-CODEC-MEDIA-I10-120412 Document Title: Codec and Media Specification Revision History: I01 - Released 04/05/06 I02 - Released 10/13/06 I03 - Released 09/25/07 I04 - Released 04/25/08 I05 - Released 07/10/08 I06 - Released 05/28/09 I07 - Released 07/02/09 I08 - Released 01/20/10 I09 - Released 05/27/10 I10 – Released 04/12/12 Date: April 12, 2012 Status: Work in Draft Issued Closed Progress Distribution Restrictions: Authors CL/Member CL/ Member/ Public Only Vendor Key to Document Status Codes: Work in Progress An incomplete document, designed to guide discussion and generate feedback, that may include several alternative requirements for consideration. -
Automatic Detection of Perceived Ringing Regions in Compressed Images
International Journal of Electronics and Communication Engineering. ISSN 0974-2166 Volume 4, Number 5 (2011), pp. 491-516 © International Research Publication House http://www.irphouse.com Automatic Detection of Perceived Ringing Regions in Compressed Images D. Minola Davids Research Scholar, Singhania University, Rajasthan, India Abstract An efficient approach toward a no-reference ringing metric intrinsically exists of two steps: first detecting regions in an image where ringing might occur, and second quantifying the ringing annoyance in these regions. This paper presents a novel approach toward the first step: the automatic detection of regions visually impaired by ringing artifacts in compressed images. It is a no- reference approach, taking into account the specific physical structure of ringing artifacts combined with properties of the human visual system (HVS). To maintain low complexity for real-time applications, the proposed approach adopts a perceptually relevant edge detector to capture regions in the image susceptible to ringing, and a simple yet efficient model of visual masking to determine ringing visibility. Index Terms: Luminance masking, per-ceptual edge, ringing artifact, texture masking. Introduction In current visual communication systems, the most essential task is to fit a large amount of visual information into the narrow bandwidth of transmission channels or into a limited storage space, while maintaining the best possible perceived quality for the viewer. A variety of compression algorithms, for example, such as JPEG[1] and MPEG/H.26xhave been widely adopted in image and video coding trying to achieve high compression efficiency at high quality. These lossy compression techniques, however, inevitably result in various coding artifacts, which by now are known and classified as blockiness, ringing, blur, etc. -
Audio Codec 120
Audio codec 120 click here to download Opus is a totally open, royalty-free, highly versatile audio codec. Opus is unmatched for interactive speech and music transmission over the Internet, but is also. Opus is a lossy audio coding format developed by the www.doorway.ru Foundation and standardized Opus supports constant and variable bitrate encoding from 6 kbit/ s to kbit/s, frame sizes from ms to ms, and five sampling rates from 8 . The RTP audio/video profile (RTP/AVP) is a profile for Real-time Transport Protocol (RTP) that specifies the technical parameters of audio and video streams. RTP specifies a general-purpose data format, but doesn't specify how. stereo audio codec (ADC and DAC) with single-ended Output Spectrum (– dB, N = ) f – Frequency – kHz. – – – – – – – 0. 0. The IETF Opus codec is a low-latency audio codec optimized for both voice and . in [RFC] can be any multiple of ms, up to a maximum of ms. IP Audio decoder with USB/Micro SD flash interface and serial port. Support of Internet Radio (AACplus, MP3, shoutcast, TCP streaming) and VoIP (SIP, RTP). V Mono Audio Codec with V Tolerant. Digital Interface . Transition Band. 5. kHz. Stop Band. kHz. Stop-Band Attenuation. dB. Group Delay. The SGTL is a low-power stereo codec is designed to provide a comprehensive audio solution for portable products that require line-in, mic-in, line-out. Opus has already been shown to out-perform other audio codecs at .. encoder now adds support for encoding packets of 80, , and Sonifex PS-PLAY IP to Audio Streaming Decoder Sonifex PS-SEND Audio to IP Streaming Encoder . -
Real-Time Programming and Processing of Music Signals Arshia Cont
