Introduction to Acoustics

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17.10 Composition The history of computer languages for music dates back well into the 1950s. Beginning with Max Mathew’s Music I, many features of these languages have remained constant, such as the notion of unit generators as discussed before, and the idea of instruments within an orchestra, controlled by a score. Early popular languages include Music V (a direct descendent of Music I) [17.5], Csound, CMix, and CMusic (written in C) [17.43]. More recent languages developed in the last decade include SuperCollider [17.44], Jsyn (in Java) [17.45], STK in C ++ [17.46], Aura [17.47], Nyquist [17.48], and ChucK [17.49]. One class of languages that has advanced education and participation in computer music are graphical systems such as MAX/MSP [17.50] andPureData (PD) [17.51]. These software systems allow novice (and expert) users to construct patches by dragging unit generator icons around on the desktop, connecting them with virtual patch cords (Fig. 17.47). 17.11 Controllers and Performance Systems Music performance is structured real-time control of sound, to achieve an aesthetic goal (hopefully for both the performers and audience). So one obvious application area for real-time computer sound synthesis and processing is in creating new forms of musical expression. Exciting developments in the last few years have emerged in the area of new controllers and systems for using parametric sound in real-time music performance, or in interactive installation art. Figure 17.48 shows three recent real-time music performance systems, all using parametric digital sound synthesis and a variety of sensors mounted on Fig. 17.48 Cook’s DigitalDoo, Trueman’s BoSSA, Bahn’s ‘r!g’ Computer Music 17.11 Controllers and Performance Systems 737 Fig. 17.47 A simple MAX/MSP patch connects a microphone through a 1/2 s (22050 samples at 44.1 k sample rate) delay to the speaker output the instruments and players. The author’s DigitalDoo is a sensor/speaker enhanced/augmented didgeridoo. Dan Trueman’s bowed sensor speaker array (BoSSA) resulted from work in modeling, refining, and redefining the violin player’s interface [17.52]. Curtis Bahn’s ‘r!g’ consists of a sensor enhanced upright (stick) bass, pedal boards, and other controllers. All three of these new instruments use spherical speakers to allow flexible control of the sound radiation into the performance space [17.53]. These instruments have been used with computers to control percussion sounds, animal noises, string models, flute models, and a large va- Part E 17.11
738 Part E Music, Speech, Electroacoustics Part E 17.12 Fig. 17.49 Gigapop networked concert between Princeton (shown on screen to left) and Montreal (players on stage). The right screen shows real-time graphics generated from gestural data captured from sensors on the instruments riety of other sample-based and parametric synthesis algorithms. Various conducting systems, including Max Mathew’s radio baton (Stanford’s Center for Computer Research in Music and Acoustics) [17.54], and Theresa Marrin Nakra’s conducting jacket (MIT Media 17.12 Music Understanding and Modeling by Computer Over the years, new research and applications have arisen in the areas of content-based audio analysis, retrieval, and modeling. Also called music information retrieval (MIR), machine listening, and other terms, the main tasks involve segmenting, separating, classifying, and modeling the textures and structures present in pieces of music or audio. Audio segmentation is the process of breaking up an audio stream into sections that are perceptually different from adjacent sections. The audio texture within a given segment is relatively stable. Examples of segment boundaries could be a transition from background sound texture to a human beginning to speak over that background. Another segment boundary might occur when the scene changes, such as leaving an office building lobby and going outside onto a busy street. In movie production, the audio scene often changes just prior to the visual scene, allowing a noncausal mode of detecting a visual scene change. Segmentation [17.60] can be accomplished in two primary ways: blind segmentation based on sudden changes in extracted audio features, and classification- Lab) [17.55] allow novices and experts to conduct music performed by the computer. Also, the growth and development of computer music has helped to advance greatly the technological capabilities of the familiar karaoke industry. Some modern karaoke systems now can deduce the song that is being sung and retrieve the appropriate accompaniment within just a few notes. Also, these systems can automatically correct the pitch and vocal qualities of a bad singer, making them sound more expert and professional. The next section will describe some of the algorithms being applied now in karaoke and other automatic accompaniment systems. Some researchers and performers feel that there is significant potential in networked computer music performances and interactions. Projects range from MIT’s Brain Opera [17.56], which allowed observers to participate and contribute content to the performance via the web, through the Auricle website, which is a kind of audio analysis/synthesis enhanced chat room [17.57]. Stanford’s SoundWire Project [17.58] and Princeton’s GigaPop Project [17.59] (Fig.17.49) are aimed at synchronized multi-site live performance through high-bandwidth low-latency streaming of uncompressed audio and video. based segmentation based on comparing audio features to a set of trained target feature values. The blind method works well and is preferred when the segment textures are varied and unpredictable, but requires the setting of thresholds to yield the best results. The classificationbased method works well on a corpus of pre-labeled data, such as speech/music, musical genres, indoor/outdoor scenes, etc. databases. Both methods require the extraction of audio features. Audio feature extraction is the process of computing a compact numerical representation of a sound segment. A variety of audio features exist, and have been used in systems for speech recognition, music/speech discrimination, musical genre (rock, pop, country, classical, jazz, etc.) labeling, and other audio classification tasks. Most features are extracted from short moving windows (5–100 ms in length, moving along at a rate of 5–20 windows per second) by using the short-time Fourier transform, wavelets, or compressed data coefficients (such as MP3). Each feature described below can be computed at different time resolutions, and the value of each feature, along with the mean
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Springer Handbook of Acoustics
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Springer Handbook of Acoustics Thom
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Foreword The present handbook cover
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List of Authors Iskander Akhatov No
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Philippe Roux Université Joseph Fo
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XIV Contents 3.7 Attenuation of Sou
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XVI Contents 10 Concert Hall Acoust
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XVIII Contents 17.8 Physical Modeli
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XX Contents 24.5 Free-Field Microph
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XXII List of Abbreviations K KDP po
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Introduction 1. Introduction to Aco
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of noise have been the subject of c
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in order to understand how objectiv
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A Brief 2. A Brief History of Acous
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of 350 m/s for the speed of sound [
