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Speech Coding and Compression
Introduction to voice coding/compression PCM coding Speech vocoders based on speech production LPC based vocoders Speech over packet networks Speech coding and compression Corso di Networked Multimedia Systems Master Universitario di Primo Livello in Progettazione e Gestione di Sistemi di Rete Carlo Drioli Università degli Studi di Verona Facoltà di Scienze Matematiche, Dipartimento di Informatica Fisiche e Naturali Speech coding and compression Introduction to voice coding/compression PCM coding Speech vocoders based on speech production LPC based vocoders Speech over packet networks Speech coding and compression: OUTLINE Introduction to voice coding/compression PCM coding Speech vocoders based on speech production LPC based vocoders Speech over packet networks Speech coding and compression Introduction to voice coding/compression PCM coding Speech vocoders based on speech production LPC based vocoders Speech over packet networks Introduction Approaches to voice coding/compression I Waveform coders (PCM) I Voice coders (vocoders) Quality assessment I Intelligibility I Naturalness (involves speaker identity preservation, emotion) I Subjective assessment: Listening test, Mean Opinion Score (MOS), Diagnostic acceptability measure (DAM), Diagnostic Rhyme Test (DRT) I Objective assessment: Signal to Noise Ratio (SNR), spectral distance measures, acoustic cues comparison Speech coding and compression Introduction to voice coding/compression PCM coding Speech vocoders based on speech production LPC based vocoders Speech over packet networks -
PSK31 Audio Beacon Kit
PSK31 Audio Beacon Kit Build this programmable single-chip generator of PSK31-encoded audio data streams and use it as a signal generator, a beacon input to your SSB rig — or as the start of a single-chip PSK31 controller! Version 2, July 2001 Created by the New Jersey QRP Club The NJQRP “PSK31 Audio Beacon Kit” 1 PSK31 Audio Beacon Kit OVERVIEW Here’s an easy, fun and intriguingly use- ting at a laptop equipped with DigiPan Thank you for purchasing the PSK31 phase relationships at bit transitions, and ful project that has evolved from an on- software. Zudio Beacon from the New Jersey QRP the schematic has been augmented with going design effort to reduce the complex- Club! We think you’ll have fun assem- Construction is simple and straightfor- helpful notations. ity of a PSK31 controller. bling and operating this inexpensive-yet- ward and you’ll have immediate feedback For the very latest information, software flexible audio modulator as PSK31-en- A conventional PC typically provides on how your Beacon works when you updates, and kit assembly & usage tips, coded data streams. the relatively intensive computing power plug in a 9V battery and speaker. please be sure to see the PSK31 Beacon required for PSK31 modulation and de- This project was first introduced as a kit Beacon Features website at http://www.njqrp.org/ modulation. at the Atlanticon QRP Forum in March psk31beacon/psk31beacon.html . Visit n Single-chip implementation of 2001, and then published in a feature ar- soon and often, as it’s guaranteed to be With this Beacon project, however, the PSK31 encoding and audio waveform gen- ticle of QST magazine in its August 2001 helpful! PSK31 modulation computations have eration. -
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 -
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. -
The Discrete Cosine Transform (DCT): Theory and Application
The Discrete Cosine Transform (DCT): 1 Theory and Application Syed Ali Khayam Department of Electrical & Computer Engineering Michigan State University March 10th 2003 1 This document is intended to be tutorial in nature. No prior knowledge of image processing concepts is assumed. Interested readers should follow the references for advanced material on DCT. ECE 802 – 602: Information Theory and Coding Seminar 1 – The Discrete Cosine Transform: Theory and Application 1. Introduction Transform coding constitutes an integral component of contemporary image/video processing applications. Transform coding relies on the premise that pixels in an image exhibit a certain level of correlation with their neighboring pixels. Similarly in a video transmission system, adjacent pixels in consecutive frames2 show very high correlation. Consequently, these correlations can be exploited to predict the value of a pixel from its respective neighbors. A transformation is, therefore, defined to map this spatial (correlated) data into transformed (uncorrelated) coefficients. Clearly, the transformation should utilize the fact that the information content of an individual pixel is relatively small i.e., to a large extent visual contribution of a pixel can be predicted using its neighbors. A typical image/video transmission system is outlined in Figure 1. The objective of the source encoder is to exploit the redundancies in image data to provide compression. In other words, the source encoder reduces the entropy, which in our case means decrease in the average number of bits required to represent the image. On the contrary, the channel encoder adds redundancy to the output of the source encoder in order to enhance the reliability of the transmission. -
Packet Status Register Every Ohio