Real-time Programming and Processing of Music Signals Arshia Cont To cite this version: Arshia Cont. Real-time Programming and Processing of Music Signals. Sound [cs.SD]. Université Pierre et Marie Curie - Paris VI, 2013. tel-00829771 HAL Id: tel-00829771 https://tel.archives-ouvertes.fr/tel-00829771 Submitted on 3 Jun 2013 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Realtime Programming & Processing of Music Signals by ARSHIA CONT Ircam-CNRS-UPMC Mixed Research Unit MuTant Team-Project (INRIA) Musical Representations Team, Ircam-Centre Pompidou 1 Place Igor Stravinsky, 75004 Paris, France. Habilitation à diriger la recherche Defended on May 30th in front of the jury composed of: Gérard Berry Collège de France Professor Roger Dannanberg Carnegie Mellon University Professor Carlos Agon UPMC - Ircam Professor François Pachet Sony CSL Senior Researcher Miller Puckette UCSD Professor Marco Stroppa Composer ii à Marie le sel de ma vie iv CONTENTS 1. Introduction1 1.1. Synthetic Summary .................. 1 1.2. Publication List 2007-2012 ................ 3 1.3. Research Advising Summary ............... 5 2. Realtime Machine Listening7 2.1. Automatic Transcription................. 7 2.2. Automatic Alignment .................. 10 2.2.1. -
Of the Cdma2000® System Specifications
3GPP2 S.P0052-0 Ver.0.2.7 Date: August 21, 2003 1 2 System Release Guide for the 3 Release <ALPHA> 4 of the cdma2000® System Specifications 5 COPYRIGHT © 2003 3GPP2 and its Organizational Partners claim copyright in this document and individual Or- ganizational Partners may copyright and issue documents or standards publications in in- dividual Organizational Partner’s name based on this document. Requests for reproduction of this document should be directed to the 3GPP2 Secretariat at [email protected]. Requests to reproduce individual Organizational Partner’s documents should be directed to that Organizational Partner. See www.3gpp2.org for more information. 6 7 © 2003 3GPP2 - i - S.P0052: CDMA2000® System Release Guide –Release <Alpha> 1 Executive Summary 2 The System Release Guide (SRG) for the Release <ALPHA> provides an overview 3 for and reference to the Release <ALPHA> of the 3GPP2 wireless telecommuni- 4 cation system (cdma2000®) capabilities, features, and services. This document 5 is intended for use by persons and /or companies who are developing and / or 6 deploying cdma2000 systems or by persons who are otherwise interested in 7 cdma2000 systems. 8 Air interface support for HRPD and enhanced IOS are included and provide 9 high-speed forward link data rate service capability up to 2.4576 Mbps in a 10 1.25 MHz. Since cdma2000 uses many IP based protocols to a large degree, it 11 offers various features of IP based services. The system in this release contains 12 support for the Legacy System, and limited support for the 3GPP2 Legacy Mo- 13 bile Station Domain, making use of IP-based transport and signaling. -
Error Correction Capacity of Unary Coding
Error Correction Capacity of Unary Coding Pushpa Sree Potluri1 Abstract Unary coding has found applications in data compression, neural network training, and in explaining the production mechanism of birdsong. Unary coding is redundant; therefore it should have inherent error correction capacity. An expression for the error correction capability of unary coding for the correction of single errors has been derived in this paper. 1. Introduction The unary number system is the base-1 system. It is the simplest number system to represent natural numbers. The unary code of a number n is represented by n ones followed by a zero or by n zero bits followed by 1 bit [1]. Unary codes have found applications in data compression [2],[3], neural network training [4]-[11], and biology in the study of avian birdsong production [12]-14]. One can also claim that the additivity of physics is somewhat like the tallying of unary coding [15],[16]. Unary coding has also been seen as the precursor to the development of number systems [17]. Some representations of unary number system use n-1 ones followed by a zero or with the corresponding number of zeroes followed by a one. Here we use the mapping of the left column of Table 1. Table 1. An example of the unary code N Unary code Alternative code 0 0 0 1 10 01 2 110 001 3 1110 0001 4 11110 00001 5 111110 000001 6 1111110 0000001 7 11111110 00000001 8 111111110 000000001 9 1111111110 0000000001 10 11111111110 00000000001 The unary number system may also be seen as a space coding of numerical information where the location determines the value of the number. -
21065L Audio Tutorial
