Mellite 2.33.0 API - de.sciss.fscape.graph

final case class ARCWindow(in: GE, size: GE, lo: GE = 0.0, hi: GE = 1.0, lag: GE = 0.96) extends SingleOut with Product with Serializable

Automatic range control UGen.

Automatic range control UGen. It traces the range of a windowed input signal.

in: signal to adjust
size: window size of input
lo: desired lower margin of output
hi: desired upper margin of output
lag: lag or feedback coefficient

final case class AffineTransform2D(in: GE, widthIn: GE, heightIn: GE, widthOut: GE = 0, heightOut: GE = 0, m00: GE, m10: GE, m01: GE, m11: GE, m02: GE, m12: GE, wrap: GE = 1, rollOff: GE = 0.86, kaiserBeta: GE = 7.5, zeroCrossings: GE = 15) extends SingleOut with Product with Serializable

An affine transformation UGen for image rotation, scaling, translation, shearing.

An affine transformation UGen for image rotation, scaling, translation, shearing. It uses either a sinc-based band-limited resampling algorithm, or bicubic interpolation, depending on the zeroCrossings parameter.

All window defining parameters (widthIn, heightIn, widthOut, heightOut) are polled once per matrix. All matrix and filter parameters are polled one per output pixel.

in: the signal to resample
widthIn: the width (number of columns) of the input matrix
heightIn: the height (number of rows) of the input matrix
widthOut: the width (number of columns) of the output matrix. the special value zero (default) means it is the same as widthIn.
heightOut: the height (number of rows) of the output matrix. the special value zero (default) means it is the same as heightIn.
m00: coefficient of the first column of the first row (scale-x)
m10: coefficient of the first column of the second row (shear-y)
m01: coefficient of the second column of the first row (shear-x)
m11: coefficient of the second column of the second row (scale-y)
m02: coefficient of the third column of the first row (translate-x)
m12: coefficient of the third column of the second row (translate-y)
wrap: if non-zero, wraps coordinates around the input images boundaries. TODO: currently wrap = 0 is broken if using sinc interpolation!
rollOff: the FIR anti-aliasing roll-off width. Between zero and one.
kaiserBeta: the FIR windowing function's parameter
zeroCrossings: the number of zero-crossings in the truncated and windowed sinc FIR. If zero, algorithm uses bicubic interpolation instead.

See also: ScanImage

final case class ArithmSeq(start: GE = 0, step: GE = 1, length: GE = Long.MaxValue) extends SingleOut with Product with Serializable

A UGen that produces an arithmetic sequence of values.

A UGen that produces an arithmetic sequence of values. If both start and step are of integer type (Int or Long), this produces an integer output, otherwise it produces a floating point output.

E.g. ArithmSeq(start = 1, step = 2) will produce a series 1, 3, 5, 7, ....

start: the first output element
step: the amount added to each successive element
length: the number of elements to output

final case class AudioFileIn(file: File, numChannels: Int) extends MultiOut with Product with Serializable

final case class AudioFileOut(in: GE, file: File, spec: AudioFileSpec) extends SingleOut with Product with Serializable

A UGen that reads in an audio file.

A UGen that reads in an audio file. The output signal is the monotonously increasing number of frames written, which can be used to monitor progress or determine the end of the writing process. The UGen keeps running until the in signal ends.

in: the signal to write.
file: the file to write to
spec: the spec for the audio file, including numbers of channels and sample-rate

final case class BinaryOp(op: Int, a: GE, b: GE) extends SingleOut with Product with Serializable

A binary operator UGen, for example two sum or multiply two signals.

A binary operator UGen, for example two sum or multiply two signals. The left or a input is "hot", i.e. it keeps the UGen running, while the right or b input may close early, and the last value will be remembered.

op: the identifier of the operator (e.g. BinaryOp.Times.id)
a: the left operand which determines how long the UGen computes
b: the right operand.

final case class Biquad(in: GE, b0: GE = 0.0, b1: GE = 0.0, b2: GE = 0.0, a1: GE = 0.0, a2: GE = 0.0) extends SingleOut with Product with Serializable

A second order filter section (biquad) UGen.

