Discrete-time Fourier transform

About: Discrete-time Fourier transform is a research topic. Over the lifetime, 5072 publications have been published within this topic receiving 144643 citations. The topic is also known as: DTFT.

A note on Fourier multipliers

Abstract: A short proof is given of de Leeuw's restriction result for multipliers. In this note we prove directly the following result of de Leeuw (Proposition 3.2 in [l]). Theorem. Let m(x, y) be a Fourier multiplier for Lp(R'+i). Then for almost every x, mx(y)=m(x, y) is a Fourier multiplier for LP(R>) and the multiplier norm of mx does not exceed that of m. In particular, the restriction is possible for each x such that (x, y) is a Lebesgue point of m for almost all y ER'. To prove this we recall that the (necessarily) bounded measurable function m is a Fourier multiplier for Lp(Rn) ii and only if there is a constant C such that, for/, gECÔ(Rn), (*) J m(x)}(x)g(-x)dx = (2^C\\fUg\\p., where/(x) =ff(y)e~ix'vdy and 1/p + l/p' = 1. The best constant C is then the norm of the operator K defined by m}= (Kf)", and we write | m | p for this quantity. Remark. The inequality (*) might be taken as the definition of Fourier multiplier instead of: m(Lv)*Q2(Lv)* for í^pS2, a duality argument for p>2. If p = \ or p = 2 the result is obvious since Fourier transforms of L1 functions restrict and since the Fourier multipliers for L2 are the essentially bounded measurable functions. In the other cases let/, (pECo(Ri), g, ^PECÔ(R'). We assume at first that m is continuous. Apply (*) and Fubini, using f(x)g(y) for f(x, y), tp(x)ip(y) for g(x, y), to deduce that I(x) = ——7 I TM>(x, y)g(y)Ky)dy (lie)' J (2*y is a Fourier multiplier for LP(R'), with |/|pg | wz|p||g||p||v^||P'. Since Received by the editors July 6, 1970. AMS 1969 subject classifications. Primary 4255, 4425.

Optical image processing using maximum or minimum phase functions

Abstract: A method utilizes an optical image processing system. The method includes calculating a product of (i) a measured magnitude of a Fourier transform of a complex transmission function of an object or optical image and (ii) an estimated phase term of the Fourier transform of the complex transmission function. The method further includes calculating an inverse Fourier transform of the product, wherein the inverse Fourier transform is a spatial function. The method further includes calculating an estimated complex transmission function by applying at least one constraint to the inverse Fourier transform.

