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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. -
Design of Optimal Feedback Filters with Guaranteed Closed-Loop Stability for Oversampled Noise-Shaping Subband Quantizers
49th IEEE Conference on Decision and Control December 15-17, 2010 Hilton Atlanta Hotel, Atlanta, GA, USA Design of Optimal Feedback Filters with Guaranteed Closed-Loop Stability for Oversampled Noise-Shaping Subband Quantizers SanderWahls HolgerBoche. Abstract—We consider the oversampled noise-shaping sub- T T T T ] ] ] ] band quantizer (ONSQ) with the quantization noise being q q v q,q v q,q + Quant- + + + + modeled as white noise. The problem at hand is to design an q - q izer q q - q q ,...,w ,...,w optimal feedback filter. Optimal feedback filters with regard 1 1 ,...,v ,...,v to the current standard model of the ONSQ inhabit several + q,1 q,1 w=[w + w=[w =[v =[v shortcomings: the stability of the feedback loop is not ensured - q q v v and the noise is considered independent of both the input and u ŋ u ŋ -1 -1 the feedback filter. Another previously unknown disadvantage, q z F(z) q q z F(z) q which we prove in our paper, is that optimal feedback filters in the standard model are never unique. The goal of this paper is (a) Real model: η is the qnt. error (b) Linearized model: η is white noise to show how these shortcomings can be overcome. The stability issue is addressed by showing that every feedback filter can be Figure 1: Noise-Shaping Quantizer “stabilized” in the sense that we can find another feedback filter which stabilizes the feedback loop and achieves a performance w v E (z) p 1 q,1 p R (z) equal (or arbitrarily close) to the original filter. -
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. -
ESE 531: Digital Signal Processing
ESE 531: Digital Signal Processing Lec 12: February 21st, 2017 Data Converters, Noise Shaping (con’t) Penn ESE 531 Spring 2017 - Khanna Lecture Outline ! Data Converters " Anti-aliasing " ADC " Quantization " Practical DAC ! Noise Shaping Penn ESE 531 Spring 2017 - Khanna 2 ADC Penn ESE 531 Spring 2017 - Khanna 3 Anti-Aliasing Filter with ADC Penn ESE 531 Spring 2017 - Khanna 4 Oversampled ADC Penn ESE 531 Spring 2017 - Khanna 5 Oversampled ADC Penn ESE 531 Spring 2017 - Khanna 6 Oversampled ADC Penn ESE 531 Spring 2017 - Khanna 7 Oversampled ADC Penn ESE 531 Spring 2017 - Khanna 8 Sampling and Quantization Penn ESE 531 Spring 2017 - Khanna 9 Sampling and Quantization Penn ESE 531 Spring 2017 - Khanna 10 Effect of Quantization Error on Signal ! Quantization error is a deterministic function of the signal " Consequently, the effect of quantization strongly depends on the signal itself ! Unless, we consider fairly trivial signals, a deterministic analysis is usually impractical " More common to look at errors from a statistical perspective " "Quantization noise” ! Two aspects " How much noise power (variance) does quantization add to our samples? " How is this noise distributed in frequency? Penn ESE 531 Spring 2017 - Khanna 11 Quantization Error ! Model quantization error as noise ! In that case: Penn ESE 531 Spring 2017 - Khanna 12 Ideal Quantizer ! Quantization step Δ ! Quantization error has sawtooth shape, ! Bounded by –Δ/2, +Δ/2 ! Ideally infinite input range and infinite number of quantization levels Penn ESE 568 Fall 2016 - Khanna adapted from Murmann EE315B, Stanford 13 Ideal B-bit Quantizer ! Practical quantizers have a limited input range and a finite set of output codes ! E.g. -
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. -
A Subjective Evaluation of Noise-Shaping Quantization for Adaptive
332 IEEE TRANSACTIONS ON COMMUNICATIONS, VOL. 36, NO. 3, MARCH 1988 A Subjective Evaluation of Noise-S haping Quantization for Adaptive Intra-/Interframe DPCM Coding of Color Television Signals BERND GIROD, H AKAN ALMER, LEIF BENGTSSON, BJORN CHRISTENSSON, AND PETER WEISS Abstract-Nonuniform quantizers for just not visible reconstruction quantizers for just not visible eri-ors have been presented in errors in an adaptive intra-/interframe DPCM scheme for component- [12]-[I41 for the luminance and in [IS], [I61 for the color coded color television signals are presented, both for conventional DPCM difference signals. An investigation concerning the visibility and for noise-shaping DPCM. Noise feedback filters that minimize the of quantization errors for an adaiptive intra-/interframe lumi- visibility of reconstruction errors by spectral shaping are designed for Y, nance DPCM coder has been reported by Westerkamp [17]. R-Y, and B-Y. A closed-form description of the “masking function” is In this paper, we present quantization characteristics for the derived which leads to the one-parameter “6 quantizer” characteristic. luminance and the color dilference signals that lead to just not Subjective tests that were carried out to determine visibility thresholds for visible reconstruction errors in sin adaptive intra-/interframe reconstruction errors for conventional DPCM and for noise shaping DPCM scheme. These quantizers have been determined by DPCM show significant gains by noise shaping. For a transmission rate means of subjective tests. In order to evaluate the improve- of around 30 Mbitsls, reconstruction errors are almost always below the ments that can be achieved by reconstruction noise shaping visibility threshold if variable length encoding of the prediction error is [l 11, we compare the quantization characteristics for just not combined with noise shaping within a 3:l:l system. -