A second order filter section (biquad) UGen. Filter coefficients are given directly rather than calculated for you. The formula is equivalent to:

y(n) = b0 * x(n) + b1 * x(n-1) + b2 * x(n-2) - a1 * y(n-1) - a2 * y(n-2)

where x is in, and y is the output. Note the naming and signum of the coefficients differs from SuperCollider's SOS.

final case class Bleach(in: GE, filterLen: GE = 256, feedback: GE = 0.001, filterClip: GE = 8.0) extends SingleOut with Product with Serializable

final case class Blobs2D(in: GE, width: GE, height: GE, thresh: GE = 0.3, pad: GE = 0) extends MultiOut with Product with Serializable

A blob detection UGen.

A blob detection UGen. It is based on the meta-balls algorithm by Julien Gachadoat (http://www.v3ga.net/processing/BlobDetection/).

Currently, we output four channels: - 0 - numBlobs - flushes for each window one element with the number of blobs detected - 1 - bounds - quadruplets of xMin, xMax, yMin, yMax - for each blob - 2 - numVertices - the number of vertices (contour coordinates) for each blob - 3 - vertices - tuples of x and y for the vertices of each blob

in: the image(s) to analyse
width: the width of the image
height: the height of the image
thresh: the threshold for blob detection between zero and one
pad: size of "border" to put around input matrices. If greater than zero, a border of that size is created internally, filled with the threshold value

final case class BufferDisk(in: GE) extends SingleOut with Product with Serializable

final case class BufferMemory(in: GE, size: GE) extends GE with Product with Serializable

Inserts a buffer into the stream of up to size frames.

final case class ChannelProxy(elem: GE, index: Int) extends GE.Lazy with Product with Serializable

Straight outta ScalaCollider.

final case class Clip(in: GE, lo: GE, hi: GE) extends SingleOut with Product with Serializable

final case class Complex1FFT(in: GE, size: GE, padding: GE = 0) extends FFTFullUGen with Product with Serializable

final case class Complex1IFFT(in: GE, size: GE, padding: GE = 0) extends FFTFullUGen with Product with Serializable

final case class Complex2FFT(in: GE, rows: GE, columns: GE = 0) extends FFT2FullUGen with Product with Serializable

final case class Complex2IFFT(in: GE, rows: GE, columns: GE = 0) extends FFT2FullUGen with Product with Serializable

final case class ComplexBinaryOp(op: Int, a: GE, b: GE) extends SingleOut with Product with Serializable

final case class ComplexUnaryOp(op: Int, in: GE) extends SingleOut with Product with Serializable

final case class Concat(a: GE, b: GE) extends SingleOut with Product with Serializable

Concatenates two signals.

final case class ConstQ(in: GE, fftSize: GE, minFreqN: GE = 0.0008, maxFreqN: GE = 0.4096, numBands: GE = 432) extends SingleOut with Product with Serializable

A UGen that performs a constant Q spectral analysis, using an already FFT'ed input signal.

A UGen that performs a constant Q spectral analysis, using an already FFT'ed input signal. For each input window, the output signal consists of numBands filter output values. These values are squared, since further processing might need to decimate them again or take the logarithm. Otherwise the caller needs to add .sqrt to get the actual filter outputs.

The default settings specify a range of nine octaves and numBands = 432 thus being 48 bands per octave. At 44.1 kHz, the default minimum frequency would be 35.28 Hz, the maximum would be 18063.36 Hz.

in: the input signal which has already been windowed and FFT'ed using Real1FFT in mode 0 or 2. The windows should have been rotated prior to the FFT so that the time signal starts in the middle of the window and then wraps around.
fftSize: the fft size which corresponds with the window size of the input
minFreqN: the normalized minimum frequency (frequency in Hz, divided by the sampling rate). should be greater than zero and less than or equal to 0.5.
maxFreqN: the normalized maximum frequency (frequency in Hz, divided by the sampling rate). should be greater than zero and less than or equal to 0.5.
numBands: the number of bands or kernels to apply, spreading them evenly (logarithmically) across the range from minimum to maximum frequency.

sealed trait Constant extends UGenIn with StreamIn

A scalar constant used as an input to a UGen.

final case class ConstantD(value: Double) extends Constant with DoubleLike with Product with Serializable

final case class ConstantI(value: Int) extends Constant with IntLike with Product with Serializable

final case class ConstantL(value: Long) extends Constant with LongLike with Product with Serializable

final case class ControlBlockSize() extends SingleOut with Product with Serializable