TAPR #97 AUTUMN 2005 President’s Corner 1 TAPR Elects New President 2 PACKET 2005 DCC Report 3 New SIG for AX.25 Layer 2 Discussions 4 20th Annual SW Ohio Digital Symposium 4 STATUS AX.25 as a Layer 2 Protocol 5 Towards a Next Generation Amateur Radio Network 6 TM REGISTER TAPR Order Form 29 President’s Corner Mi Column es Su Column This marks my inaugural from you to confirm this. We are moving to more membership experience and involvement in projects. column in PSR as TAPR widely embrace and promote cutting edge technology, We are continuing to streamline the management of President. Some of you may and while this is not really new for us, it does mean that the office, and to assist this initiative, I will draw your remember me from my past we will be contemplating a shift in focus so that packet attention to the improvements that have been made incarnation on the TAPR will not really be our main or only focus. Digital modes to the web site. The responses we have seen so far have Board of Directors. It seems and technology will still be central, but I think you’ll been overwhelmingly positive, and we welcome further like another lifetime ago, but it wasn’t so long ago that appreciate our attempts to marry digital with analog feedback. TAPR was born in Tucson. In fact, next year will mark (you remember analog, don’t you?) and microwaves, I would like to make this column YOUR column, the twenty-fifth anniversary of our incorporation. -
Speech Compression Using Discrete Wavelet Transform and Discrete Cosine Transform
International Journal of Engineering Research & Technology (IJERT) ISSN: 2278-0181 Vol. 1 Issue 5, July - 2012 Speech Compression Using Discrete Wavelet Transform and Discrete Cosine Transform Smita Vatsa, Dr. O. P. Sahu M. Tech (ECE Student ) Professor Department of Electronicsand Department of Electronics and Communication Engineering Communication Engineering NIT Kurukshetra, India NIT Kurukshetra, India Abstract reproduced with desired level of quality. Main approaches of speech compression used today are Aim of this paper is to explain and implement waveform coding, transform coding and parametric transform based speech compression techniques. coding. Waveform coding attempts to reproduce Transform coding is based on compressing signal input signal waveform at the output. In transform by removing redundancies present in it. Speech coding at the beginning of procedure signal is compression (coding) is a technique to transform transformed into frequency domain, afterwards speech signal into compact format such that speech only dominant spectral features of signal are signal can be transmitted and stored with reduced maintained. In parametric coding signals are bandwidth and storage space respectively represented through a small set of parameters that .Objective of speech compression is to enhance can describe it accurately. Parametric coders transmission and storage capacity. In this paper attempts to produce a signal that sounds like Discrete wavelet transform and Discrete cosine original speechwhether or not time waveform transform -
Speech Coding Using Code Excited Linear Prediction
ISSN : 0976-8491 (Online) | ISSN : 2229-4333 (Print) IJCST VOL . 5, Iss UE SPL - 2, JAN - MAR C H 2014 Speech Coding Using Code Excited Linear Prediction 1Hemanta Kumar Palo, 2Kailash Rout 1ITER, Siksha ‘O’ Anusandhan University, Bhubaneswar, Odisha, India 2Gandhi Institute For Technology, Bhubaneswar, Odisha, India Abstract The main problem with the speech coding system is the optimum utilization of channel bandwidth. Due to this the speech signal is coded by using as few bits as possible to get low bit-rate speech coders. As the bit rate of the coder goes low, the intelligibility, SNR and overall quality of the speech signal decreases. Hence a comparative analysis is done of two different types of speech coders in this paper for understanding the utility of these coders in various applications so as to reduce the bandwidth and by different speech coding techniques and by reducing the number of bits without any appreciable compromise on the quality of speech. Hindi language has different number of stops than English , hence the performance of the coders must be checked on different languages. The main objective of this paper is to develop speech coders capable of producing high quality speech at low data rates. The focus of this paper is the development and testing of voice coding systems which cater for the above needs. Keywords PCM, DPCM, ADPCM, LPC, CELP Fig. 1: Speech Production Process I. Introduction Speech coding or speech compression is the compact digital The two types of speech sounds are voiced and unvoiced [1]. representations of voice signals [1-3] for the purpose of efficient They produce different sounds and spectra due to their differences storage and transmission. -
Amateur Extra License Class
Amateur Extra License Class 1 Amateur Extra Class Chapter 8 Radio Modes and Equipment 2 1 Modulation Systems FCC Emission Designations and Terms • Specified by ITU. • Either 3 or 7 characters long. • If 3 characters: • 1st Character = The type of modulation of the main carrier. • 2nd Character = The nature of the signal(s) modulating the main carrier. • 3rd Character = The type of information to be transmitted. • If 7 characters, add a 4-character bandwidth designator in front of the 3-character designator. 3 Modulation Systems FCC Emission Designations and Terms • Type of Modulation. N Unmodulated Carrier A Amplitude Modulation R Single Sideband Reduced Carrier J Single Sideband Suppressed Carrier C Vestigial Sideband F Frequency Modulation G Phase Modulation P, K, L, M, Q, V, W, X Various Types of Pulse Modulation 4 2 Modulation Systems FCC Emission Designations and Terms • Type of Modulating Signal. 0 No modulating signal 1 A single channel containing quantized or digital information without the use of a modulating sub-carrier 2 A single channel containing quantized or digital information with the use of a modulating sub-carrier 3 A single channel containing analog information 7 Two or more channels containing quantized or digital information 8 Two or more channels containing analog information X Cases not otherwise covered 5 Modulation Systems FCC Emission Designations and Terms • Type of Transmitted Information. N No information transmitted A Telegraphy - for aural reception B Telegraphy - for automatic reception C Facsimile D Data transmission, telemetry, telecommand E Telephony (including sound broadcasting) F Television (video) W Combination of the above X Cases not otherwise covered 6 3 Modulation Systems FCC Emission Designations and Terms • 3-character designator examples: • A1A = CW. -