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number and relative strength of its
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To his contemporaries, Koenig was p
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Probably the most important use of
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piezoelectric ceramic compositions
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surveys together [2.46]. Masking of
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ther of computer music, since he de
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Basic 3. Linear Basic Linear Acoust
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M average molecular weight n unit v
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a) b) S v.n ∆t ∆S V |v| ∆t n
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temperature, q =−κ∇T , (3.16)
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The equivalent density M/V is conse
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Basic Linear Acoustics 3.3 Equation
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Because any vector field may be dec
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The latter and (3.84), in a manner
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assumed to have no ambient motion,
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Because the friction associated wit
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3.5 Waves of Constant Frequency One
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3.5.4 Time Averages of Products Whe
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as well as symmetry considerations,
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The auxiliary internal variables th
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Then the transient at a distant pos
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ω ′ = 0, and this is consistent
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1.0 10 -1 10 -2 10 -3 10 -4 10 -5 A
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This yields the interpretation that
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direction of propagation when a pla
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so the time-averaged incident energ
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Although the simple result of (3.31
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ut, also in keeping with the linear
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equation, the two differential equa
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Rodrigues relation, one has �1
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for the derivative.) In the asympto
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The differential scattering cross s
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elow) is F(t) = (2c) 1/2 �t −
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0.5 0 -0.5 -1.0 -1.5 0 Y0(η) (π/2
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is the Wronskian for the Bessel and
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The function qS is termed the sourc
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The appropriate identification for
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where jℓ is the spherical Bessel
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this subtlety taken into account, o
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U(x) Basic Linear Acoustics 3.15 Wa
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is ordinarily valid. With these ass
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along rays. These can be regarded a
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A(x0) x0 A(x) Fig. 3.53 Sketch of a
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possible, and one where neither the
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which is independent of the z-coord
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[3.108], and Carslaw [3.109]. In th
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where C(X)andS(X) are the Fresnel i
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Fig. 3.60 Characteristic diffractio
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of elastic solids, Trans. Camb. Phi
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3.101 J.B. Keller: Geometrical acou
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114 Part A Propagation of Sound Par
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Underwater 5. Underwater Acoustics
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5.1 Ocean Acoustic Environment The
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Long-Range Propagation Paths Figure
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tal direction, there is no loss ass
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equivalent circuit, as shown in Fig
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Attenuation á (dB/km) 1000 100 10
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5.2.5 Ambient Noise There are essen
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ubble natural acoustic resonance ω
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a bubbly medium (and for the simple
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As discussed, because detection inv
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(1/A)∇ 2 A ≪ K 2 )sothat(5.34)
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water, where the deep sound channel
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egion in the form p(r, z) = ψ(r, z
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5.4.6 Propagation and Transmission
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tegration interval. The source puls
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5.6 SONAR Array Processing Signal p
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former output is: �∞ b(θ,t) =
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Fig. 5.37 Angle-versus-time represe
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5.7 Active SONAR Processing Depth M
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a factor when there is a reverberan
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depth measurements that correspond
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along the cross-shelf track taken b
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time as a result of multiple paths,
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technique is very sensitive, and ex
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Fig. 5.57a,b Typical echogram obtai
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Frequency (Hz) 150 100 50 0 0 a) b)
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out. These data allow for study of
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5.59 G. Raleigh, J.M. Cioffi: Spati
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Physical 6. Physical Acou Acoustics
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where I is the intensity of the sou
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Wave velocity The wall exerts a dow
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destructively interfered with one a
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or: � � p2 � rms SPL = 10 log
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6.1.4 Wave Propagation in Solids Si
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where c is again the wave speed and
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measured. Some resonances are cause
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as 50 µmto5µm through one oscilla
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the number of false positives and m
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with the small mass), and frequency
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Laser Lens 1 Circular aperture Fig.
