Difference between revisions of "Taps paper"

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'''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
* {amisra,prc,gewang}@cs.princeton.edu
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
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
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
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
Describe analysis user interface?
Once the components of a sound have been separated and saved as templates, TAPESTREA allows each template to be transformed and synthesized individually. At this point, TAPESTREA also offers additional synthesis templates to control the placement or distribution of basic components in a composition. The transformation and synthesis options for the different template types are as follows:
== Deterministic Events ==
Deterministic events are synthesized from their tracks using sinusoidal re-synthesis. Frequency and magnitude between consecutive frames in a track are linearly interpolated, and time-domain samples are computed from this information.
The track representation allows considerable flexibility in applying frequency and time transformations on a deterministic event. The event's frequency (related to its pitch) can be linearly scaled before computing the time-domain samples, simply by multiplying the frequency at each point on its tracks by a specified factor. Similarly, the event can be stretched or shrunk in time by scaling the time values in the time-to-frequency trajectories of its tracks. This technique works for almost any frequency or time scaling factor without producing artifacts. Frequency and time transformations can take place in real-time in TAPESTREA, thus allowing an event to be stretched, shrunk or pitch shifted even as it is being synthesized.
== Transient Events ==
Since transient events are brief by definition, TAPESTREA stores them directly as time-domain audio frames. Synthesizing a transient event without any transformations, therefore, involves playing back the samples in the audio frame.
In addition, TAPESTREA allows time-stretching and pitch-shifting in transient events as well. This is implemented using a phase vocoder [cite Dolson], which limits the scaling factors to a range smaller and perhaps more reasonable than what is available for deterministic events, yet large enough to create noticeable effects.
Transient events by nature can also act as "grains" for traditional granular synthesis [cite Truax/Roads again?] The frequency and time transformation tools for transients, along with the additional synthesis templates described in Sections 4.4 to 4.6, can thus provide an interactive "granular synthesis" interface.
== Stochastic Background ==
The internal representation of a ''stochastic background'' template begins with a link to a sound file containing the related background component extracted in the analysis phase. However, merely looping through this sound file or randomly mixing segments of it does not produce a satisfactory background sound. Instead, our goal here is to generate ongoing background that sounds controllably similar to the original extracted stochastic background.
Therefore, the stochastic background is synthesized from the saved sound file using an extension of the wavelet tree learning algorithm [cite Dubnov]. In the original algorithm, the saved background sound
is decomposed into a wavelet tree where each node represents a wavelet coefficient, with depth corresponding to resolution. The wavelet coefficients are computed using the Daubechies wavelet
with 5 vanishing moments. A new wavelet tree is then built, with each node selected based on the similarity of its ancestors and its first ''k'' predecessors (nodes at the same depth but associated with earlier time samples) to corresponding sequences of nodes in the original tree. The learning algorithm also takes into account the amount of randomness desired. Finally, the new wavelet tree undergoes an inverse wavelet transform to provide the synthesized time-domains samples. This learning technique works best with the separated stochastic background as input, since the harmonic events it would otherwise chop up have been removed.
TAPESTREA uses a modified and optimized version of the algorithm, which follows the same basic step except in some details. For instance, the modified algorithm includes the option of incorporating randomness into the first level of learning, and also considers ''k'' as dependent on the depth of a node rather than being constant. More importantly, it optionally avoids learning the coefficients at the highest resolutions. These resolutions roughly correspond to high frequencies, and randomness at these levels does not significantly alter the results, while the learning involved in attaining that randomness takes the most time. Optionally stopping the learning at a lower level thus optimizes the algorithm and allows it to run in real-time.
Further, TAPESTREA offers interactive control over the learning parameters in the form of "randomness" and "similarity" parameters. The size of a sound segment to be analyzed as one unit can also be controlled, and results in a "smooth" synthesized background for larger sizes versus a more "chunky" background for smaller sizes. Creatively manipulating these parameters can, in fact, yield interesting (and weird) musical compositions generated through "stochastic background" alone.
== Event Loops ==
Event loops are synthesis templates designed to facilitate the parametric repetition of a single event. Any deterministic or transient event template can be formed into a loop. When the loop is played, instances of the associated event are synthesized at the specified density and periodicity, and within a specified range of random transformations. These parameters can be modified while the loop is playing, to let the synthesized sound change gradually.
The density refers to how many times the event is repeated per second, and could be on the order of 0.001 to 1000. At the higher densities, and especially for transient events, the synthesized sound is often perceived as continuous, thus resembling granular synthesis.
The periodicity, ranging from 0 to 1, denotes how periodic the repetition is, with a periodicity of 1 meaning that the event is repeated at fixed time intervals. The interval between consecutive occurrences of an event is generally determined by feeding the desired periodicity and density into a Gaussian random number generator. It is straightforward to replace this generator with one that follows a Poisson or other user-specified probability distribution.
In addition to the parameters for specifying the temporal placement of events, TAPESTREA allows each instance of the recurring event to be randomly transformed within a range. The range is determined by selected average frequency- and time-scale factors, and a randomness factor that dictates how far an individual transformation may vary from the average. Individual transformation parameters are uniformly selected from within this range. Apart from frequency and time scaling, the gain and pan of event instances can also randomly vary in the same way.
== Timelines ==
While a loop parametrically controls the repetition of a single event, with some amount of randomization, a timeline allows a template to be explicitly placed in time, in relation to other templates. Any number of existing templates can be added to a timeline, as well as deleted from it or re-positioned within it once they have been added.
A template's location on the timeline indicates its onset time with respect to when the timeline starts playing. When a timeline is played, each template on it is synthesized at the appropriate onset time, and is played for its duration or till the end of the timeline is reached. The duration of the entire timeline can be on the order of milliseconds to weeks, and may be modified after the timeline's creation.
TAPESTREA also allows the placement of timelines within timelines (or even within themselves). This allows for template placement to be controlled at multiple time-scales or levels, making for a "multiresolution synthesis."
== Mixed Bags ==
* Controlling relative density of multiple templates...
* Combines features of timelines and loops.
== Score Language ==
* ChucK
* 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.
== Other controls ==
* Reverb, gain, pan
= Discussions/Contributions/Sound samples/ =
* what?
* 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),
* 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.

Latest revision as of 16:10, 12 March 2006