Set-Class Similarity, Voice Leading, and the Fourier Transform

Abstract: In this article, I consider two ways to model distance (or inverse similarity) between chord types, one based on voice leading and the other on shared interval content. My goal is to provide a contrapuntal reinterpretation of Ian Quinn’s work, which uses the Fourier transform to quantify similarity of interval content. The first section of the article shows how to find the minimal voice leading between chord types or set-classes. The second uses voice leading to approximate the results of Quinn’s Fourier-based method. The third section explains how this is possible, while the fourth argues that voice leading is somewhat more flexible than the Fourier transform. I conclude with a few thoughts about realism and relativism in music theory. twentieth-century music often moves flexibly between contrasting harmonic regions: in the music of Stravinsky, Messiaen, Shostakovich, Ligeti, Crumb, and John Adams, we find diatonic passages alternating with moments of intense chromaticism, sometimes mediated by nondiatonic scales such as the whole-tone and octatonic. In some cases, the music moves continuously from one world to another, making it hard to identify precise bound aries between them. Yet we may still have the sense that a particular passage, melody, or scale is, for instance, fairly diatonic, more-or-less octatonic, or less diatonic than whole-tone. A challenge for music theory is to formalize these intuitions by proposing quantitative methods for locating musical objects along the spectrum of contemporary harmonic possibilities. One approach to this problem uses voice leading: from this point of view, to say that two set-classes are similar is to say that any set of the first type can be transformed into one of the second without moving its notes very far. Thus, the acoustic scale is similar to the diatonic because we can transform one into the other by a single-semitone shift; for example, the acoustic scale {C, D, E, F≥, G, A, B≤} can be made diatonic by the single-semitone displacement F≥ → F or B≤ → B. Similarly, when we judge the minor seventh chord Thanks to Rachel Hall, Justin Hoffman, Ian Quinn, Joe Straus, and in particular Clifton Callender, whose investigations into continuous Fourier transforms deeply influenced my thinking. Callender pursued his approach despite strenuous objections on my part, for which I am both appropriately grateful and duly chastened. 252 J O U r n A L o f M U S I C T h E O r Y Dmitri Tymoczko Voice Leading and the Fourier Transform to be very similar to the dominant seventh, we are saying that we can relate them by a single-semitone shift. This conception of similarity dates back to John roeder’s work in the mid-1980s (1984, 1987) and has been developed more recently by Thomas robinson (2006), Joe Straus (2007), and Clifton Callender, Ian Quinn, and myself (2008). The approach is consistent with the thought that composers, sitting at a piano keyboard, would judge chords to be similar when they can be linked by small physical motions. Another approach uses intervallic content: from this point of view, to say that set-classes are similar is to say that they contain similar collections of intervals. (That the two methods are different is shown by “Z-related” or “nontrivially homometric” sets, which contain the same intervals but are nonidentical according to voice leading.) In a fascinating pair of papers, Quinn has demonstrated that the Fourier transform can be used to quantify this approach.1 Essentially, for any number n from 1 to 6, and every pitch class p in a chord, the Fourier transform assigns a two-dimensional vector whose components are Vp,n 5 (cos 2ppn/12, sin 2ppn/12). (1) Adding these vectors together, for one particular n and all the pitch classes p in the chord, produces a composite vector representing the chord as a whole— its “nth Fourier component.” The length (or “magnitude”) of this vector, Quinn astutely observes, reveals something about the chord’s harmonic character: in particular, chords saturated with (12/n)-semitone intervals, or intervals approximately equal to 12/n, tend to score highly on this index of chord quality.2 The Fourier transform thus seems to capture the intuitive sense that chords can be more or less diminished-seventh-like, perfect-fifthy, or wholetonish. It also seems to offer a distinctive approach to set-class similarity: from this point of view, two set-classes can be considered “similar” when their Fourier magnitudes are approximately equal—a situation that obtains when the chords have approximately the same intervals. The interesting question is how these two conceptions relate. In recent years, a number of theorists have tried to reinterpret Quinn’s Fourier magnitudes using voice-leading distances. robinson (2006), for example, pointed out that there is a strong anticorrelation between the magnitude of a chord’s first Fourier component and the size of the minimal voice leading to the nearest chromatic cluster. (See also Straus 2007, which echoes robinson’s point.) however, neither robinson nor Straus found an analogous interpretation of the other Fourier components. In an interesting article in this issue (see pages 219–49), Justin hoffman extends this work, interpreting Fourier components in light of unusual “voice-leading lattices” in which voices move by distances other than one semitone. But despite this intriguing idea, the 1 See Quinn 2006 and 2007. Quinn’s use of the Fourier transform develops ideas in Lewin 1959 and 2001 and Vuza 1993. 