# Morphological Mutation Functions: Applications to Motivic Transformation and a New Class of Cross-Synthesis Techniques

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Page 234 ï~~Morphological Mutation Functions Applications to Motivic Transformation and a New Class of Cross-Synthesis Techniques Larry Polansky and Martin McKinney Bregman Electro-Acoustic Music Studio Dartmouth College, Hanover, NH 03755 email: larry.polansky@mac.dartmouth.edu; martin.mckinney@mac.dartmouth.edu Abstract: This paper is an introduction to a class of algorithms for melodic, formal and timbral based transformation using mutation functions. These functions use formalized morphological metrics, or distance functions on morphologies (e.g shapes, motives, melodies, waveforms, or any sets of ordered data). The mathematical/musical parameters of these functions are presented formally, along with applications to melodic transformation and crosssynthesis, called mutation synthesis, which uses these functions to modulate one waveform into another along the time and frequency domains. General Introduction The concepts of distance and similarity are fundamental to a discussion of form. In order to decide, at the very least, if two objects are distinct, a concept of similarity is needed. Measuring distance in musical morphologies (melodies, duration sequences, shapes in other parameters, and large scale forms) is difficult, since several aspects of musical morphology must be taken into account, including, but not limited to: contour, intervallic magnitude, combinatoriality (that is, how much interrelationship of component parts to consider), and aspects of invariance under certain transformations. Polansky (1987) has outlined several simple distance functions, called morphological metrics, which attempt to isolate several factors of similarity. By making the idea of metrics generative, rather than analytic, we develop simple transformations, called morphological mutations, which yield interesting and fertile procedures for "cross-fading" one morphology into another along perceptual axes. These functions have an implied distance function, called the index, which can be thought of as an inverse metric. Introduction to Mutation Functions Mutation functions are of the general form: f(S,T,2) =M where S is a source morphology (melody, duration sequence, ordered set of samples, etc.), T is a target morphology, M is the resultant mutant, and 92 is some value between 0 and 1. Intermediate values for "2 yield morphologies which have morphological characteristics (depending on the mutation function) somewhere between the source and target [McKinney, 1991]. Morphological Mutation Functions ICMC 2 Although each mutation function "cross-fades" S into T through M, they do so in different ways, and there are several mutation parameters which determine the trajectory of the mutation and, more importantly, what morphological property of S and T is used for the transformation. As a simple example, a pure contour mutation with (2 = 1 will yield a morphology that is only identical to T in contour (direction of intervals). In other words, a contour mutation with 2 = 1 will produce a morphology T' for which: d(T,T') = 0 under some metric function d with a well-defined notion of contour [Polansky, 1987], [Polansky and Bassein, 1990]. Types of Mutation Several classes of mutation are developed based upon aspects of morphology: order, interval contour, interval magnitude, and linearity vs. combinatoriality of intervals. Contour mutations transform the source into the target solely on the basis of interval direction (or sign). Magnitude mutations use the interval magnitude, signed or unsigned (the former is a combination of magnitude and contour). Figure la shows a four element morphology with different contours between each pair of successive elements: the contour between elements A and B is positive (+1); the contour between elements B and C is null (0); and the contour between elements C and D is negative (-1). In Figure la, we are only considering what Polansky has called linear contour [Polansky and Bassein, 1990], [Polansky, 1987], the relationships between adjacent elements of a morphology. Interval magnitude is the absolute magnitude difference between two elements in a morphology. L. Polansky and M. McKinney