A scalar information UGen that reports the control block size.

final case class DC(in: GE) extends SingleOut with Product with Serializable

Creates a constant infinite signal.

final case class DCT_II(in: GE, size: GE, numCoeffs: GE, zero: GE = 0) extends SingleOut with Product with Serializable

A UGen for type II discrete cosine transform.

in: input signal
size: input signal window size
numCoeffs: number of coefficients output
zero: if zero (default), the zero'th coefficient is _skipped_ in the output, if non-zero, the zero'th coefficient is included. In any case, the output window size is numCoeffs.

final case class DEnvGen(levels: GE, lengths: GE, shapes: GE = 1, curvatures: GE = 0.0) extends SingleOut with Product with Serializable

An envelope generator UGen similar to SuperCollider's DemandEnvGen.

For each parameter of the envelope (levels, durations and shapes), values are polled every time a new segment starts. The UGen keeps running as long as level inputs are provided. If any of the other inputs terminate early, their last values will be used for successive segments (e.g. one can use a constant length or shape).

Note that as a consequence of having a "hot" levels input, the target level of the last segment may not be reached, as the UGen would terminate one sample earlier. As a workaround one can duplicate the last level value with a length of 1.

The difference to DemandEnvGen is that this UGen does not duplicate functionality in the form of levelScale, levelBias, timeScale, and it does not provide reset and gate functionality. Durations are given as lengths in sample frames.

final case class DebugGen() extends SingleOut with Product with Serializable

case class DebugIntPromise(in: GE, p: Promise[IndexedSeq[Int]]) extends ZeroOut with Product with Serializable

A debugging UGen that completes a promise with the input values it sees.

in: the signal to monitor

final case class DebugOut(in: GE) extends ZeroOut with Product with Serializable

A UGen that simply consumes a signal and keeps running until the in signal ends.

in: the signal to keep 'alive'.

final case class DebugPoll(in: GE, label: String = "poll") extends ZeroOut with Product with Serializable

A UGen that prints snapshots of its input to the console.

A UGen that prints snapshots of its input to the console. Note that arguments have different order than in ScalaCollider!

in: the input to be pulled. If this is a constant, the UGen will close after polling it. This is to prevent a dangling DebugPoll whose trigger is infinite (such as Impulse). If you want to avoid that, you should wrap the input in a DC.
label: an identifying label to prepend to the printing.

case class DebugSink(in: GE) extends ZeroOut with Product with Serializable

A debugging UGen that installs a persistent no-op sink, allowing the in UGen to remain in the graph even if it does not have a side-effect and it is not connected to any other graph element.

in: the element to keep inside the graph

final case class DebugTake(in: GE) extends SingleOut with Product with Serializable

final case class DelayN(in: GE, maxLength: GE, length: GE) extends SingleOut with Product with Serializable

final case class DetectLocalMax(in: GE, size: GE) extends SingleOut with Product with Serializable

A UGen that outputs triggers for local maxima within a sliding window.

A UGen that outputs triggers for local maxima within a sliding window. If multiple local maxima occur within the current window, only the one with the largest value will cause the trigger.

By definition, the first and last value in the input stream cannot qualify for local maxima.

in: the signal to analyze for local maxima
size: the sliding window size. Each two emitted triggers are spaced apart at least by size frames.

final case class Differentiate(in: GE) extends SingleOut with Product with Serializable

A UGen that outputs the differences between adjacent input samples.

The length of the signal is the length of the input. The first output will the the first input (internal x1 state is zero).

final case class Done(in: GE) extends SingleOut with Product with Serializable

A UGen that outputs a single frame of 1 when the input is finished.

final case class Drop(in: GE, length: GE) extends SingleOut with Product with Serializable

final case class Dseq(seq: GE, repeats: GE = 1) extends SingleOut with Product with Serializable

XXX TODO --- not implemented yet.