a Using The Low-Cost, High Performance ADSP-21065L Digital Signal Processor For Digital Audio Applications Revision 1.0 - 12/4/98 dB +12 0 -12 Left Right Left EQ Right EQ Pan L R L R L R L R L R L R L R L R 1 2 3 4 5 6 7 8 Mic High Line L R Mid Play Back Bass CNTR 0 0 3 4 Input Gain P F R Master Vol. 1 2 3 4 5 6 7 8 Authors: John Tomarakos Dan Ledger Analog Devices DSP Applications 1 Using The Low Cost, High Performance ADSP-21065L Digital Signal Processor For Digital Audio Applications Dan Ledger and John Tomarakos DSP Applications Group, Analog Devices, Norwood, MA 02062, USA This document examines desirable DSP features to consider for implementation of real time audio applications, and also offers programming techniques to create DSP algorithms found in today's professional and consumer audio equipment. Part One will begin with a discussion of important audio processor-specific characteristics such as speed, cost, data word length, floating-point vs. fixed-point arithmetic, double-precision vs. single-precision data, I/O capabilities, and dynamic range/SNR capabilities. Comparisions between DSP's and audio decoders that are targeted for consumer/professional audio applications will be shown. Part Two will cover example algorithmic building blocks that can be used to implement many DSP audio algorithms using the ADSP-21065L including: Basic audio signal manipulation, filtering/digital parametric equalization, digital audio effects and sound synthesis techniques. TABLE OF CONTENTS 0. INTRODUCTION ................................................................................................................................................................4 1. -
LDAC: Experience Your N8 Through a Wireless Hi-Res Connection
LDAC: Experience your N8 through a wireless Hi-Res connection (1) Introduction Bluetooth has always been considered as a convenient but low quality option for audiophiles. The situation was changed dramatically in past few years when new codec such as AAC and aptX gained popularity, and the latest LDAC Bluetooth codec developed by Sony has added fuel to the fire. LDAC allows streaming audio up to 24Bit/96kHz over a Bluetooth connection, and it has made full use of the bandwidth, offering a connection speed up to 990 kbps. LDAC employs a hybrid coding scheme to squeeze the Hi-Res audio data into the limited bandwidth. It comes with 3 connection modes: quality priority (990 kbps), normal (660 kbps), and connection priority (330 kbps) respectively. The playback quality will be compromised when only lower speed options are feasible (either hardware limitation of environment constraint). LDAC is a lossy compression codec. At Quality Priority mode, Sony promised CD- quality playback at quality priority mode and from what has been announced, 16Bit/44.1kHz bitstream will be transmitted as-is without any compression. LDAC is used across a wide range of Sony products, including headphones, speakers, mobile phones, portable player and home theater systems. When Google announced Android O (aka Android 8.0 Oreo) at Mar. 2017, the mobile industry was surprised that Sony has become an Android partner and played a key role to improve the wireless audio capability of Android devices. Among the features enhancements and bug fixes, LDAC will be available as a part of the core Android Open Source Project (AOSP) code, enabling OEMs to support this Sony codec in their Android 0 devices freely. -
Soft Compression for Lossless Image Coding
1 Soft Compression for Lossless Image Coding Gangtao Xin, and Pingyi Fan, Senior Member, IEEE Abstract—Soft compression is a lossless image compression method, which is committed to eliminating coding redundancy and spatial redundancy at the same time by adopting locations and shapes of codebook to encode an image from the perspective of information theory and statistical distribution. In this paper, we propose a new concept, compressible indicator function with regard to image, which gives a threshold about the average number of bits required to represent a location and can be used for revealing the performance of soft compression. We investigate and analyze soft compression for binary image, gray image and multi-component image by using specific algorithms and compressible indicator value. It is expected that the bandwidth and storage space needed when transmitting and storing the same kind of images can be greatly reduced by applying soft compression. Index Terms—Lossless image compression, information theory, statistical distributions, compressible indicator function, image set compression. F 1 INTRODUCTION HE purpose of image compression is to reduce the where H(X1;X2; :::; Xn) is the joint entropy of the symbol T number of bits required to represent an image as much series fXi; i = 1; 2; :::; ng. as possible under the condition that the fidelity of the It’s impossible to reach the entropy rate and everything reconstructed image to the original image is higher than a we can do is to make great efforts to get close to it. Soft com- required value. Image compression often includes two pro- pression [1] was recently proposed, which uses locations cesses, encoding and decoding. -
USER's GUIDE Ver. 1.0EN