An Ultra Low-Power Miniature Speech CODEC at 8 Kb/S and 16 Kb/S Robert Brennan, David Coode, Dustin Griesdorf, Todd Schneider Dspfactory Ltd
To appear in ICSPAT 2000 Proceedings, October 16-19, 2000, Dallas, TX. An Ultra Low-power Miniature Speech CODEC at 8 kb/s and 16 kb/s Robert Brennan, David Coode, Dustin Griesdorf, Todd Schneider Dspfactory Ltd. 611 Kumpf Drive, Unit 200 Waterloo, Ontario, N2V 1K8 Canada Abstract SmartCODEC platform consists of an effi- cient, block-floating point, oversampled This paper describes a CODEC implementa- Weighted OverLap-Add (WOLA) filterbank, a tion on an ultra low-power miniature applica- software-programmable dual-Harvard 16-bit tion specific signal processor (ASSP) designed DSP core, two high fidelity 14-bit A/D con- for mobile audio signal processing applica- verters, a 14-bit D/A converter and a flexible tions. The CODEC records speech to and set of peripherals [1]. The system hardware plays back from a serial flash memory at data architecture (Figure 1) was designed to enable rates of 16 and 8 kb/s, with a bandwidth of 4 memory upgrades. Removable memory cards kHz. This CODEC consumes only 1 mW in a or more power-efficient memory could be package small enough for use in a range of substituted for the serial flash memory. demanding portable applications. Results, improvements and applications are also dis- The CODEC communicates with the flash cussed. memory over an integrated SPI port that can transfer data at rates up to 80 kb/s. For this application, the port is configured to block 1. Introduction transfer frame packets every 14 ms. e Speech coding is ubiquitous. Increasing de- c i WOLA Filterbank v mands for portability and fidelity coupled with A/D e and D the desire for reduced storage and bandwidth o i l Programmable d o r u utilization have increased the demand for and t D/A DSP Core A deployment of speech CODECs. -
Cognitive Speech Coding Milos Cernak, Senior Member, IEEE, Afsaneh Asaei, Senior Member, IEEE, Alexandre Hyafil
1 Cognitive Speech Coding Milos Cernak, Senior Member, IEEE, Afsaneh Asaei, Senior Member, IEEE, Alexandre Hyafil Abstract—Speech coding is a field where compression ear and undergoes a highly complex transformation paradigms have not changed in the last 30 years. The before it is encoded efficiently by spikes at the auditory speech signals are most commonly encoded with com- nerve. This great efficiency in information representation pression methods that have roots in Linear Predictive has inspired speech engineers to incorporate aspects of theory dating back to the early 1940s. This paper tries to cognitive processing in when developing efficient speech bridge this influential theory with recent cognitive studies applicable in speech communication engineering. technologies. This tutorial article reviews the mechanisms of speech Speech coding is a field where research has slowed perception that lead to perceptual speech coding. Then considerably in recent years. This has occurred not it focuses on human speech communication and machine because it has achieved the ultimate in minimizing bit learning, and application of cognitive speech processing in rate for transparent speech quality, but because recent speech compression that presents a paradigm shift from improvements have been small and commercial applica- perceptual (auditory) speech processing towards cognitive tions (e.g., cell phones) have been mostly satisfactory for (auditory plus cortical) speech processing. The objective the general public, and the growth of available bandwidth of this tutorial is to provide an overview of the impact has reduced requirements to compress speech even fur- of cognitive speech processing on speech compression and discuss challenges faced in this interdisciplinary speech ther. -
Block Transforms in Progressive Image Coding
This is page 1 Printer: Opaque this Blo ck Transforms in Progressive Image Co ding Trac D. Tran and Truong Q. Nguyen 1 Intro duction Blo ck transform co ding and subband co ding have b een two dominant techniques in existing image compression standards and implementations. Both metho ds actually exhibit many similarities: relying on a certain transform to convert the input image to a more decorrelated representation, then utilizing the same basic building blo cks such as bit allo cator, quantizer, and entropy co der to achieve compression. Blo ck transform co ders enjoyed success rst due to their low complexity in im- plementation and their reasonable p erformance. The most p opular blo ck transform co der leads to the current image compression standard JPEG [1] which utilizes the 8 8 Discrete Cosine Transform DCT at its transformation stage. At high bit rates 1 bpp and up, JPEG o ers almost visually lossless reconstruction image quality. However, when more compression is needed i.e., at lower bit rates, an- noying blo cking artifacts showup b ecause of two reasons: i the DCT bases are short, non-overlapp ed, and have discontinuities at the ends; ii JPEG pro cesses each image blo ck indep endently. So, inter-blo ck correlation has b een completely abandoned. The development of the lapp ed orthogonal transform [2] and its generalized ver- sion GenLOT [3, 4] helps solve the blo cking problem to a certain extent by b or- rowing pixels from the adjacent blo cks to pro duce the transform co ecients of the current blo ck.