XXX TODO --- not implemented yet. A UGen that cycles over a list of values.

seq: sequence of values to be returned
repeats: the number of repetitions of the sequence

final case class Elastic(in: GE, num: GE = 1) extends SingleOut with Product with Serializable

Inserts a buffer into the stream of num blocks.

final case class ElseGE(pred: IfOrElseIfThen[GE], branch: Graph, result: GE) extends ElseLike[GE] with GE.Lazy with Product with Serializable

final case class ElseIf[+A](pred: IfOrElseIfThen[A], cond: GE) extends Product with Serializable

final case class ElseIfThen[+A](pred: IfOrElseIfThen[A], cond: GE, branch: Graph, result: A) extends IfThenLike[A] with ElseOrElseIfThen[A] with Product with Serializable

sealed trait ElseLike[+A] extends ElseOrElseIfThen[A]

sealed trait ElseOrElseIfThen[+A] extends Then[A]

final case class ElseUnit(pred: IfOrElseIfThen[Any], branch: Graph) extends ElseLike[Any] with Expander[Unit] with Product with Serializable

sealed trait FFT2FullUGen extends SingleOut

sealed trait FFT2HalfUGen extends SingleOut

sealed trait FFTFullUGen extends SingleOut

sealed trait FFTHalfUGen extends SingleOut

final case class FilterSeq(in: GE, gate: GE) extends SingleOut with Product with Serializable

A UGen that filters its input, retaining only those elements in the output sequence for which the predicate gate holds.

You can think of it as Gate but instead of outputting zeroes when the gate is closed, no values are output at all. Consequently, the length of the output stream will differ from the length of the input stream.

See also: Gate

final case class Fold(in: GE, lo: GE, hi: GE) extends SingleOut with Product with Serializable

final case class FoldCepstrum(in: GE, size: GE, crr: GE, cri: GE, clr: GE, cli: GE, ccr: GE, cci: GE, car: GE, cai: GE) extends SingleOut with Product with Serializable

final case class Fourier(in: GE, size: GE, padding: GE = 0, dir: GE = 1.0, mem: GE = 131072) extends SingleOut with Product with Serializable

Disk-buffered (large) Fourier transform.

Disk-buffered (large) Fourier transform. Output windows will have a complex size of (size + padding).nextPowerOfTwo

in: input signal to transform. This must be complex (Re, Im interleaved)
size: the (complex) window size
padding: the (complex) zero-padding size for each window
dir: the direction is 1 for forward and -1 for backward transform. other numbers will do funny things.
mem: the amount of frames (chunk size) to buffer in memory. this should be a power of two.

final case class Frames(in: GE) extends SingleOut with Product with Serializable

A UGen that generates a signal that incrementally counts the frames of its input.

A UGen that generates a signal that incrementally counts the frames of its input. The first value output will be 1, and the last will correspond to the number of frames seen, i.e. Length(in).

final case class Gate(in: GE, gate: GE) extends SingleOut with Product with Serializable

A UGen that passes through its input while the gate is open, and outputs zero while the gate is closed.

See also: FilterSeq
Latch

final case class GenWindow(size: GE, shape: GE, param: GE = 0.0) extends SingleOut with Product with Serializable

A repeated window generator UGen.

A repeated window generator UGen. It repeats the same window again and again (unless parameters are modulated). The parameters are demand-rate, polled once per window.

size: the window size
shape: the identifier of the window shape, such as GenWindow.Hann.
param: parameter used by some window shapes, such as GenWindow.Kaiser

final case class GeomSeq(start: GE = 1, grow: GE = 2, length: GE = Long.MaxValue) extends SingleOut with Product with Serializable

A UGen that produces a geometric sequence of values.

A UGen that produces a geometric sequence of values. If both start and grow are of integer type (Int or Long), this produces an integer output, otherwise it produces a floating point output.

E.g. GeomSeq(start = 1, grow = 2) will produce a series 1, 2, 4, 8, ....

start: the first output element
grow: the multiplicative factor by which each successive element will grow
length: the number of elements to output

final case class GimpSlur(in: GE, width: GE, height: GE, kernel: GE, kernelWidth: GE, kernelHeight: GE, repeat: GE = 1, wrap: GE = 0) extends SingleOut with Product with Serializable

A UGen similar to GIMP's Slur image filter.

