Psychoacoustics of Multichannel Audio
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NOISE-CON 2004 the Impact of A-Weighting Sound Pressure Level
Baltimore, Maryland NOISE-CON 2004 2004 July 12-14 The Impact of A-weighting Sound Pressure Level Measurements during the Evaluation of Noise Exposure Richard L. St. Pierre, Jr. RSP Acoustics Westminster, CO 80021 Daniel J. Maguire Cooper Standard Automotive Auburn, IN 46701 ABSTRACT Over the past 50 years, the A-weighted sound pressure level (dBA) has become the predominant measurement used in noise analysis. This is in spite of the fact that many studies have shown that the use of the A-weighting curve underestimates the role low frequency noise plays in loudness, annoyance, and speech intelligibility. The intentional de-emphasizing of low frequency noise content by A-weighting in studies can also lead to a misjudgment of the exposure risk of some physical and psychological effects that have been associated with low frequency noise. As a result of this reliance on dBA measurements, there is a lack of importance placed on minimizing low frequency noise. A review of the history of weighting curves as well as research into the problems associated with dBA measurements will be presented. Also, research relating to the effects of low frequency noise, including increased fatigue, reduced memory efficiency and increased risk of high blood pressure and heart ailments, will be analyzed. The result will show a need to develop and utilize other measures of sound that more accurately represent the potential risk to humans. 1. INTRODUCTION Since the 1930’s, there have been large advances in the ability to measure sound and understand its effects on humans. Despite this, a vast majority of acoustical measurements done today still use the methods originally developed 70 years ago. -
A Thesis Entitled Nocturnal Bird Call Recognition System for Wind Farm
A Thesis entitled Nocturnal Bird Call Recognition System for Wind Farm Applications by Selin A. Bastas Submitted to the Graduate Faculty as partial fulfillment of the requirements for the Master of Science Degree in Electrical Engineering _______________________________________ Dr. Mohsin M. Jamali, Committee Chair _______________________________________ Dr. Junghwan Kim, Committee Member _______________________________________ Dr. Sonmez Sahutoglu, Committee Member _______________________________________ Dr. Patricia R. Komuniecki, Dean College of Graduate Studies The University of Toledo December 2011 Copyright 2011, Selin A. Bastas. This document is copyrighted material. Under copyright law, no parts of this document may be reproduced without the expressed permission of the author. An Abstract of Nocturnal Bird Call Recognition System for Wind Farm Applications by Selin A. Bastas Submitted to the Graduate Faculty as partial fulfillment of the requirements for the Master of Science Degree in Electrical Engineering The University of Toledo December 2011 Interaction of birds with wind turbines has become an important public policy issue. Acoustic monitoring of birds in the vicinity of wind turbines can address this important public policy issue. The identification of nocturnal bird flight calls is also important for various applications such as ornithological studies and acoustic monitoring to prevent the negative effects of wind farms, human made structures and devices on birds. Wind turbines may have negative impact on bird population. Therefore, the development of an acoustic monitoring system is critical for the study of bird behavior. This work can be employed by wildlife biologist for developing mitigation techniques for both on- shore/off-shore wind farm applications and to address bird strike issues at airports. -
The Articulatory and Acoustic Characteristics of Polish Sibilants and Their Consequences for Diachronic Change