Page 235 ï~~B C 1.5 1.0 D 0.5 I I I I1t2+ t t.l 1 2 t+3 Source a) b) Figure 1. a) A sample morphology illustrating the notions of interval sign and interval magnitude. b) Contour Mutation with an index of OS and clumping value of 0.0. In Figure la, the interval magnitude between points A and B is 1.0, between points B and C is 0.0, and between points C and D is 0.5. For more information on the associated metrics for these functions see [Polansky, 1987]. The actual interval function, called A, may be completely general, and defined specifically for a given parameter. For example, it can be arithmetic, ratiometric, or even as simple as "equal to or not." All of the mutation equations below can be rewritten to incorporate this more general notion of the interval A between two elements of a morphology (a detailed exploration of this is beyond the scope of this paper). The mutation index, 2, functions as a kind of "knob" in these mutations. In timbral mutations, 92 becomes the actual index of modulation, and can be a time-varying function (at audio or sub-audio rates), a real-time input, a constant, a sample itself, or a simple lookup table (like a ramp or sinusoid). Contour Mutations The contour mutation function mutates a percentage of the intervals of a source morphology by assigning the contour of the respective interval in the target morphology to the interval magnitude of the source. The index determines what percentage of source intervals, that differ in contour from their respective target intervals, get mutated (take on the contour of the target). By only considering respective intervals from the source and target that differ in sign, the index of the mutation becomes a linear and exhaustive "measure" of the metric space between the source and the target. A contour mutation with an index of 0.0 would produce a mutant identical to the source. With an index of 1.0, the mutant would have all of the interval magnitudes of the source and the interval signs of the target. Note that the above only describes a linear contour mutation, in other words, not all of the contour relationships between all elements of the morphology are considered (like, for example, Morphological Mutation Functions the contour relationship between the 2nd and 5th elements). Clumping Although the index in the contour mutation function determines what percentage of intervals get mutated, it does not determine exactly which intervals get mutated for a given mutation. There are many ways the mutated intervals can be distributed throughout the resultant morphology. So far we have experimented with the use of a fixed clumping value, d, and stochastic clumping. The clumping value d of a mutation function determines what percentage of the mutated intervals are mutated consecutively, d ranges from 0.0 to 1.0: a value of 0.0 spreads the mutated intervals uniformly across the mutated morphology and a value of 1.0 groups all of the mutated intervals into one clump of mutated intervals. For example, with S2 = 0.5 and d = 1.0, the first half of the morphology would be mutated (clumping is as high as possible), the second half left unchanged. With stochastic clumping, each interval in the source with a different contour than its respective interval in the target is selected to be mutated with a probability equal to the index. Thus, for two morphologies, successive mutations using stochastic clumping and the same index could (and probably will) result in different mutation morphologies, creating a different trajectory of mutation. Clumping is critical in using mutation functions to go from one morphology to another by varying index. If stochastic clumping is used, it is possible that the perception of the linear progression of mutant morphologies will be less evident than if the same clumping value is used throughout the progression, as there could be much variation in the order of mutated intervals. Contour Mutation The simple linear contour mutation function is as follows: L. Polansky and M. McKinney ICMC 235

Page 236 ï~~M M+(TiTi-1) */Si- Si-1l Mi = i-1 /Ti Ti-1/ for mutated intervals, and Mi =Mi1 + (Si-Si-1) for non-mutated intervals, for all Mi E M, all Si E S, and all Ti E T assuming the source S, the target T and the mutant M are all equal length morphologies (we will assume these conditions for all equations below unless otherwise stated) and where the index 2 and the clumping value d determine whether the interval will be mutated as described above. Figure lb shows the contour mutation, with.f2 = 0.5 and d = 0.0, on two simple contours. Since d = 0.0, the mutated intervals are spread evenly throughout the contour, and every second interval is mutated starting with the first interval. Magnitude Mutations Regularity Mutation functions that operate on the interval magnitude can have one of two types of indexes. A Uniform index mutates all interval magnitudes of the source by a percentage of