A UGen similar to GIMP's Slur image filter. Instead of a hard-coded kernel, the probability table must be provided as a separate input. The kernel width and height should be odd, so that the kernel is considered to be symmetric around each input image's pixel. If they are odd, the centre corresponds to integer divisions kernelWidth/2 and kernelHeight/2.

in: image input
width: image width
height: image height
kernel: normalized and integrated probability table. Like the image, the cells are read horizontally first, every widthKernel cell begins a new cell. The cell probabilities must have been integrated from first to last, and must be normalized so that the last cell value equals one. A new kernel signal is read once per input image. (If the signal ends, the previous kernel will be used again).
kernelWidth: width of the kernel signal. Read once per input image.
kernelHeight: height of the kernel signal. Read once per input image.
repeat: number of recursive application of the displacement per image. Read once per input image.
wrap: if non-zero, wraps pixels around the image bounds, otherwise clips.

final case class GramSchmidtMatrix(in: GE, rows: GE, columns: GE, normalize: GE = 0) extends SingleOut with Product with Serializable

A UGen that orthogonalizes an input matrix using the stabilized Gram-Schmidt algorithm.

A UGen that orthogonalizes an input matrix using the stabilized Gram-Schmidt algorithm. It processes the row vectors per matrix by making them orthogonal to one another, optionally also orthonormal.

in: the input matrices
rows: the number of rows of the input
columns: the number of columns of the input

final case class HPF(in: GE, freqN: GE = 0.02) extends SingleOut with Product with Serializable

A second order high-pass filter UGen.

A second order high-pass filter UGen. It implements the same type of filter as the SuperCollider equivalent.

in: the signal to filter
freqN: the normalized cut-off frequency (frequency in Hertz divided by sampling rate)

final case class Hash(in: GE) extends SingleOut with Product with Serializable

A UGen that produces a continuous hash value, useful for comparisons and debugging applications.

A UGen that produces a continuous hash value, useful for comparisons and debugging applications. The hash is produced using the 64-bit Murmur 2 algorithm, however the continuous output is given without "finalising" the hash, and it is not initialized with the input's length (as it is unknown at that stage).

Not yet implemented: Only after the input terminates, one additional sample of finalised hash is given (you can use .last to only retain the final hash).

in: the signal to hash.

final case class If(cond: GE) extends Product with Serializable

Beginning of a conditional block.

cond: the condition, which is treated as a boolean with zero being treated as false and non-zero as true. Note that multi-channel expansion does not apply here, because the branches may have side-effects which are not supposed to be repeated across channels. Instead, if cond expands to multiple channels, they are combined using logical | (OR).

See also: IfThen

sealed trait IfOrElseIfThen[+A] extends Then[A]

final case class IfThen[+A](cond: GE, branch: Graph, result: A) extends IfThenLike[A] with Product with Serializable

A side effecting conditional block.

A side effecting conditional block. To turn it into a full if-then-else construction, call Else or ElseIf.

See also: Else
ElseIf

sealed trait IfThenLike[+A] extends IfOrElseIfThen[A] with Expander[Unit]

final case class ImageFileIn(file: File, numChannels: Int) extends MultiOut with Product with Serializable

final case class ImageFileOut(in: GE, file: File, spec: Spec) extends ZeroOut with Product with Serializable

final case class ImageFileSeqIn(template: File, numChannels: Int, indices: GE) extends MultiOut with Product with Serializable

final case class ImageFileSeqOut(in: GE, template: File, spec: Spec, indices: GE) extends ZeroOut with Product with Serializable

final case class Impulse(freqN: GE, phase: GE = 0.0) extends SingleOut with Product with Serializable

Impulse (repeated dirac) generator.

Impulse (repeated dirac) generator. For a single impulse that is never repeated, use zero.

freqN: normalized frequency (reciprocal of frame period)
phase: phase offset in cycles (0 to 1).

final case class LFSaw(freqN: GE, phase: GE = 0.0) extends SingleOut with Product with Serializable

Aliased sawtooth oscillator.

Aliased sawtooth oscillator. Note that the frequency is not in Hertz but the normalized frequency as we do not maintained one global sample rate. For a frequency in Hertz, freqN would be that frequency divided by the assumed sample rate.

Note: Unlike SuperCollider where LFSaw starts at zero for a zero phase, this oscillator begins at -1. To let it start at zero, use a phase of 0.5

freqN: normalized frequency (f/sr).
phase: phase offset from 0 to 1. Note: negative values are currently broken.

final case class LPF(in: GE, freqN: GE = 0.02) extends SingleOut with Product with Serializable

A second order low-pass filter UGen.