R2 USER’S GUIDE ver. 1.0 EN THANK YOU FOR PURCHASING A COWON PRODUCT. We do our utmost to deliver DIGITAL PRIDE to our customers. This manual contains information on how to use the product and the precautions to take during use. If you familiarize yourself with this manual, you will have a more enjoyable digital experience. Product specification may change without notice. Images contained in this manual may differ from the actual product. 2 COPYRIGHT NOTICE Introduction to website + The address of the product-related website is http://www.COWON.com. + You can download the latest information on our products and the most recent firmware updates from our website. + For first-time users, we provide an FAQ section and a user guide. + Become a member of the website by using the serial number on the back of the product to register the product. Y ou will then be a registered member. + Once you become a registered member, you can use the one-to-one enquiry service to receive online customer advice. Yo u can also receive information on new products and events by e-mail. General + COWON® and PLENUE® are registered trademarks of our company and/or its affiliates. + This manual is copyrighted by our company, and any unauthorized reproduction or distribution of its contents, in whole or in part, is strictly pr ohibited. + Our company complies with the Music Industry Promotion Act, Game Industry Promotion Act, Video Industry Promotion Act, and other relevant la ws and regulations. Users are also encouraged to comply with any such laws and regulations. -
Analysis of Ringing Artifact in Image Fusion Using Directional Wavelet
Special Issue - 2021 International Journal of Engineering Research & Technology (IJERT) ISSN: 2278-0181 NTASU - 2020 Conference Proceedings Analysis of Ringing Artifact in Image Fusion Using Directional Wavelet Transforms y z x Ashish V. Vanmali Tushar Kataria , Samrudhha G. Kelkar , Vikram M. Gadre Dept. of Information Technology Dept. of Electrical Engineering Vidyavardhini’s C.O.E. & Tech. Indian Institute of Technology, Bombay Vasai, Mumbai, India – 401202 Powai, Mumbai, India – 400076 Abstract—In the field of multi-data analysis and fusion, image • Images taken from multiple sensors. Examples include fusion plays a vital role for many applications. With inventions near infrared (NIR) images, IR images, CT, MRI, PET, of new sensors, the demand of high quality image fusion fMRI etc. algorithms has seen tremendous growth. Wavelet based fusion is a popular choice for many image fusion algorithms, because of its We can broadly classify the image fusion techniques into four ability to decouple different features of information. However, categories: it suffers from ringing artifacts generated in the output. This 1) Component substitution based fusion algorithms [1]–[5] paper presents an analysis of ringing artifacts in application of 2) Optimization based fusion algorithms [6]–[10] image fusion using directional wavelets (curvelets, contourlets, non-subsampled contourlets etc.). We compare the performance 3) Multi-resolution (wavelets and others) based fusion algo- of various fusion rules for directional wavelets available in rithms [11]–[15] and literature. The experimental results suggest that the ringing 4) Neural network based fusion algorithms [16]–[19]. artifacts are present in all types of wavelets with the extent of Wavelet based multi-resolution analysis decouples data into artifact varying with type of the wavelet, fusion rule used and levels of decomposition. -
2D Signal Processing (III): 2D Filtering
6.003 Signal Processing Week 11, Lecture A: 2D Signal Processing (III): 2D Filtering 6.003 Fall 2020 Filtering and Convolution Time domain: Frequency domain: 푋[푘푟, 푘푐] ∙ 퐻[푘푟, 푘푐] = 푌[푘푟, 푘푐] Today: More detailed look and further understanding about 2D filtering. 2D Low Pass Filtering Given the following image, what happens if we apply a filter that zeros out all the high frequencies in the image? Where did the ripples come from? 2D Low Pass Filtering The operation we did is equivalent to filtering: 푌 푘푟, 푘푐 = 푋 푘푟, 푘푐 ∙ 퐻퐿 푘푟, 푘푐 , where 2 2 1 푓 푘푟 + 푘푐 ≤ 25 퐻퐿 푘푟, 푘푐 = ቊ 0 표푡ℎ푒푟푤푠푒 2D Low Pass Filtering 2 2 1 푓 푘푟 + 푘푐 ≤ 25 Find the 2D unit-sample response of the 2D LPF. 퐻퐿 푘푟, 푘푐 = ቊ 0 표푡ℎ푒푟푤푠푒 퐻 푘 , 푘 퐿 푟 푐 ℎ퐿 푟, 푐 (Rec 8A): 푠푛 Ω 푛 ℎ 푛 = 푐 퐿 휋푛 Ω푐 Ω푐 The step changes in 퐻퐿 푘푟, 푘푐 generated overshoot: Gibb’s phenomenon. 2D Convolution Multiplying by the LPF is equivalent to circular convolution with its spatial-domain representation. ⊛ = 2D Low Pass Filtering Consider using the following filter, which is a circularly symmetric version of the Hann window. 2 2 1 1 푘푟 + 푘푐 2 2 + 푐표푠 휋 ∙ 푓 푘푟 + 푘푐 ≤ 25 퐻퐿2 푘푟, 푘푐 = 2 2 25 0 표푡ℎ푒푟푤푠푒 2 2 1 푓 푘푟 + 푘푐 ≤ 25 퐻퐿1 푘푟, 푘푐 = ቐ Now the ripples are gone. 0 표푡ℎ푒푟푤푠푒 But image more blurred when compare to the one with LPF1: With the same base-width, Hann window filter cut off more high freq.