the difference of the source interval magnitudes and the target interval magnitudes. The index determines the percentage of the difference. Thus the mutation interval magnitude, Mint, is calculated by: Mint = Sint + index * (Tint- Sint) where Sint is the respective source interval magnitude and Tint is the respective target interval magnitude. Figure 2a shows an Interval Magnitude mutation, with a Uniform index of 0.5, of two simple morphologies. Each interval magnitude in the mutant is one half of the difference of the respective interval magnitudes in the source and target. An Irregular index mutates a percentage of the source interval magnitudes by applying the full target interval magnitude of the target's respective interval. The index determines what percentage of the mutant's intervals assume the target's interval magnitude. The rest of the mutant's intervals keep the source's interval magnitudes. Figure 2b shows an Interval Magnitude mutation, with an Irregular index of 0.5, of two simple morphologies. In this example, every second interval magnitude is the same as the source, starting with the first one, and every interval magnitude between those is the same as the target. The mutated intervals (mutated intervals refer to the intervals in the mutant that have the target's respective interval magnitude) are spread evenly throughout the mutant. Clumping, as described above, is used to distribute the mutated intervals. The Contour Mutation only uses an Irregular index - contour mutation of a specific interval is an all or none operation. The distinction between Uniform and Irregular indexes is the quality of the index. Signed and Unsigned Mutations Mutation functions are also classified by whether or not they operate on the interval sign of the morphology. The Contour Mutation function is inherently signed. Mutation functions that operate on the interval magnitude, however, can be Signed or Unsigned. A Signed mutation function gives the mutated interval the sign (or contour) of the respective target interval and an Unsigned mutation function gives the mutated interval the sign (or contour) of the respective source interval. Some Magnitude mutations Four magnitude mutations are defined based on the two characteristics just described: 1) Uniform Signed Interval Magnitude Mutation (Absolute Magnitude Mutation) 2) Irregular Signed Interval Magnitude Mutation 3) Uniform Unsigned Interval Magnitude Mutation 4) Irregular Unsigned Interval Magnitude Mutation Target Target SoremujJ.Sorc MutantMutant a) b) Figure 2. a) Interval Magnitude Mutation with a Uniform index of 0.5. b) Interval Magnitude Mutation with an Irregular index of 0.5. Morphological Mutation Functions ICMC 236 L. Polansky and M. McKinney

Page 237 ï~~Uniform Signed Interval Magnitude Mutation (USIM) (or The Absolute Magnitude Mutation - AM) The Absolute Magnitude mutation mutates absolute values of the source and target elements, rather than intervals. This is essentially the same as the USIM if the first value of the source and the target are the same. The function is: Mi = Si + S2 * (Ti - Si) This mutation is a simple crossfade between the source and target. Figure 3a shows the AM mutation, with S2 = 0.5. Each element (point) in the mutant is half way between the respective elements in the source and target. Irregular Signed Interval Magnitude Mutation (ISIM) The ISIM is the same as the Absolute Magnitude mutation but the ISIM has an Irregular index. As in the USIM, since the mutant intervals contain the sign and interval magnitude of the target, when the index is 1.0, the mutant is the target. The function is: Mi = Mi.1 + (Ti - Ti-1) for mutated intervals Mi = Mi.1 + (Si - Si-1) for non-mutated intervals where the index "2 and the clumping value d determine whether the interval will be mutated in one of the several manners described above. Figure 3b shows the ISIM, with S2 = 0.5 and d = 0.0. Since a = 0.0, the mutated intervals are spread evenly throughout the contour, and every second interval is mutated, starting with the first interval. Uniform Unsigned Interval Magnitude Mutation (UUIM) The UUIM applies the source interval sign to a percentage difference (depending on the index) of the source and target interval magnitudes. The function is: = M + (Si - Si-1) /Si - Si-1/ (/Si - Si-1/ + S2 */( /T i - Ti-1 - /Sij- Si/ )/) Note that: (Si - Si-1) Isi - Si-1i is just the sign of the source interval. Figure 4a shows the UUIM, with "2 = 0.5. Each interval has an absolute magnitude that is half way in between the absolute magnitudes of the respective intervals in the source and the target. Each