A second order low-pass filter UGen. It implements the same type of filter as the SuperCollider equivalent.

in: the signal to filter
freqN: the normalized cut-off frequency (frequency in Hertz divided by sampling rate)

final case class Latch(in: GE, gate: GE) extends SingleOut with Product with Serializable

A sample-and-hold UGen.

A sample-and-hold UGen. It passes through its input while the gate is open, and outputs the last held value while the gate is closed.

See also: Gate

final case class Length(in: GE) extends SingleOut with Product with Serializable

Reports the length of the input as a single value one the input has terminated.

final case class Limiter(in: GE, attack: GE, release: GE, ceiling: GE = 1.0) extends SingleOut with Product with Serializable

A UGen that limits the amplitude of its input signal.

A UGen that limits the amplitude of its input signal. Modelled after FScape 1.0 limiter module. N.B.: The UGen outputs a gain control signal, so the input must be multiplied by this signal to obtain the actually limited signal.

in: input signal
attack: the attack duration in frames, as -60 dB point
release: the release duration in frames, as -60 dB point
ceiling: the maximum allowed amplitude

final case class Line(start: GE, end: GE, length: GE) extends SingleOut with Product with Serializable

A line segment generating UGen.

A line segment generating UGen. The UGen terminates when the segment has reached the end.

A line can be used to count integers (in the lower ranges, where floating point noise is not yet relevant), e.g. Line(a, b, b - a + 1) counts from a to b (inclusive).

start: starting value
end: ending value
length: length of the segment in sample frames

final case class Loudness(in: GE, sampleRate: GE, size: GE, spl: GE = 70, diffuse: GE = 1) extends SingleOut with Product with Serializable

A loudness measurement UGen, using Zwicker bands.

A loudness measurement UGen, using Zwicker bands. One value in Phon per window is output.

in: the signal to analyse
sampleRate: sample rate of the input signal
size: the window size for which to calculate values
spl: the reference of 0 dBFS in decibels
diffuse: whether to assume diffuse field (1) or free field (0)

final case class MatrixInMatrix(in: GE, rowsOuter: GE, columnsOuter: GE, rowsInner: GE, columnsInner: GE, rowStep: GE = 1, columnStep: GE = 1, mode: GE = 0) extends SingleOut with Product with Serializable

Note: mode is not yet implemented.

final case class MatrixOutMatrix(in: GE, rowsInner: GE, columnsInner: GE, columnsOuter: GE, rowOff: GE = 0, columnOff: GE = 0, rowNum: GE = 1, columnNum: GE = 1) extends SingleOut with Product with Serializable

A UGen that stitches together sequences of sub-matrices to a larger matrix.

A UGen that stitches together sequences of sub-matrices to a larger matrix. The matrix dimensions and offsets are updated per "matrix chunk" which is are columnsOuter/columnNum input matrices of size rowsInner * columnsInner. In other words, the UGen has no intrinsic knowledge of the height of the output matrix.

For example, if the input matrices are of size (5, 6) (rows, columns), and we want to assemble the cells (1, 1), (1, 2), (1, 3), (2, 1), (2, 2), (2, 3), that is copped matrices of size (2, 3) beginning at the second row and second column, and we want the outer matrix to have 9 columns, so that each three input matrices appear horizontally next to each other, the settings would be rowsInner = 5, columnsInner = 6, columnsOuter = 9, rowOff = 1, columnOff = 1, rowNum = 2, columnNum = 3.

For more complex behaviour, such as skipping particular rows or columns, ScanImage can be used.

in: the sequence of smaller matrices
rowsInner: height of the input matrices
columnsInner: width of input matrices
columnsOuter: width of the output matrix. Must be an integer multiple of columnNum.
rowOff: offset in rows within the input matrices, where copying to the output matrix begins
columnOff: offset in columns within the input matrices, where copying to the output matrix begins
rowNum: number of rows to copy from each input matrix
columnNum: number of columns to copy from each input matrix.

final case class MelFilter(in: GE, size: GE, minFreq: GE = 55.0, maxFreq: GE = 18000.0, sampleRate: GE = 44100.0, bands: GE = 42) extends SingleOut with Product with Serializable

A UGen that maps short-time Fourier transformed spectra to the mel scale.