Quantization Noise Shaping on Arbitrary Frame Expansions
Hindawi Publishing Corporation EURASIP Journal on Applied Signal Processing Volume 2006, Article ID 53807, Pages 1–12 DOI 10.1155/ASP/2006/53807 Quantization Noise Shaping on Arbitrary Frame Expansions Petros T. Boufounos and Alan V. Oppenheim Digital Signal Processing Group, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Room 36-615, Cambridge, MA 02139, USA Received 2 October 2004; Revised 10 June 2005; Accepted 12 July 2005 Quantization noise shaping is commonly used in oversampled A/D and D/A converters with uniform sampling. This paper consid- ers quantization noise shaping for arbitrary finite frame expansions based on generalizing the view of first-order classical oversam- pled noise shaping as a compensation of the quantization error through projections. Two levels of generalization are developed, one a special case of the other, and two different cost models are proposed to evaluate the quantizer structures. Within our framework, the synthesis frame vectors are assumed given, and the computational complexity is in the initial determination of frame vector ordering, carried out off-line as part of the quantizer design. We consider the extension of the results to infinite shift-invariant frames and consider in particular filtering and oversampled filter banks. Copyright © 2006 P. T. Boufounos and A. V. Oppenheim. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 1. INTRODUCTION coefficient through a projection onto the space defined by another synthesis frame vector. This requires only knowl- Quantization methods for frame expansions have received edge of the synthesis frame set and a prespecified order- considerable attention in the last few years. -
Color Error Diffusion with Generalized Optimum Noise
COLOR ERROR DIFFUSION WITH GENERALIZED OPTIMUM NOISE SHAPING Niranjan Damera-Venkata Brian L. Evans Halftoning and Image Pro cessing Group Emb edded Signal Pro cessing Lab oratory Hewlett-Packard Lab oratories Dept. of Electrical and Computer Engineering 1501 Page Mill Road The University of Texas at Austin Palo Alto, CA 94304 Austin, TX 78712-1084 E-mail: [email protected] E-mail: [email protected] In this pap er, we derive the optimum matrix-valued er- ABSTRACT ror lter using the matrix gain mo del [3 ] to mo del the noise We optimize the noise shaping b ehavior of color error dif- shaping b ehavior of color error di usion and a generalized fusion by designing an optimized error lter based on a linear spatially-invariant mo del not necessarily separable prop osed noise shaping mo del for color error di usion and for the human color vision. We also incorp orate the con- a generalized linear spatially-invariant mo del of the hu- straint that all of the RGB quantization error b e di used. man visual system. Our approach allows the error lter We show that the optimum error lter may be obtained to have matrix-valued co ecients and di use quantization as a solution to a matrix version of the Yule-Walker equa- error across channels in an opp onent color representation. tions. A gradient descent algorithm is prop osed to solve the Thus, the noise is shap ed into frequency regions of reduced generalized Yule-Walker equations. In the sp ecial case that human color sensitivity. -
Noise Shaping
INF4420 ΔΣ data converters Spring 2012 Jørgen Andreas Michaelsen ([email protected]) Outline Oversampling Noise shaping Circuit design issues Higher order noise shaping Introduction So far we have considered so called Nyquist data converters. Quantization noise is a fundamental limit. Improving the resolution of the converter, translates to increasing the number of quantization steps (bits). Requires better -1 N+1 component matching, AOL > β 2 , and N+1 -1 -1 GBW > fs ln 2 π β . Introduction ΔΣ modulator based data converters relies on oversampling and noise shaping to improve the resolution. Oversampling means that the data rate is increased to several times what is required by the Nyquist sampling theorem. Noise shaping means that the quantization noise is moved away from the signal band that we are interested in. Introduction We can make a high resolution data converter with few quantization steps! The most obvious trade-off is the increase in speed and more complex digital processing. However, this is a good fit for CMOS. We can apply this to both DACs and ADCs. Oversampling The total quantization noise depends only on the number of steps. Not the bandwidth. If we increase the sampling rate, the quantization noise will not increase and it will spread over a larger area. The power spectral density will decrease. Oversampling Take a regular ADC and run it at a much higher speed than twice the Nyquist frequency. Quantization noise is reduced because only a fraction remains in the signal bandwidth, fb. Oversampling Doubling the OSR improves SNR by 0.5 bit Increasing the resolution by oversampling is not practical. -