interval has the same sign as the source. Irregular Unsigned Interval Magnitude Mutation (IUIM) The IUIM mutates a percentage of the source intervals (determined by the index) by assigning the sign of the source and the complete magnitude of the respective target interval to the mutant. An interval that is not mutated keeps its source interval magnitude and sign. Because this mutation function has an Irregular index, a clumping value a determines the distribution of mutated intervals in the mutant. The function is: (Si- Si-1) * -Tii= M i +,Si Si-1]i for mutated intervals, and Mi = Mi-1 + (Si - Si-1) for non-mutated intervals, where the index "2 and the clumping value a determine whether the interval will be mutated in one of the several manners described above. Figure 4b shows the IUIM, with "2 = 0.5 and d = 0.0. Since d = 0.0, the mutated intervals are spread evenly throughout the contour, and every second interval is mutated starting with the first interval. Mutation Function Relationships An important relationship is that of the contour mutation to the Unsigned Interval Target muOTarget ~***** source Mutant Source Mutant a) b) Figure 3. a) AM Mutation with an index of 0.5 b) ISIM Mutation with an index of 0.5 and clumping value of 0.0. ISIM Mutation with an index of 0.5 and clumping value of 0.0. Morphological Mutation Functions ICMC 237 L. Polansky and M. McKinney

Page 238 ï~~Target Target SourceuSourut a) b) Figure 4. a) UUIM Mutation with an index of 0S. b) IUIM Mutation with an index of 0.5 and clumping value of 0.0. Magnitude mutation functions. These are mutually exclusive and, with indexes of 1.0, are collectively exhaustive of the metric space they define. A contour mutation with an index of 1.0 followed by an Unsigned Interval Magnitude (Uniform or Irregular index) with an index of 1.0 using the output of the contour mutation as the source and the original target as the target, yields a mutant that is identical to the target. Figure 5 is an example of this combination of mutation functions on two simple contours. The resulting mutant has the contour of the target in the first mutation and the interval magnitudes of the target in the second. Thus, combining these mutations, in either order, provides a means for progressing from a source morphology to a target. Using time varying indexes, modifying the way they approach 1.0, and by altering a, one can create different trajectories for the mutation (see also Figure 6). The mutation will, however, always become the target when both of the indexes are 1.0. Mutation Synthesis The authors have applied the mutation functions to digital sound samples, in both the time and frequency domain. Because the mutation functions are non-linear, it is difficult to predict what the output (mutant) will sound like or describe its frequency content (a typical method for analyzing sound). This suggests an experimental and exhaustive approach to applying the mutation functions to digital sound and evaluating the output aurally. The mutation functions were coded in C on a NeXTTM Computer and designed to operate on sounds of the NeXT sound file format [McKinney, 1991]. Time Domain Sounds are mutated in the time domain with the functions describe above by treating a sound as a morphology and its individual sample values (discrete points of the signal amplitude) as elements of that morphology. Normalization and Filtering The inherent instability of the unsigned mutation functions is an obstacle in applying them to time domain sound samples. Typically sources and targets have different contours and interval magnitudes, and applying just the interval magnitudes or just the contour of one to the other offsets the absolute magnitude of the resultant, especially for periodic signals. Since the offset is the same for each period of the signal, the overall offset grows by that constant amount every period. As the input signals grow in length, the offset grows and, if straight normalization is used, the desired signal component amplitudes decrease until they are inaudible. Typically it does not take a very long signal (on the order of one second at a 44100 sampling rate) before the desired signal is completely inaudible. One method of eliminating this offset is to fit a straight line to the signal using linear regression and subtract the line from the signal. This does not significantly distort the desired signal and allows it to expand when normalized. A straight line approximation of a signal is obtained by the following equation: r1x2 - (y~)2 y = Z(x*y) -Zx*Zy L n This method works for steady state signals. For time varying signals, however, the offset created by the mutation function may be positive for part of the signal and negative for another part, so a straight line subtraction is not sufficient. Higher degree polynomials could be fit to the mutant, but that would not be very efficient. After a closer look at the rate (or slope) of the offset created by the mutation functions, it was discovered to generally be below the audio range. In effect, it is an inaudible residue from the mutation functions themselves. By applying a high pass filter with a cutoff frequency just below the hearing threshold Morphological Mutation Functions ICMC 238 L. Polansky and M. McKinney