A UGen that maps short-time Fourier transformed spectra to the mel scale. To obtain the MFCC, one has to take the log of the output of this UGen and decimate it with a DCT.

Example:

def mfcc(in: GE) = {
  val fsz  = 1024
  val lap  = Sliding(in, fsz, fsz/2) * GenWindow(fsz, GenWindow.Hann)
  val fft  = Real1FFT(lap, fsz, mode = 1)
  val mag  = fft.complex.mag.max(-80)
  val mel  = MelFilter(mag, fsz/2, bands = 42)
  DCT_II(mel.log, 42, 13, zero = 0)
}

in: magnitudes of spectra, as output by Real1FFT(..., mode = 1).complex.abs
size: bands in input spectrum (assumed to be fft-size / 2). lowest band corresponds to DC and highest to (size - 1)/size * sampleRate/2.
minFreq: lower frequency to sample. Will be clipped between zero (inclusive) and Nyquist (exclusive).
maxFreq: upper frequency to sample. Will be clipped between minFreq (inclusive) and Nyquist (exclusive).
bands: number of filter bands output

final case class Metro(period: GE, phase: GE = 0L) extends SingleOut with Product with Serializable

Metronome (repeated dirac) generator.

Metronome (repeated dirac) generator. For a single impulse that is never repeated, use a period of zero. Unlike Impulse which uses a frequency and generates fractional phases prone to floating point noise, this is UGen is useful for exact sample frame spacing. Unlike Impulse, the phase cannot be modulated.

period: number of frames between impulses. Zero is short-hand for Long.MaxValue
phase: phase offset in frames. Initialization time only!

final case class NormalizeWindow(in: GE, size: GE, mode: GE = NormalizeWindow.Normalize) extends SingleOut with Product with Serializable

A UGen that normalizes each input window according to a mode.

A UGen that normalizes each input window according to a mode. It can be used for normalizing the value range or removing DC offset. If the last window is not entirely filled, the output will pad that window always to zero (no matter the normalization mode!)

in: the input signal
size: the input's window size
mode: 0 for normalizing the amplitude to 1, 1 for fitting into the range of 0 to 1, 2 for fitting into the range of -1 to 1, 3 for removing DC (creating a mean of zero).

final case class OffsetOverlapAdd(in: GE, size: GE, step: GE, offset: GE, minOffset: GE) extends SingleOut with Product with Serializable

Overlapping window summation with offset (fuzziness) that can be modulated.

final case class OnePole(in: GE, coef: GE) extends SingleOut with Product with Serializable

A one pole (IIR) filter UGen.

A one pole (IIR) filter UGen. Implements the formula :

out(i) = ((1 - abs(coef)) * in(i)) + (coef * out(i-1))

in: input signal to be processed
coef: feedback coefficient. Should be between -1 and +1

final case class OnePoleWindow(in: GE, size: GE, coef: GE) extends SingleOut with Product with Serializable

A one pole (IIR) filter UGen applied to windowed data.

A one pole (IIR) filter UGen applied to windowed data. Implements the formula :

out(i) = ((1 - abs(coef)) * in(i)) + (coef * out(i-1))

This filter runs in parallel for all frames of the window (or matrix). That is, in the above formula out is replaced by each window element, and i is the window count.

in: input signal to be processed
size: window size
coef: feedback coefficient. Should be between -1 and +1

final case class OverlapAdd(in: GE, size: GE, step: GE) extends SingleOut with Product with Serializable

A UGen that performs overlap-and-add operation on a stream of input windows.

A UGen that performs overlap-and-add operation on a stream of input windows. The size and step parameters are demand-rate, polled once per (input) window.

in: the non-overlapped input
size: the window size in the input
step: the step between successive windows in the output. when smaller than size, the overlapping portions are summed together.