2 These magnitudes are the same for transpositionally or inversionally related chords, so it is reasonable to speak of a set-class’s Fourier magnitudes. Dmitri Tymoczko Voice Leading and the Fourier Transform 253 relation between Fourier analysis and more traditional conceptions of voice leading remains obscure. The purpose of this article is to describe a general connection between the two approaches: it turns out that the magnitude of a chord’s nth Fourier component is approximately inversely related to the size of the minimal voice leading to the nearest subset of any perfectly even n-note chord.3 For instance, a chord’s first Fourier component is approximately inversely related to the size of the minimal voice leading to any transposition of {0}; the second Fourier component is approximately inversely related to the size of the minimal voice leading to any transposition of either {0} or {0, 6}; the third component is approximately inversely related to the size of the minimal voice leading to any transposition of either {0}, {0, 4}, or {0, 4, 8}, and so on. Interestingly, however, we can see this connection clearly only when we model chords as multisets in continuous pitch-class space, following the approach of Callender, Quinn, and Tymoczko (2008). (This in fact may be one reason why previous theorists did not notice the relationship.) When we do adopt this perspective, we see that there is a deep relationship between two seemingly very different conceptions of set-class similarity, one grounded in voice leading, the other in interval content. Furthermore, this realization allows us to generalize some of the features of Quinn’s approach, using related methods that transcend some of the limitations of the Fourier transform proper. I. Voice leading and set-class similarity Let me begin by describing the voice-leading approach to set-class similarity (or inverse distance), reviewing along the way some basic definitions. Much of what follows is drawn from (or implicit in) earlier essays, including Tymoczko 2006 and 2008 and Callender, Quinn, and Tymoczko 2008; readers who want to explore these ideas further are hereby referred to these more in-depth discussions. We can label pitch classes using real numbers (not just integers) in the range [0, 12), with C as 0.4 here the octave has size 12, and familiar twelvetone equal-tempered semitones have size 1. This system provides labels for every conceivable pitch class and does not limit us to any particular scale; thus, the number 4.5 refers to “E quarter-tone sharp,” halfway between the twelvetone equal-tempered pitch classes E and F. A voice leading between pitch-class sets corresponds to a phrase like “the C major triad moves to E major by moving C down to B, holding E fixed, and shifting G up by semitone to G≥.” We can notate this more efficiently by writing 3 By “perfectly even n-note chord” I mean the chord that exactly divides the octave into n equally sized pieces, not necessarily lying in any familiar scale. For example, the perfectly even eight-note chord is {0, 1.5, 3, 4.5, 6, 7.5, 9, 10.5}. 4 The notation [x, y) indicates a range that includes the lower bound x but not the upper bound y. Similarly (x, y) includes neither upper nor lower bounds, while [x, y] includes both. 254 J O U r n A L o f M U S I C T h E O r Y Dmitri Tymoczko Voice Leading and the Fourier Transform (C, E, G) 1, 0, 1 (B, E, G≥), indicating that C moves to B by one descending semitone, E moves to E by zero semitones, and G moves to G≥ by one ascending semitone. The order in which voices are listed is not important; thus, (C, E, G) 1, 0, 1 (B, E, G≥) is the same as (E, G, C) 0, 1, 1 (E, G≥, B). The numbers above the arrows represent paths in pitch-class space, or directed distances such as “up two semitones,” “down seven semitones,” “up thirteen semitones,” and so on. When the paths all lie in the range (–6, 6] I eliminate them; thus, a notation like (C, E, G) → (B, E, G≥) indicates that each voice moves to its destination along the shortest possible route, with the arbitrary convention being that tritones ascend. Formally, voice leadings between pitch-class sets can be modeled as multisets of ordered pairs, in which the first element is a pitch class and the second a real number representing a path in pitch-class space. Voice leadings are bijective when they associate each element of one chord with precisely one element of the other. however, it matters whether w

The errors in FFT estimation of the Fourier transform

Abstract: The problem of determining the error in approximating the Fourier transform by the discrete Fourier transform is studied. Exact formulas for the relative error are established for classes of functions, called canonical-k (k/spl ges/0), and asymptotic error formulas are established for a much wider class of functions, called order-k. The formulas are dependent only on the class and not on the function in the class whose Fourier transform is being approximated, and this facilitates the application of the results.

Fourier analysis using spectral observers

Abstract: New techniques for Fourier spectral analysis are reported, for which ongoing spectral estimates are generated in real time. The algorithms are recursive, expressable in state variable form, and involve real number computations. The ability of these spectral observers to perform one-step-per-sample updating is demonstrated with numerical examples.