The articulatory and acoustic characteristics of Polish sibilants and their consequences for diachronic change Veronique´ Bukmaier & Jonathan Harrington Institute of Phonetics and Speech Processing, University of Munich, Germany [email protected] [email protected] The study is concerned with the relative synchronic stability of three contrastive sibilant fricatives /s ʂɕ/ in Polish. Tongue movement data were collected from nine first-language Polish speakers producing symmetrical real and non-word CVCV sequences in three vowel contexts. A Gaussian model was used to classify the sibilants from spectral information in the noise and from formant frequencies at vowel onset. The physiological analysis showed an almost complete separation between /s ʂɕ/ on tongue-tip parameters. The acoustic analysis showed that the greater energy at higher frequencies distinguished /s/ in the fricative noise from the other two sibilant categories. The most salient information at vowel onset was for /ɕ/, which also had a strong palatalizing effect on the following vowel. Whereas either the noise or vowel onset was largely sufficient for the identification of /s ɕ/ respectively, both sets of cues were necessary to separate /ʂ/ from /s ɕ/. The greater synchronic instability of /ʂ/ may derive from its high articulatory complexity coupled with its comparatively low acoustic salience. The data also suggest that the relatively late stage of /ʂ/ acquisition by children may come about because of the weak acoustic information in the vowel for its distinction from /s/. 1 Introduction While there have been many studies in the last 30 years on the acoustic (Evers, Reetz & Lahiri 1998, Jongman, Wayland & Wong 2000,Nowak2006, Shadle 2006, Cheon & Anderson 2008, Maniwa, Jongman & Wade 2009), perceptual (McGuire 2007, Cheon & Anderson 2008,Li et al. -
Decibels, Phons, and Sones
Decibels, Phons, and Sones The rate at which sound energy reaches a Table 1: deciBel Ratings of Several Sounds given cross-sectional area is known as the Sound Source Intensity deciBel sound intensity. There is an abnormally Weakest Sound Heard 1 x 10-12 W/m2 0.0 large range of intensities over which Rustling Leaves 1 x 10-11 W/m2 10.0 humans can hear. Given the large range, it Quiet Library 1 x 10-9 W/m2 30.0 is common to express the sound intensity Average Home 1 x 10-7 W/m2 50.0 using a logarithmic scale known as the Normal Conversation 1 x 10-6 W/m2 60.0 decibel scale. By measuring the intensity -4 2 level of a given sound with a meter, the Phone Dial Tone 1 x 10 W/m 80.0 -3 2 deciBel rating can be determined. Truck Traffic 1 x 10 W/m 90.0 Intensity values and decibel ratings for Chainsaw, 1 m away 1 x 10-1 W/m2 110.0 several sound sources listed in Table 1. The decibel scale and the intensity values it is based on is an objective measure of a sound. While intensities and deciBels (dB) are measurable, the loudness of a sound is subjective. Sound loudness varies from person to person. Furthermore, sounds with equal intensities but different frequencies are perceived by the same person to have unequal loudness. For instance, a 60 dB sound with a frequency of 1000 Hz sounds louder than a 60 dB sound with a frequency of 500 Hz. -
Guide for the Use of the International System of Units (SI)
Guide for the Use of the International System of Units (SI) m kg s cd SI mol K A NIST Special Publication 811 2008 Edition Ambler Thompson and Barry N. Taylor NIST Special Publication 811 2008 Edition Guide for the Use of the International System of Units (SI) Ambler Thompson Technology Services and Barry N. Taylor Physics Laboratory National Institute of Standards and Technology Gaithersburg, MD 20899 (Supersedes NIST Special Publication 811, 1995 Edition, April 1995) March 2008 U.S. Department of Commerce Carlos M. Gutierrez, Secretary National Institute of Standards and Technology James M. Turner, Acting Director National Institute of Standards and Technology Special Publication 811, 2008 Edition (Supersedes NIST Special Publication 811, April 1995 Edition) Natl. Inst. Stand. Technol. Spec. Publ. 811, 2008 Ed., 85 pages (March 2008; 2nd printing November 2008) CODEN: NSPUE3 Note on 2nd printing: This 2nd printing dated November 2008 of NIST SP811 corrects a number of minor typographical errors present in the 1st printing dated March 2008. Guide for the Use of the International System of Units (SI) Preface The International System of Units, universally abbreviated SI (from the French Le Système International d’Unités), is the modern metric system of measurement. Long the dominant measurement system used in science, the SI is becoming the dominant measurement system used in international commerce. The Omnibus Trade and Competitiveness Act of August 1988 [Public Law (PL) 100-418] changed the name of the National Bureau of Standards (NBS) to the National Institute of Standards and Technology (NIST) and gave to NIST the added task of helping U.S. -
Detection and Classification of Whale Acoustic Signals