Page 239 ï~~(about 16 Hz) all of the residue disappears and the mutant can be fully normalized. Because the residue is inaudible, filtering it out does not destroy the validity of the mutation functions as crosssynthesis functions. Figure 5. ContourlUiM mutation with indexes of 1.0 Frequency Domain Work on mutations of the frequency magnitude of sound signals is still in the preliminary stages, with some very promising results. The basic method for mutating two sound signals in the frequency domain is to take a running Fourier analysis of both the source and the target and perform the mutation on the magnitude window. First, an N-point Fast Fourier Transform (FFT) is taken of both Source and the Target sounds, which is obtained by: N-1 X(k) = > (n) * eJ(2rkn/N) n=O where: k is angular frequency ranging from 0 to 27r, N is the length of the FFT, and x(n) is the time domain signal. Under the current NeXT implementation, the length of the FFT is user selectable, and the overlap size of the windows is one half of the FFT length. This introduces a trade-off of high frequency resolution and low window (to signal) resolution for long FFTs and the reverse for short FFTs. The size of the FFT has not been experimented with very much at this stage. The FFT is expressed as a sum of complex exponentials because: eJ' = cosw + jsinw and thus each frequency can be represented as complex exponential or alternatively as a magnitude and phase where: Magnitude = Ia2 + b Morphological Mutation Functions ICMC 2 Phase = tan-1(b) where: a is the amplitude of the real (cosine) part and b is the amplitude of the imaginary (sine) part. In this first implementation of the mutation functions, the frequency magnitude of the signals was mutated and the phase was simply crossfaded using the following method: Phase = index * targetPhase + (1 - index) * sourcePhase At first, phase was ignored but the signals in sequential windows did not line up properly. This method of crossfading the phase was the simplest way to stay within the basic constraints of the mutation functions: when the index is zero, the mutant should resemble the source and when the index is one, it should resemble the target. One can see that this function is a simple Absolute Magnitude mutation. In future implementations, mutating the phase as well as the magnitude of the frequencies with the same mutation function will be investigated. Mutating the frequency data in their complex representation will be experimented with as well. After the frequency data is mutated the inverse FFT is taken and each window is multiplied by a crossfade window and overlapped with the preceding and following windows. In the current implementation, before the inverse FF is taken, the frequency magnitude data is normalized so there are no negative frequencies, which could have resulted from the mutation function. A crossfade window provides a clean flow from one window to the next. Melodic Example The following melodic example, Figure 6, uses a combined mutation: Linear Contour with stochastic clumping, followed by IUIM (Irregular Unsigned Interval Magnitude), again with stochastic clumping. This example mutates the first several measures of a fiddle tune (the source), "The Road to Chimacum" by Patricia Spaeth [Williams, page 13], to an excerpt (of equal length) from Bill Cole's transcription of John Coltrane's solo from the tune "Countdown" (the target) [Cole, page 105]. "... Chimacum" is transposed up a major sixth so that both of the tunes can start on the same pitch, and only pitches are mutated (all the rhythms in the source and target are eighth notes). This example shows 9 melodies, more or less equal spaced mutations from the source (12 = 0.0) to the target (Â~2 = 1.0), although values for 12 -.2 and Â~2 =.8 are omitted for space reasons only. L. Polansky and M. McKinney

