Audio Signal Processing and Coding
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How to Tape-Record Primate Vocalisations Version June 2001
How To Tape-Record Primate Vocalisations Version June 2001 Thomas Geissmann Institute of Zoology, Tierärztliche Hochschule Hannover, D-30559 Hannover, Germany E-mail: [email protected] Key Words: Sound, vocalisation, song, call, tape-recorder, microphone Clarence R. Carpenter at Doi Dao (north of Chiengmai, Thailand) in 1937, with the parabolic reflector which was used for making the first sound- recordings of wild gibbons (from Carpenter, 1940, p. 26). Introduction Ornithologists have been exploring the possibilities and the methodology of tape- recording and archiving animal sounds for many decades. Primatologists, however, have only recently become aware that tape-recordings of primate sound may be just as valuable as traditional scientific specimens such as skins or skeletons, and should be preserved for posterity. Audio recordings should be fully documented, archived and curated to ensure proper care and accessibility. As natural populations disappear, sound archives will become increasingly important. This is an introductory text on how to tape-record primate vocalisations. It provides some information on the advantages and disadvantages of various types of equipment, and gives some tips for better recordings of primate vocalizations, both in the field and in the zoo. Ornithologists studying bird sound have to deal with very similar problems, and their introductory texts are recommended for further study (e.g. Budney & Grotke 1997; © Thomas Geissmann Geissmann: How to Tape-Record Primate Vocalisations 2 Kroodsman et al. 1996). For further information see also the websites listed at the end of this article. As a rule, prices for sound equipment go up over the years. Prices for equipment discussed below are in US$ and should only be used as very rough estimates. -
Digital Radio and CD-Quality Audio
Technological University Dublin ARROW@TU Dublin Books/Book chapters School of Media 2010-01-01 Sounding the Future: Digital Radio and CD-Quality Audio Brian O'Neill Technological University Dublin, [email protected] Follow this and additional works at: https://arrow.tudublin.ie/aaschmedbk Part of the Broadcast and Video Studies Commons, Communication Technology and New Media Commons, and the Film and Media Studies Commons Recommended Citation B. O'Neill, ‘Sounding the future': digital radio and CD-quality audio. In O'Neill, B., M. Ala-Fossi, et al., Eds. (2010). Digital Radio in Europe: Technologies, Industries and Cultures. Bristol, Intellect Books, pp.85-98. This Book Chapter is brought to you for free and open access by the School of Media at ARROW@TU Dublin. It has been accepted for inclusion in Books/Book chapters by an authorized administrator of ARROW@TU Dublin. For more information, please contact [email protected], [email protected]. This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 4.0 License [1] Chapter Four ‘Sounding the future': digital radio and CD-quality audio Brian O’Neill Central to the early effort to win acceptance for DAB in the early 1990s was an extensive process of promotion of the many claimed advantages of the new broadcasting technology. Digital radio broadcasting under the Eureka 147 DAB project offered many technical enhancements – more efficient use of the spectrum, improved transmission methods, and lower running costs – features that were attractive to industry professionals, broadcasting organisations, regulators and spectrum planners. But digital radio was also designed as a consumer proposition offering audiences a new and improved listening experience with ease of tuning, reliable reception, text and data services, interactive features, and significantly, ‘CD- quality’ audio. -
Digital Devices in Sociolinguistic Fieldworks
Digital Devices in Sociolinguistic Fieldworks* [NOTE: This is a largely modified version of Matsuda (2000) and was presented at METHODS (International Conference on Methods in Dialectology) IX Meeting at University of Joensuu on August 5, 2002. In addition to the people I mentioned in Matsuda (2000), I would like to send my cordial thanks to Paul Kerswill, Renée Fürst, T. de Graaf, John Nerbonne, for their comments and discussions at the conference. Renée deserves special thanks here, for providing me with additional references after the conference. It should be stressed, however, that all remaining errors are mine only. This research is partly supported by Grant-in-Aid for Scientific Research #13410139 from Japan Society for the Promotion of Science.] Kenjiro Matsuda Kobe Shoin Women''s University [email protected] 0. Introduction 1. Recyclability of Sociolinguistic Fieldwork Data With the beginning of Variationist paradigm in 1960s, sociolinguists began tape-recording and analyzing the daily speeches in the speech community. The search for the vernacular (in the sense of Labov 1972) was a clear departure from the traditional dialectological fieldwork that elicits a speaker’s response using a questionnaire. The interview may involve one or multiple speakers (group session), but in either case, there is no pre-set time length and the interview may go on as long as the speaker wants to talk. The interviewer may have a course of questions (module) to facilitate the naturalistic interview, so that s/he can obtain a wide range of speech style from the speaker, but the basic principle here is to follow the flow of the talk. -