Detection and Classification of Whale Acoustic Signals by Yin Xian Department of Electrical and Computer Engineering Duke University Date: Approved: Loren Nolte, Supervisor Douglas Nowacek (Co-supervisor) Robert Calderbank (Co-supervisor) Xiaobai Sun Ingrid Daubechies Galen Reeves Dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Electrical and Computer Engineering in the Graduate School of Duke University 2016 Abstract Detection and Classification of Whale Acoustic Signals by Yin Xian Department of Electrical and Computer Engineering Duke University Date: Approved: Loren Nolte, Supervisor Douglas Nowacek (Co-supervisor) Robert Calderbank (Co-supervisor) Xiaobai Sun Ingrid Daubechies Galen Reeves An abstract of a dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Electrical and Computer Engineering in the Graduate School of Duke University 2016 Copyright c 2016 by Yin Xian All rights reserved except the rights granted by the Creative Commons Attribution-Noncommercial Licence Abstract This dissertation focuses on two vital challenges in relation to whale acoustic signals: detection and classification. In detection, we evaluated the influence of the uncertain ocean environment on the spectrogram-based detector, and derived the likelihood ratio of the proposed Short Time Fourier Transform detector. Experimental results showed that the proposed detector outperforms detectors based on the spectrogram. The proposed detector is more sensitive to environmental changes because it includes phase information. In classification, our focus is on finding a robust and sparse representation of whale vocalizations. Because whale vocalizations can be modeled as polynomial phase sig- nals, we can represent the whale calls by their polynomial phase coefficients. -
The Human Ear Hearing, Sound Intensity and Loudness Levels
UIUC Physics 406 Acoustical Physics of Music The Human Ear Hearing, Sound Intensity and Loudness Levels We’ve been discussing the generation of sounds, so now we’ll discuss the perception of sounds. Human Senses: The astounding ~ 4 billion year evolution of living organisms on this planet, from the earliest single-cell life form(s) to the present day, with our current abilities to hear / see / smell / taste / feel / etc. – all are the result of the evolutionary forces of nature associated with “survival of the fittest” – i.e. it is evolutionarily{very} beneficial for us to be able to hear/perceive the natural sounds that do exist in the environment – it helps us to locate/find food/keep from becoming food, etc., just as vision/sight enables us to perceive objects in our 3-D environment, the ability to move /locomote through the environment enhances our ability to find food/keep from becoming food; Our sense of balance, via a stereo-pair (!) of semi-circular canals (= inertial guidance system!) helps us respond to 3-D inertial forces (e.g. gravity) and maintain our balance/avoid injury, etc. Our sense of taste & smell warn us of things that are bad to eat and/or breathe… Human Perception of Sound: * The human ear responds to disturbances/temporal variations in pressure. Amazingly sensitive! It has more than 6 orders of magnitude in dynamic range of pressure sensitivity (12 orders of magnitude in sound intensity, I p2) and 3 orders of magnitude in frequency (20 Hz – 20 KHz)! * Existence of 2 ears (stereo!) greatly enhances 3-D localization of sounds, and also the determination of pitch (i.e. -
The Decibel Scale
Projectile motion - 1 VCE Physics.com The decibel scale • Sound intensity • Sound level: The decibel scale • Sound level calculations • Some decibel measurements • Change in intensity • Human hearing • The Phon scale The decibel scale - 2 VCE Physics.com Sound intensity • The intensity of sound decreases with the square of the distance from the source. power P 2 Sound intensity = I = (unit =W /m ) A area 2 I r P 2 = 1 I = 2 4πr I1 r2 The decibel scale - 3 VCE Physics.com Sound intensity • eg a sound source emitting a power of 1 mW, when heard at a distance of 1 vs 2 m P 1×10−3W I = = = 8×10−5W /m2 A 4π 1m 2 ( ) P 1×10−3W I = = =2×10−5W /m2 A 4π (2m)2 The decibel scale - 4 VCE Physics.com Sound level: The decibel scale • The decibel scale is a relative scale, based upon the threshold of -12 2 hearing I0 = 10 W/m . • It is a logarithmic scale, an increase of 10 corresponds to 10 times the intensity. • 20dB = 102 times, 30dB = 103 times the intensity. I L =10log I 0 L −12 10 2 I =10 W /m I L =10log −12 10 The decibel scale - 5 VCE Physics.com Sound