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Ground ring Insulator Sample Resist
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Energy ratio 1.0 0.8 0.6 0.4 Calcul
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terms. In this equation γ is the r
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directly. The nonlinearity paramete
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Thermoacoust 7. Thermoacoustics The
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Table 7.1 The acoustic-electric ana
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walls of the pores. (Positive G ind
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a) b) c) C reso d) δtherm e) p 0 U
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a) b) UA,l c) d) E 0 Q A Q A TA TA
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tor. This eliminates the need to le
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to this consumption of acoustic pow
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The traditional Stirling refrigerat
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Washington 1986) pp. 550-554, Softw
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258 Part B Physical and Nonlinear A
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Acoustics 9. Acoustics in Halls for
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parameters, so we can assist in bui
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weeks or even years is less reliabl
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9.3.1 Reverberation Time Reverberan
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selected). A distance different fro
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ing sound, as will naturally be exp
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of the sound fields. Consequently,
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e derived from interrupted noise de
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Acoustics in Halls for Speech and M
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espectively, can be calculated from
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ated by the architects. However, on
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of C and G. However, as all the ind
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F b d F n I S Fb Fn S d F n/F b d/d
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HS S èn ã d0 P0 rn Position of ey
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Time T (s) 2.5 2 1.5 1 5 10 15 Acou
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floor can be tilted to reduce volum
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ÄLcurv (dB) 10 8 6 4 2 0 -2 -4 -6
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Seating capacity 2662 1 915 + 324 2
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4 3 2 1 Acoustics in Halls for Spee
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cially in auditoria with T values l
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esonance (AR)andmultichannel reverb
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Concert 10. Concert Hall Acoustics
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Concert Hall Acoustics Based on Sub
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0 0 Concert Hall Acoustics Based on
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mately by Concert Hall Acoustics Ba
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10.1.5 Specialization of Cerebral H
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The other (n − 1) bits indicated
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As for the conflicting requirements
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have mainly been concerned with tem
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a) c) S S -4.3 -2.8 -2.0 -3.5 -3dB
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a model of the auditory-brain syste
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Building 11. Building Acou Acoustic
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Pressure Maximum Minimum 0 D Distan
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Table 11.1 Absorption coefficients
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Absorption coefficient α 1 0 Frequ
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is of key importance in rooms where
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Table 11.4 Transmission loss and ST
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TL of wall - (TL of door, window or
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Table 11.7 Generalized noise reduct
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Masking sound pressure level (dB) 5
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As for the room criterion (RC) meth
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11.5 Noise Control Methods for Buil
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Neoprene pads (30 durometer) with a
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Concrete Caulk around perimeter Vib
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Trim board to conceal gap (fasten o
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Gypsum board partition (as schedule
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Double-layer ribbed or waffle neopr
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11.6 Acoustical Privacy in Building
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Table 11.9 AI,SIIandPIforopenplanof
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Electronic sound masking in plenum
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E 1179 Standard Specification for S
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430 Part D Hearing and Signal Proce
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Psychoacoust 13. Psychoacoustics Ps
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Absolute threshold (dB SPL) 100 90
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the signal. By using this off-cente
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is as a crude indicator of the exci
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Relative response (dB) 100 90 80 70
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In a variation of this procedure, t
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Level of matching noise (dB) 100 90
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13.4 Temporal Processing in the Aud
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Most models include an initial stag
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The components were either uniforml
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(Frequency DL)/ERBN 0.2 0.1 0.05 0.