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. -
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. -
Generalized Golomb Codes and Adaptive Coding of Wavelet-Transformed Image Subbands
IPN Progress Report 42-154 August 15, 2003 Generalized Golomb Codes and Adaptive Coding of Wavelet-Transformed Image Subbands A. Kiely1 and M. Klimesh1 We describe a class of prefix-free codes for the nonnegative integers. We apply a family of codes in this class to the problem of runlength coding, specifically as part of an adaptive algorithm for compressing quantized subbands of wavelet- transformed images. On test images, our adaptive coding algorithm is shown to give compression effectiveness comparable to the best performance achievable by an alternate algorithm that has been previously implemented. I. Generalized Golomb Codes A. Introduction Suppose we wish to use a variable-length binary code to encode integers that can take on any non- negative value. This problem arises, for example, in runlength coding—encoding the lengths of runs of a dominant output symbol from a discrete source. Encoding cannot be accomplished by simply looking up codewords from a table because the table would be infinite, and Huffman’s algorithm could not be used to construct the code anyway. Thus, one would like to use a simply described code that can be easily encoded and decoded. Although in practice there is generally a limit to the size of the integers that need to be encoded, a simple code for the nonnegative integers is often still the best choice. We now describe a class of variable-length binary codes for the nonnegative integers that contains several useful families of codes. Each code in this class is completely specified by an index function f from the nonnegative integers onto the nonnegative integers. -
The Pillars of Lossless Compression Algorithms a Road Map and Genealogy Tree
International Journal of Applied Engineering Research ISSN 0973-4562 Volume 13, Number 6 (2018) pp. 3296-3414 © Research India Publications. http://www.ripublication.com The Pillars of Lossless Compression Algorithms a Road Map and Genealogy Tree Evon Abu-Taieh, PhD Information System Technology Faculty, The University of Jordan, Aqaba, Jordan. Abstract tree is presented in the last section of the paper after presenting the 12 main compression algorithms each with a practical This paper presents the pillars of lossless compression example. algorithms, methods and techniques. The paper counted more than 40 compression algorithms. Although each algorithm is The paper first introduces Shannon–Fano code showing its an independent in its own right, still; these algorithms relation to Shannon (1948), Huffman coding (1952), FANO interrelate genealogically and chronologically. The paper then (1949), Run Length Encoding (1967), Peter's Version (1963), presents the genealogy tree suggested by researcher. The tree Enumerative Coding (1973), LIFO (1976), FiFO Pasco (1976), shows the interrelationships between the 40 algorithms. Also, Stream (1979), P-Based FIFO (1981). Two examples are to be the tree showed the chronological order the algorithms came to presented one for Shannon-Fano Code and the other is for life. The time relation shows the cooperation among the Arithmetic Coding. Next, Huffman code is to be presented scientific society and how the amended each other's work. The with simulation example and algorithm. The third is Lempel- paper presents the 12 pillars researched in this paper, and a Ziv-Welch (LZW) Algorithm which hatched more than 24 comparison table is to be developed. -
New Approaches to Fine-Grain Scalable Audio Coding
New Approaches to Fine-Grain Scalable Audio Coding Mahmood Movassagh Department of Electrical & Computer Engineering McGill University Montreal, Canada December 2015 Research Thesis submitted to McGill University in partial fulfillment of the requirements for the degree of PhD. c 2015 Mahmood Movassagh In memory of my mother whom I lost in the last year of my PhD studies To my father who has been the greatest support for my studies in my life Abstract Bit-rate scalability has been a useful feature in the multimedia communications. Without the need to re-encode the original signal, it allows for improving/decreasing the quality of a signal as more/less of a total bit stream becomes available. Using scalable coding, there is no need to store multiple versions of a signal encoded at different bit-rates. Scalable coding can also be used to provide users with different quality streaming when they have different constraints or when there is a varying channel; i.e., the receivers with lower channel capacities will be able to receive signals at lower bit-rates. It is especially useful in the client- server applications where the network nodes are able to drop some enhancement layer bits to satisfy link capacity constraints. In this dissertation, we provide three contributions to practical scalable audio coding systems. Our first contribution is the scalable audio coding using watermarking. The proposed scheme uses watermarking to embed some of the information of each layer into the previous layer. This approach leads to a saving in bitrate, so that it outperforms (in terms of rate-distortion) the common scalable audio coding based on the reconstruction error quantization (REQ) as used in MPEG-4 audio. -