Sonically Optimized Noise Shaping Techniques
Sonically Optimized Noise Shaping Techniques Second edition Alexey Lukin [email protected] 2002 In this article we compare different commercially available noise shaping algo- rithms and introduce a new highly optimized frequency response curve for noise shap- ing filter implemented in our ExtraBit Mastering Processor. Contents INTRODUCTION ......................................................................................................................... 2 TESTED NOISE SHAPERS ......................................................................................................... 2 CRITERIONS OF COMPARISON............................................................................................... 3 RESULTS...................................................................................................................................... 4 NOISE STATISTICS & PERCEIVED LOUDNESS .............................................................................. 4 NOISE SPECTRUMS ..................................................................................................................... 5 No noise shaping (TPDF dithering)..................................................................................... 5 Cool Edit (C1 curve) ............................................................................................................5 Cool Edit (C3 curve) ............................................................................................................6 POW-R (pow-r1 mode)........................................................................................................ -
Clock Jitter Effects on the Performance of ADC Devices
Clock Jitter Effects on the Performance of ADC Devices Roberto J. Vega Luis Geraldo P. Meloni Universidade Estadual de Campinas - UNICAMP Universidade Estadual de Campinas - UNICAMP P.O. Box 05 - 13083-852 P.O. Box 05 - 13083-852 Campinas - SP - Brazil Campinas - SP - Brazil [email protected] [email protected] Karlo G. Lenzi Centro de Pesquisa e Desenvolvimento em Telecomunicac¸oes˜ - CPqD P.O. Box 05 - 13083-852 Campinas - SP - Brazil [email protected] Abstract— This paper aims to demonstrate the effect of jitter power near the full scale of the ADC, the noise power is on the performance of Analog-to-digital converters and how computed by all FFT bins except the DC bin value (it is it degrades the quality of the signal being sampled. If not common to exclude up to 8 bins after the DC zero-bin to carefully controlled, jitter effects on data acquisition may severely impacted the outcome of the sampling process. This analysis avoid any spectral leakage of the DC component). is of great importance for applications that demands a very This measure includes the effect of all types of noise, the good signal to noise ratio, such as high-performance wireless distortion and harmonics introduced by the converter. The rms standards, such as DTV, WiMAX and LTE. error is given by (1), as defined by IEEE standard [5], where Index Terms— ADC Performance, Jitter, Phase Noise, SNR. J is an exact integer multiple of fs=N: I. INTRODUCTION 1 s X = jX(k)j2 (1) With the advance of the technology and the migration of the rms N signal processing from analog to digital, the use of analog-to- k6=0;J;N−J digital converters (ADC) became essential. -
Quantization Noise Shaping for Information Maximizing Adcs Arthur J
1 Quantization Noise Shaping for Information Maximizing ADCs Arthur J. Redfern and Kun Shi Abstract—ADCs sit at the interface of the analog and dig- be done via changing the constant bits vs. frequency profile ital worlds and fundamentally determine what information is for delta sigma ADCs on a per channel basis [3], projecting available in the digital domain for processing. This paper shows the received signal on a basis optimized for the signal before that a configurable ADC can be designed for signals with non constant information as a function of frequency such that within conversion [4], [9] and by allocating more ADCs to bands a fixed power budget the ADC maximizes the information in the where the signal has a higher variance [10]. Efficient shaping converted signal by frequency shaping the quantization noise. for these cases relies on having a sufficiently large number Quantization noise shaping can be realized via loop filter design of ADCs such that the SNR of the received signal does not for a single channel delta sigma ADC and extended to common change significantly within the band converted by the 1 of the time and frequency interleaved multi channel structures. Results are presented for example wireline and wireless style channels. individual constant bit vs. frequency ADCs which comprise the multi channel ADC structure. Index Terms—ADC, quantization noise, shaping. Compressive sensing ADCs provide an implicit example of bits vs. frequency shaping for cases when the input signal I. INTRODUCTION is sparse in frequency and the sampling rate is proportional to the occupied signal bandwidth rather than the total system T’S common for electronic devices to operate with con- bandwidth [5].