The Really Good TitleWithout TAPESTREA
- Pasta Tree, E. Rat Pesta, Ape Treats
- Ananya Misra, Perry Cook, Ge Wang
- Department of Computer Science (also Music), Princeton University
- 1 ABSTRACT
- 2 INTRODUCTION
- 3 RELATED WORK
- 4 ANALYSIS PHASE
- 5 SYNTHESIS PHASE
- 6 Discussions/Contributions/Sound samples/
- 7 CONCLUSIONS
- 8 REFERENCES
Coming up eventually, along with references. (Note: don't forget these parts...)
In the 1940s and 50s, Pierre Schaeffer developed musique concrete. Unlike traditional music, musique concrete starts with existing or concrete recorded sounds, which are organized into abstract musical structures. The existing recordings often include natural and industrial sounds that are not conventionally musical, but can be manipulated to make music, either by editing magnetic tape or now more commonly through digital sampling. Typical manipulations include cutting, copying, reversing, looping and changing the speed of recorded segments.
Today, several other forms of electronic/electroacoustic music also involve manipulating a set of recorded sounds. Acousmatic music [cite Dhomont], for instance, evolved from musique concrete and refers to compositions designed for environments that emphasize the sound itself rather than the performance-oriented aspects of the piece.
The acoustic ecology [cite Schafer] movement gave rise to soundscape composition [cite Truax] or the creation of realistic soundscapes from recorded environmental sounds. One of the key features of soundscape composition, according to Truax, is that "most pieces can be placed on a continuum between what might be called `found sound' and `abstracted' approaches." However, while "contemporary signal processing techniques can easily render such sounds unrecognizable and completely abstract," a soundscape composition piece remains recognizable even at the abstract end of the continuum.
Sound designers for movies, theater and art often have a related goal of starting with real world sounds and creating emotionally evocative sound scenes, which are still real, yet transformed and transformative. Classic examples include mixing a transformed lion's roar with other sounds to accompany the wave sounds in "Perfect Storm," and the sound design for "Black Hawk Down" [cite Paul Rudy's Miami ICMC paper and provide another informative clause or something]. These sound designers are "sound sculptors" as well, but transform sounds to enhance or create a sense of reality, rather than for musical purposes.
Artists from all of the above backgrounds share the process of manipulating recordings, but aim to achieve different effects. We present a single framework for starting with recordings and producing sounds that can lie anywhere on a `found' to `unrecognizable' continuum. `Found' sounds can be modified in subtle ways or extended indefinitely, while moving towards the `unrecognizable' end of the spectrum unleashes a range of manipulations beyond time-domain techniques. In fact, the same set of techniques apply throughout the continuum, differing only in how they are used. We call this framework TAPESTREA: Techniques and Paradigms for Expressive Synthesis, Transformation and Rendering of Environmental Audio.
TAPESTREA manipulates recorded sounds in two phases. In the analysis phase, the sound is separated into reusable components that map to individual foreground events or background. In the synthesis phase, these components are parametrically transformed, combined and re-synthesized using time- and frequency-domain techniques that can be controlled on multiple levels. While we highlight the synthesis methods in this paper, the analysis phase is also integral as it enables the most flexible means for dealing with real-world sonic material.
Related techniques used for musical composition include spectral modeling synthesis [cite Serra/Smith] and granular synthesis [cite Truax, Roads-microsound]. Spectral modeling synthesis separates a sound into sinusoids and noise, and was originally used formodeling instrument sounds. Granular synthesis, in contrast, functions in the time-domain and involves continuously controlling very brief sonic events or sound grains. TAPESTREA employs aspects of both, using separation techniques on environmental sounds and controlling the temporal placement of resulting events.
Another technique used in TAPESTREA is an extension of a wavelet tree learning algorithm [cite Dubnov] for sound texture synthesis. This method performs a wavelet decomposition on a sound clip and uses machine learning on the wavelet coefficients to generate similar non-repeating sound texture. The algorithm works well for sounds that are mostly stochastic, but can break up extended pitched portions. It can also be slow in its original form. TAPESTREA takes advantage of this technique by improving the speed of the algorithm, and only using in on the types of sound for which it works well.
TAPESTREA starts by separating a recording into deterministic events or the sinusoidal or pitched components of the sound, transient events or brief noisy bursts of energy, and the remaining stochastic background or din. This separation can be parametrically controlled and takes place in the analysis phase.