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. -
Answers to Exercises
Answers to Exercises A bird does not sing because he has an answer, he sings because he has a song. —Chinese Proverb Intro.1: abstemious, abstentious, adventitious, annelidous, arsenious, arterious, face- tious, sacrilegious. Intro.2: When a software house has a popular product they tend to come up with new versions. A user can update an old version to a new one, and the update usually comes as a compressed file on a floppy disk. Over time the updates get bigger and, at a certain point, an update may not fit on a single floppy. This is why good compression is important in the case of software updates. The time it takes to compress and decompress the update is unimportant since these operations are typically done just once. Recently, software makers have taken to providing updates over the Internet, but even in such cases it is important to have small files because of the download times involved. 1.1: (1) ask a question, (2) absolutely necessary, (3) advance warning, (4) boiling hot, (5) climb up, (6) close scrutiny, (7) exactly the same, (8) free gift, (9) hot water heater, (10) my personal opinion, (11) newborn baby, (12) postponed until later, (13) unexpected surprise, (14) unsolved mysteries. 1.2: A reasonable way to use them is to code the five most-common strings in the text. Because irreversible text compression is a special-purpose method, the user may know what strings are common in any particular text to be compressed. The user may specify five such strings to the encoder, and they should also be written at the start of the output stream, for the decoder’s use. -
Lec 05 Arithmetic Coding
ECE 5578 Multimedia Communication Lec 05 Arithmetic Coding Zhu Li Dept of CSEE, UMKC web: http://l.web.umkc.edu/lizhu phone: x2346 Z. Li, Multimedia Communciation, 2018 p.1 Outline Lecture 04 ReCap Arithmetic Coding About Homework-1 and Lab Z. Li, Multimedia Communciation, 2018 p.2 JPEG Coding Block (8x8 pel) based coding DCT transform to find sparse * representation Quantization reflects human visual system Zig-Zag scan to convert 2D to 1D string Run-Level pairs to have even more = compact representation Hoffman Coding on Level Category Quant Table: Fixed on the Level with in the category Z. Li, Multimedia Communciation, 2018 p.3 Coding of AC Coefficients Zigzag scanning: Example 8 24 -2 0 0 0 0 0 -31 -4 6 -1 0 0 0 0 0 -12 -1 2 0 0 0 0 0 0 -2 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Example: zigzag scanning result 24 -31 0 -4 -2 0 6 -12 0 0 0 -1 -1 0 0 0 2 -2 0 0 0 0 0 -1 EOB (Run, level) representation: (0, 24), (0, -31), (1, -4), (0, -2), (1, 6), (0, -12), (3, -1), (0, -1), (3, 2), (0, -2), (5, -1), EOB Z. Li, Multimedia Communciation, 2018 p.4 Coding of AC Coefficients Run / Base Run / Base … Run / Base codeword Catg. codeword Catg. Codeword Cat. EOB 1010 - - … ZRL 1111 1111 001 0/1 00 1/1 1100 … 15/1 1111 1111 1111 0101 0/2 01 1/2 11011 … 15/2 1111 1111 1111 0110 0/3 100 1/3 1111001 … 15/3 1111 1111 1111 0111 0/4 1011 1/4 111110110 … 15/4 1111 1111 1111 1000 0/5 11010 1/5 11111110110 … 15/5 1111 1111 1111 1001 … … … … … … … ZRL: represent 16 zeros when number of zeros exceeds 15. -
Optimization Methods for Data Compression
OPTIMIZATION METHODS FOR DATA COMPRESSION A Dissertation Presented to The Faculty of the Graduate School of Arts and Sciences Brandeis University Computer Science James A. Storer Advisor In Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy by Giovanni Motta May 2002 This dissertation, directed and approved by Giovanni Motta's Committee, has been accepted and approved by the Graduate Faculty of Brandeis University in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY _________________________ Dean of Arts and Sciences Dissertation Committee __________________________ James A. Storer ____________________________ Martin Cohn ____________________________ Jordan Pollack ____________________________ Bruno Carpentieri ii To My Parents. iii ACKNOWLEDGEMENTS I wish to thank: Bruno Carpentieri, Martin Cohn, Antonella Di Lillo, Jordan Pollack, Francesco Rizzo, James Storer for their support and collaboration. I also thank Jeanne DeBaie, Myrna Fox, Julio Santana for making my life at Brandeis easier and enjoyable. iv ABSTRACT Optimization Methods for Data Compression A dissertation presented to the Faculty of the Graduate School of Arts and Sciences of Brandeis University, Waltham, Massachusetts by Giovanni Motta Many data compression algorithms use ad–hoc techniques to compress data efficiently. Only in very few cases, can data compressors be proved to achieve optimality on a specific information source, and even in these cases, algorithms often use sub–optimal procedures in their execution. It is appropriate to ask whether the replacement of a sub–optimal strategy by an optimal one in the execution of a given algorithm results in a substantial improvement of its performance. Because of the differences between algorithms the answer to this question is domain dependent and our investigation is based on a case–by–case analysis of the effects of using an optimization procedure in a data compression algorithm.