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  • About This Issue

The articles in this issue commence with a case study of compositional algorithms applied in live performance. The laptop performing duo klipp av ("cut apart" in Swedish) consists of Nick Collins (audio) and Fredrik Olofsson (video and computer graphics). As their name suggests, one of the duo's primary performance strategies consists of the real-time segmentation and splicing of audio and video signals. Algorithms control the composition and repetition of the splices, which are applied to stored material or to audiovisual events captured live. The interaction and mapping between audio and video constitutes a central concern of the duo. In their article in this issue, the two performers provide some contextual background to their work and then explain their segmentation and mapping techniques in detail. (Not coincidentally, some of these topics hark back to our issue on visual music, Winter 2005, for which this article was originally submitted.)

Two of this issue's articles deal with subtractive sound synthesis, an historically important but still vital area of electronic music. The first of these articles concerns virtual analog synthesis, namely, digital emulation of analog synthesizers (an idea featured in our Winter 1997 issue, and one that has become commonplace in today's commercial music software and hardware). The authors—Vesa Välimäki and Antti Huovilainen at the Helsinki University of Technology—offer new, efficient methods for digitally generating the sawtooth, pulse, and triangle waveforms of classic analog synthesizers, but with attenuated aliasing. They also propose an efficient digital version of the famous Moog ladder filter, one that has fairly independent cutoff and resonance and offers several types of response.

The other article on subtractive synthesis, by Mitsuko Aramaki et al., describes a model for synthesizing percussion instruments and related impact sounds. In the authors' real-time implementation of their theoretical model, the stochastic source is Gaussian white noise, band-pass filtered through 40 resonant filters and combined with the output of 40 periodic oscillators. The mixture of the white noise, filtered noise, and oscillator output is fed first through a set of filters modeling the excitation, next through a set of time-varying filters modeling the material (wood, steel, glass, etc.), and finally through a filter that accounts for the impact force. The authors focus on usability, offering several strategies for mapping the large set of synthesis parameters to a reduced set of control parameters.

The final two articles have in common the broad topic of the characterization of musical signals. In the first of these articles, Matt Cooper et al. present a survey of techniques for extracting features from sound recordings and presenting the results visually so as to facilitate music information retrieval. As background, the authors describe ways to reduce the number of parameter dimensions resulting from audio analysis. They then describe some approaches to visualizing attributes of a single piece of music: the similarity matrix, beat spectrum, beat spectrogram, beat histogram, "GenreGram" (a real-time display of instantaneous classification results) and "timbregram" (a sequence of color-coded vertical stripes, with time on the horizontal axis). Finally, the article considers two methods for visualizing collections of music: timbre spaces and "Islands of Music" (a technique, based on self-organizing maps and smoothed data histograms, that was explained in our Summer 2004 issue).

Shlomo Dubnov's article takes a new look at randomness and structure in musical signals. He adopts ideas not only from traditional signal [End Page 1] processing, but also from information theory, to arrive at a perspective on anticipation in music. The author stresses that one needs to take into account not just the signal, but also the listener, in order to model the predictability of the signal (which is not the same thing as its determinism). Information rate (IR) refers to the reduction in uncertainty about the present signal that is afforded by considering its past. The author's "vector-IR" technique generalizes notions of anticipation to apply to vectors in a multidimensional space, à la principal components analysis (PCA) or independent components analysis (ICA). The musical applicability of the technique is considered by comparing his automatically derived "anticipation profile" to time-varying human judgments of emotional force in recorded music.



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