Deterministic events are foreground events extracted by sinusoidal modeling based on the spectral modeling framework [cite Serra]. Overlapping frames of the sound are transformed into the frequency domain using the FFT. For each spectral frame, the n highest peaks above a specified magnitude threshold are recorded, where n can range from 1 to 50. These peaks can also be loaded from a preprocessed file. The highest peaks from every frame are then matched across frames by frequency, subject to a controllable "frequency sensitivity" threshold, to form sinusoidal tracks. Tracks can be "mute" or below the magnitude threshold for a specified maximum number of frames, or can be discarded if they fail to satisfy a minimum track length requirement. Undiscarded tracks are optionally grouped [cite Ellis, Melih and Gonzalez?] by harmonicity, common amplitude and frequency modulation, and common onset/offset, to form deterministic events, which are essentially collections of related sinusoidal tracks. If the grouping option is not selected, each track is interpreted as a separate deterministic event.
Transient events or brief noisy foreground events are usually detected in the time-domain by observing changes in signal energy over time [cite Verma and Meng, Bello et al.?]. TAPESTREA analyzes the recorded sound using a non-linear one-pole envelope follower filter with a sharp attack and slow decay and finds points where the derivative of the envelope is above a threshold. These points mark sudden increases in energy and are interpreted as transient onsets. A transient event is considered to last for up to half a second from its onset; its exact length can be controlled within that range.
The stochastic background represents parts of the recording that constitute background noise, and is obtained by removing the detected deterministic and transient events from the original sound. Deterministic events are removed by eliminating the peaks of each sinusoidal track from the corresponding spectral frames. To eliminate a peak, the magnitudes of the bins beneath the peak are smoothed down, while the phase in these bins is randomized. Transient events, in turn, are removed in the time-domain by applying wavelet tree learning [cite Dubnov] to generate a sound clip that resembles nearby transient-free segments of the original recording. This synthesized "clean" background replaces the samples containing the transient event to be removed.
Separating a sound into components in this way has several advantages. The distinction between foreground and background components is semantically clear to humans, who can therefore work within the framework with a concrete understanding of what each component represents. The different types of components are also stored and processed differently according to their defining characteristics, thus allowing flexible transformations on individual components. Each transformed component can be saved as a template and later reloaded, reused, copied, further transformed, or otherwise treated as a single object. In addition, the act of separating a sound into smaller sounds makes it possible to "compose" a variety of pieces by combining these constituents in diverse ways.
Describe analysis user interface?
- Templates are synthesized individually with transformations.
- Gain / pan control exists
- Sinusoidal resynthesis.
- Pitch and time transformations, real-time.
- Phase vocoder is not stable but exists -- so the composer has the standard pitch/time stretching tools.
- Transients make nice "grains" for traditional granular synthesis.
- Wavelet tree learning.
- Improvements can go here.
- For repeating a single event.
- Random frequency and time transformations on each instance.
- Periodicity of event distributionñGaussian.
- Can also be replaced by other distributions such as Poisson or
your own invention.
- Density of eventsñhow frequently they recurñrelates to granular
synthesis for transients.
- Explicitly state when a template is played in relation to
- Timelines within timelines... multiresolution synthesis?
- Controlling relative density of multiple templates...
- Combines features of timelines and loops.
- Precise control beyond what sliders can provide.
- Any other speci�c features?
Pitch and Time Quantizations
- Quantize pitch to a scale.
- A few pre-programmed pitch tables or one that's user programmable.
- The ability to change pitch table on a timeline would be good
- Quantizing time to a time grid for rhythm.
These could probably be done through chuck, but maybe there's a clean way to do it through tapestrea itself.
- Real references to be added.
- Dannenberg, R. B. (1989). The Canon score language. Computer
Music Journal 13(1), 47ñ56.
- Dannenberg, R. B., C. L. Fraley, and P. Velikonja (1991). Fugue:
A functional language for sound synthesis. Computer 24(7), 36ñ42.
- Dannenberg, R. B. and C. W. Mercer (1992). Real-time software
synthesis on superscalar architectures. In Proceedings of the International Computer Music Conference, pp. 174ñ177. International Computer Music Association.
- Lindemann, E., F. Dechelle, B. Smith, and M. Starkier (1991).
The architecture of the IRCAM musical workstation. Computer Music Journal 15(3), 41ñ49.
- Mathews, M. V. (1969). The Technology of Computer Music.
Cambridge, Massachusetts: MIT Press.