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elaborate place models have been pr
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13.6 Timbre Perception 13.6.1 Time-
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the duplex theory of sound localiza
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harmonic (by shifting the frequency
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sive. While some studies have been
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sentences (see Fig. 13.20). They va
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components is usually only perceive
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References 13.1 ISO 389-7: Acoustic
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13.74 D. Ronken: Monaural detection
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asymmetric function, J. Acoust. Soc
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13.211 J. Vliegen, A.J. Oxenham: Se
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534 Part E Music, Speech, Electroac
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The 16. The Human Human Voice in Sp
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uation, the effect of gravity is in
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piratory muscles (the internal inte
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Mean Ps (cm H2O) 50 40 30 20 10 0 D
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Glottal flow Closed Opening Open Cl
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Transglottal airflow (l/s) 0.4 0.2
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Mean spectrum level (dB) -30 -40 -5
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20 10 0 -10 -20 -30 -40 -50 0 i y u
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Mean level (dB) 0 -10 -20 -30 -40 1
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Page 746 and 747: Audio 18. Audio and Electroacoustic
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790 Part F Biological and Medical A
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Medical 21. Medical Acous Acoustics
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21.1 Introduction to Medical Acoust
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Auscultation location Right & left
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portance. The Strouhal number has b
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Fig. 21.3 Phono-cardiograph transdu
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Amplitude 8 6 4 2 0 -2 -4 -6 Displa
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λ1 = c1 / fus θ1 λ2 = c2 / fus
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lood flow can be resolved if the si
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vided in the image thickness direct
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not as predicted, because the wave
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Fig. 21.18a,b Quadrature Doppler de
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a) b) c) 100 0 V mV 10 0 a = 13 a =
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Ultrasound contact gel Position enc
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a) 10 cm b) c) 10 cm Fig. 21.32a-c
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a) Pin B Pin A Ultrasound line b) M
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Organ Patient Ultrasound transducer
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systems cannot tell the difference
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40 µm can be resolved. For a 10 kH
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a) b) c) d) Medical Acoustics 21.4
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Fig. 21.54 Doppler spectral wavefor
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10 20 30 10 20 30 Pre-exercise B-mo
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Vibrations in a punctured artery De
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the veins and diffuse into the inte
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21.5.3 Agitated Saline and Patent F
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transported by those cells and/or r
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1 0.8 0.6 0.4 0.2 0 Relative power
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High intensity focused ultrasound h
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a) b) c) Fig. 21.74a-c B-mode imagi
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provided simple metrics for the lik
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thickness of the carotid arteries,
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Structural 22. Structural Acoustics
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Structural Acoustics and Vibrations
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Magnitude (arb. units) 1.4 1.2 1.0
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This does not include the case wher
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the mass matrix is taken to be cons
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Magnitude (dB) -10 -30 -50 0 1 2 3
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where D(ω) = ω 4 − 2iω 3�
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form y(x, t) = � Φn(x)qn(t) . (2
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first to solve the wave equation (2
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5 3 1 -1 -3 -5 0 1 2 3 4 5 6 7 8 9
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conditions at each end) are necessa
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from which we can derive through (2
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In this case, the eigenfrequencies
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of radiation modes and their link w
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Finally, the displacement is ˜ξ(x
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notes the value of this eigenmode a
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Pm = ˙Q H [Rs + Ra] ˙Q , (22.262)
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where and Ra = 2ζaω0 M Z(s) = zL
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Radiated Power. The mean acoustic p
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(22.318) can be written ξ(x, y, t)
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Denoting the eigenfrequencies and e
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Magnitude (arb. units) 3 2 1 0 -1 -
Page 942 and 943:
for any real symmetric tensor Xij.
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F(N) 1 0.5 0 -0.5 -1 -1 -0.8 -0.6 -
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whose solution is θ1 = A cos ωτ.