Page 240 ï~~"Road to Chiniacum" -> "Countdown" Contour/UUIM Mutation Rcoad to aCirnacum" A t ii - 144 is 11-fl Al -do __ 00 1 04 mod 1.1 PsourceS2=0 6 6 A 14.0 PL [ Wr I i i i - M M10 1w, I I iT I Y v f7- LOO, =ii, %I I I mod I he= r b b # -:J F1 F owed 52=.3 A tp-r- is _OP_ raft" K, I IN IfLod= NOW!a6 b 9 A A _j _r: A rol i I FY 1 52=.5 Loge-- 6a 1 1z IMF I Olt I Ole I de _N f2=.6 b b 0 Q=.7.0d I fi 40 rr% 52=.9 'Countdown IbA _ v FWvTI Tymn m I FWV lW m In I I = __L Now 10 ". F Wig2tns1.0 Figure 6. Melodic Example Morphological Mutation Functions ICMC 240 L. Polansky and M. McKinney

Page 241 ï~~This example was written algorithmically in HMSL and saved to FINALE as a MIDIFile. In this example, the contour and IUIM mutations are not synchronized, each one stochastically chooses intervals to mutate from the source towards the target for each mutated melody, independently of the other mutation. In other words, the same HMSL program run several times, would produce different intermediary mutations, but the general trajectory of mutation would be the same, eventually generating the target (S2 = 1.0). A slightly different interval function is used in this example, useful in normalizing the mutations. All interval calculations are made to the first element of the morphologies. That is, a simple signed interval is of the form Si - S1, instead S i- Si-1. Current and Future Work These mutations have been implemented on two different platforms, in the computer music language HMSL and on the NeXT computer. The HMSL implementation allows for sophisticated real-time melodic mutations, as well as waveform mutation (also real-time) on the Commodore Amiga and on the Macintosh-based DigiDesign AudioMedia or Sound Accelerator cards. The use of HMSL allows for these mutations to be controlled in a variety of interactive contexts. The mutations have been used in several pieces by Polansky, including Bedhaya SadralBedhaya Guthrie, (1990a), and 3 Studies, for performers and live interactive computers (1990b). On the NeXT machine, McKinney has implemented a simple, user-friendly platform for experimentation with time and frequency domain mutation of soundfiles. This implementation allows the user to specify values for clumping, index, index waveforms, types of mutation, and so on, and has so far been the primary source for beginning to understand the sonic aspects of mutation synthesis [McKinney, 1991]. Work on mutation functions and mutation synthesis is just beginning, and many theoretical and practical issues need addressing, including: 1) continued expansion of these two platforms; 2) implementation of 56000 code inside of HMSL for a user-friendly real-time mutation synthesis platform, to be used in performance at both the event and timbral levels; 3) a formal and mathematical understanding of the relationships between mutation functions and standard synthesis algorithms, so that, for-example, resulting spectra will be more predictable; 4) implementation of fast algorithms for combinatorial mutations, both at the Morphological Mutation Functions melodic and waveform level; 5) more sophisticated and accurate normalization and filtering techniques; 6) algorithms and methods for mutating morphologies of unequal lengths; 7) a systematic set of experiments to gain sonic understanding of these functions, starting with simple waveforms, constant phases, and known indexes (as in waveshaping synthesis); 8) more work on the effects of phase differences in the time domain; 9) experiments with different values and types of clumping; 10) techniques for mutations of morphologies of unequal length; 11) more experimentation in the weighted concatenation of different mutation functions. We have already begun to work on most of these issues, but space limitations prevent us from describing them in more detail here. References Cole, Bill. 1976. John Coltrane. Schirmer Books. New York. McKinney, Martin. 1991. "Mutation Synthesis." Master's Thesis, Dartmouth College M.A Program in Electro-Acoustic Music. Polansky, Larry, and Bassein, Richard. 1990. "Possible and Impossible Melody: Some Formal Aspects of Contour." Paper presented at the Society for Music Theory Conference, Oakland, CA. Submitted for publication. Polansky, Larry. 1990a. Bedhaya Sadra/Bedhaya Guthrie. Score, for voices, kemanak, and gamelan instruments. Frog Peak Music. - 1990b. 3 Studies. Software. Frog Peak Music. 1987. "Morphological Metrics: An introduction to a theory of formal distances." In Proc. of the ICMC. Compiled by James Beauchamp. San Francisco. Computer Music Association. Williams, Vivian, editor. 1986. 141 Brand New Old Time Fiddle Tunes (by Pacific Northwest Composers). Volume 2. Voyager Publications. Seattle. L. Polansky and M. McKinney ICMC 241