level calculations • eg A sound has the intensity of 10-4 W/m2. This is a sound level of I 10−4 L =10log =10log =10log108 = 80dB I 10−12 0 eg The intensity of a 45 dB sound is L 45 −12 −12 10 10 −7.5 −8 2 I =10 =10 =10 =3.2×10 W /m What is the intensity of 120 dB? L 120 −12 −12 I =1010 =10 10 =100 =1W /m2 The decibel scale - 6 VCE Physics.com Some decibel measurements Source Intensity (W/m2) Sound level (dB) Threshold of hearing 10-12 0 dB Soft whisper 10-10 20 dB Quiet conversation 10-8 40 dB Loud conversation 10-6 60 dB Highway traffic 10-4 80 dB Tractor engine 10-2 100 dB Threshold of pain (Rock concert) 10-0 (1) 120 dB Jet engine (less than 50 m away) 102 (100) 140 dB Rocket launch (less than 500 m away) 104 (10,000) 160 dB + • Damage to hearing is due to both the sound level & exposure time. -
Music Information Retrieval Using Social Tags and Audio
A Survey of Audio-Based Music Classification and Annotation Zhouyu Fu, Guojun Lu, Kai Ming Ting, and Dengsheng Zhang IEEE Trans. on Multimedia, vol. 13, no. 2, April 2011 presenter : Yin-Tzu Lin (阿孜孜^.^) 2011/08 Types of Music Representation • Music Notation – Scores – Like text with formatting symbolic • Time-stamped events – E.g. Midi – Like unformatted text • Audio – E.g. CD, MP3 – Like speech 2 Image from: http://en.wikipedia.org/wiki/Graphic_notation Inspired by Prof. Shigeki Sagayama’s talk and Donald Byrd’s slide Intra-Song Info Retrieval Composition Arrangement probabilistic Music Theory Symbolic inverse problem Learning Score Transcription MIDI Conversion Accompaniment Melody Extraction Performer Structural Segmentation Synthesize Key Detection Chord Detection Rhythm Pattern Tempo/Beat Extraction Modified speed Onset Detection Modified timbre Modified pitch Separation Audio 3 Inspired by Prof. Shigeki Sagayama’s talk Inter-Song Info Retrieval • Generic-similar – Music Classification • Genre, Artist, Mood, Emotion… Music Database – Tag Classification(Music Annotation) – Recommendation • Specific-similar – Query by Singing/Humming – Cover Song Identification – Score Following 4 Classification Tasks • Genre Classification • Mood Classification • Artist Identification • Instrument Recognition • Music Annotation 5 Paper Outline • Audio Features – Low-level features – Middle-level features – Song-level feature representations • Classifiers Learning • Classification Task • Future Research Issues 6 Audio Features 7 Low-level Features -
Selecting Proper Features and Classifiers for Accurate Identification of Musical Instruments
International Journal of Machine Learning and Computing, Vol. 3, No. 2, April 2013 Selecting Proper Features and Classifiers for Accurate Identification of Musical Instruments D. M. Chandwadkar and M. S. Sutaone y The performance of the learning procedure is evaluated Abstract—Selection of effective feature set and proper by classifying new sound samples (cross-validation). classifier is a challenging task in problems where machine One of the most crucial aspects in the above procedure of learning techniques are used. In automatic identification of instrument classification is to find the right features [2] and musical instruments also it is very crucial to find the right set of classifiers. Most of the research on audio signal processing features and accurate classifier. In this paper, the role of various features with different classifiers on automatic has been focusing on speech recognition and speaker identification of musical instruments is discussed. Piano, identification. Few features used for these can be directly acoustic guitar, xylophone and violin are identified using applied to solve the instrument classification problem. various features and classifiers. Spectral features like spectral In this paper, feature extraction and selection for centroid, spectral slope, spectral spread, spectral kurtosis, instrument classification using machine learning techniques spectral skewness and spectral roll-off are used along with autocorrelation coefficients and Mel Frequency Cepstral is considered. Four instruments: piano, acoustic guitar, Coefficients (MFCC) for this purpose. The dependence of xylophone and violin are identified using various features instrument identification accuracy on these features is studied and classifiers. A number of spectral features, MFCCs, and for different classifiers. Decision trees, k nearest neighbour autocorrelation coefficients are used. -
A-Weighting”: Is It the Metric You Think It Is?