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4.5 3.5 2.5 1.5 A 0.5 0 0.5 1.0 1.5
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Equations (22.423) are usually writ
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3. As the amplitude reaches the top
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Shallow Spherical Shells and Plates
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22.20 L. Cremer, M. Heckl: Structur
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Noise Noise is discussed in terms o
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where the overbars represent time a
Page 962 and 963:
Typical outdoor setting A-weighted
Page 964 and 965:
90 dB(A)” is widely used, and imp
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Microphone, amplifier and A/D conve
Page 968 and 969:
When each segment of the measuremen
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found from � LW = Lp + 10 log A +
Page 972 and 973:
surement method is the ultimate use
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An intensity analyzer is more compl
Page 976 and 977:
23.2.5 Criteria for Noise Emissions
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Table 23.5 Table of limit values fr
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a) Air flows freely to rotor Weak t
Page 982 and 983:
Static efficiency normalized to its
Page 984 and 985:
ful applications. Good results are
Page 986 and 987:
Table 23.8 Crossover speeds for var
Page 988 and 989:
Reduction of airplane engine noise
Page 990 and 991:
A variety of materials are used to
Page 992 and 993:
∆LF (dB) 0 -10 -20 α -30 0.05 0.
Page 994 and 995:
Absorption coefficient 1.0 0.8 0.6
Page 996 and 997:
high enough that there is little so
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23.4.4 Criteria for Noise Immission
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Table 23.10 (cont.) Some features o
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Noise 23.4 Noise and the Receiver 1
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view of administrative procedures r
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provides recommendations for a foll
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sound power levels of noise sources
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23.68 ISO: ISO 9296:1988 Acoustics
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in impedance tubes - Part 2: Transf
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23.194 Commission of the European C
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1022 Part H Engineering Acoustics P
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Sound 25. Intens Sound Intensity So
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Under such conditions the intensity
Page 1050 and 1051:
a) Pressure and particle velocity 0
Page 1052 and 1053:
τ is a dummy time variable. The ca
Page 1054 and 1055:
Error in intensity (dB) 5 0 -5 -10
Page 1056 and 1057:
8 7 6 5 4 3 2 1 0 -1 -2 -3 Error du
Page 1058 and 1059:
crophones are calibrated with a pis
Page 1060 and 1061:
Sound power level (dB re 1 pW) 72 7
Page 1062 and 1063:
it is relatively straightforward si
Page 1064 and 1065:
intensity normal to the source. Thi
Page 1066 and 1067:
25.9 W. Maysenhölder: The reactive
Page 1068 and 1069:
25.77 ISO: ISO 11205 Acoustics - No
Page 1070 and 1071:
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Optical 27. Optical Methods Metho f
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course also present in ordinary hol
Page 1096 and 1097:
Optical Methods for Acoustics and V
Page 1098 and 1099:
Page 1100 and 1101:
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50 100 150 200 250 300 350 400 450
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a) b) Optical Methods for Acoustics
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nm 1000 0 -1000 150 y (mm) 100 50 O
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Page 1110 and 1111:
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Laser Optical Methods for Acoustics
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References 27.1 E.F.F. Chladni: Die
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27.75 E.-L.Johansson, L. Benckert,
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Page 1130 and 1131:
About the Authors Iskander Akhatov
Page 1132 and 1133:
Neville H. Fletcher Chapter F.19 Au
Page 1134 and 1135:
Brian C. J. Moore Chapter D.13 Univ
Page 1136 and 1137:
Detailed Contents List of Abbreviat
Page 1138 and 1139:
3.8.3 Acoustic Power ..............
Page 1140 and 1141:
4.8.3 Typical Speed of Sound Profil
Page 1142 and 1143:
7.3 Engines .......................
Page 1144 and 1145:
10 Concert Hall Acoustics Based on
Page 1146 and 1147:
13.4 Temporal Processing in the Aud
Page 1148 and 1149:
15.2.5 String-Bridge-Body Coupling
Page 1150 and 1151:
19.6 Birds ........................
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22.4.5 Combinations of Elementary S
Page 1154 and 1155:
24.8 Overall View on Microphone Cal
Page 1156 and 1157:
Subject Index 3 dB bandwidth 463 A
Page 1158 and 1159:
British Medical Ultrasound Society
Page 1160 and 1161:
electric circuit analogues - acoust
Page 1162 and 1163:
hyperspeech 703 hypospeech 703 I IA
Page 1164 and 1165:
- participation factors 909 - stiff
Page 1166 and 1167:
- musical instruments 541 - musical
Page 1168 and 1169:
- nonlinear vibrations 957 - plate
Page 1170 and 1171:
subjective preference theory 353 su
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Introduction to Acoustics

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