Proceedings of Acoustics 2013 – Victor Harbor 17-20 November 2013, Victor Harbor, Australia “A-Weighting”: Is it the metric you think it is? McMinn, Terrance Curtin University of Technology ABSTRACT It is the generally accepted view that the “A-Weighting” (dBA) curve mimics human hearing to measure relative loudness. However it is not widely appreciated that the “A-Weighting” curve lacks validity especially at low frequen- cies (below 100 Hz) and for sounds above 60 dB. Research in the field of equal loudness has progressed and re- defined the shape of the original 40 phon Munson and Fletcher equal loudness curves. Unfortunately, the “A-Weighting” curve has not been revised in the light of this research or ISO 226. This paper highlights some of the changes in the research and problems identified with “A-Weighting” as it has come into current usage. INTRODUCTION The published paper smoothed the data from a number of The A Weighting was introduced in sound level meters based tests and then curve fitted to produce the figures below. on the American Tentative Standards for Sound Level Meters There is no information in the paper identifying the method Z24.3-1936 for Measurement of Noise and Other Sounds or justification for extrapolation of the curves beyond the test (Cited in Pierre and Maguire 2004). This standard adopted frequency range. the 40 decibel loudness levels of Fletcher and Munson to establish the “Curve A” (A-Weighting) and 70 decibel for the “Curve B” (B-Weighting). However it should be recognized that over time the usage of the “A-Weighting” has changed from that intended in Z24.3-1936. -
Loudness Level
Loudness level This article was checked by pedagogue This article ws checked by pedagogue, but later was changed. Checked version of the article can be found here (http s://www.wikilectures.eu/index.php?title=Loudness_leve l&oldid=18545). See also comparation of actual and checked version (https:// www.wikilectures.eu/index.php?title=Loudness_level&diff= 21411&oldid=18545). This article was checked by pedagogue This article was checked by pedagogue, but later was changed. What is loudness? This article was checked by pedagogue This article was checked by pedagogue, but later was changed. Loudness is the certain criterion of a sound, which correlate with physical strength of sound(amplitude). Loudness also can scaling a sound, extending from quiet to loud. Sometimes, loudness is confused with other measures of sound strength, such as sound pressure, sound intensity and sound power. However, loudness is much more complex than other types of sound strength. Besides, loudness is also affected by additional parameters other than sound pressure, for example, frequency, bandwidth and duration. Unit of loudness(level) Characteristic of loudness can be shown by certain graph, equal loudness curve. Loudness(or loudness level) can be measured by the phon. Phon The phon is a unit of loudness level for pure tones. Its purpose is to compensate for the effect of frequency on the perceived loudness of tones. This unit was proposed by S. S. Stevens. By definition, the number of phon of a sound is the dB SPL of a sound at a frequency of 1 kHz that sounds just as loud. This implies that 0 phon is the limit of perception, and inaudible sounds have negative phon levels.