See also: Sliding

final case class PeakCentroid1D(in: GE, size: GE, thresh1: GE = 0.5, thresh2: GE = 0.33, radius: GE = 1) extends GE.Lazy with Product with Serializable

final case class PeakCentroid2D(in: GE, width: GE, height: GE, thresh1: GE = 0.5, thresh2: GE = 0.33, radius: GE = 1) extends MultiOut with Product with Serializable

final case class Pearson(x: GE, y: GE, size: GE) extends SingleOut with Product with Serializable

A UGen that calculates the Pearson product-moment correlation coefficient of two input matrices.

x: first matrix
y: second matrix
size: matrix or window size

final case class PitchAC(in: GE, sampleRate: GE, pitchMin: GE = 75.0, pitchMax: GE = 600.0, numCandidates: GE = 15, silenceThresh: GE = 0.03, voicingThresh: GE = 0.45, octaveCost: GE = 0.01, octaveJumpCost: GE = 0.35, voicedUnvoicedCost: GE = 0.14) extends GE.Lazy with Product with Serializable

A graph element that implements Boersma's auto-correlation based pitch tracking algorithm.

cf. Paul Boersma, ACCURATE SHORT-TERM ANALYSIS OF THE FUNDAMENTAL FREQUENCY AND THE HARMONICS-TO-NOISE RATIO OF A SAMPLED SOUND, Institute of Phonetic Sciences, University of Amsterdam, Proceedings 17 (1993), 97-110

Note that this is currently implemented as a macro, thus being quite slow. A future version might implement it directly as a UGen. Currently stepSize is automatically given, and windowing is fixed to Hann.

final case class PitchesToViterbi(lags: GE, strengths: GE, numIn: GE = 14, peaks: GE, maxLag: GE, voicingThresh: GE = 0.45, silenceThresh: GE = 0.03, octaveCost: GE = 0.01, octaveJumpCost: GE = 0.35, voicedUnvoicedCost: GE = 0.03) extends SingleOut with Product with Serializable

A UGen that takes concurrent pitch tracker paths, and conditions them for the Viterbi algorithm.

A UGen that takes concurrent pitch tracker paths, and conditions them for the Viterbi algorithm. The inputs are typically taken from AutoCorrelationPitches, and from this a suitable add signal is produced to be used in the Viterbi UGen. The output are matrices of size (numIn + 1).squared.

Warning: This is still not thoroughly tested.

lags: pitches given as sample periods, such as returned by AutoCorrelationPitches.
strengths: strengths corresponding to the lags, such as returned by AutoCorrelationPitches.
numIn: number of paths / candidates. to this the unvoiced candidate is added
peaks: the peak amplitude of the underlying input signal, one sample per pitch frame, used for the unvoiced candidate.
maxLag: the maximum lag time, corresponding to the minimum pitch
voicingThresh: threshold for determining whether window is voiced or unvoiced.
silenceThresh: threshold for determining whether window is background or foreground.
octaveCost: weighting factor for low versus high frequency preference.
octaveJumpCost: costs for moving pitches up and down. to match the parameters in Praat, you should multiply the "literature" value by 0.01 * sampleRate / stepSize (typically in the order of 0.25)
voicedUnvoicedCost: cost for transitioning between voiced and unvoiced segments. to match the parameters in Praat, the "literature" value by 0.01 * sampleRate / stepSize (typically in the order of 0.25) see StrongestLocalMaxima see Viterbi

final case class Plot1D(in: GE, size: GE, label: String = "plot") extends ZeroOut with Product with Serializable

Debugging utility that plots 1D "windows" of the input data.

final case class Poll(in: GE, trig: GE, label: String = "poll") extends ZeroOut with Product with Serializable

A UGen that prints snapshots of its input to the console.

A UGen that prints snapshots of its input to the console. Note that arguments have different order than in ScalaCollider!

in: the input to be pulled. If this is a constant, the UGen will close after polling it. This is to prevent a dangling Poll whose trigger is infinite (such as Impulse). If you want to avoid that, you should wrap the input in a DC.
trig: trigger that causes the UGen to print a snapshot of the input.
label: an identifying label to prepend to the printing.

final case class PriorityQueue(keys: GE, values: GE, size: GE) extends SingleOut with Product with Serializable

A sorting UGen that can be thought of as a bounded priority queue.

A sorting UGen that can be thought of as a bounded priority queue. It keeps all data in memory but limits the size to the top size items. By its nature, the UGen only starts outputting values once the input signal (keys) has finished.

keys: the sorting keys; higher values mean higher priority
values: the values corresponding with the keys and eventually output by the UGen. It is well possible to use the same signal both for keys and values.
size: the maximum size of the priority queue.

final case class Progress(in: GE, trig: GE, label: String = "render") extends ZeroOut with Product with Serializable

A UGen that contributes to the progress monitoring of a graph.

A UGen that contributes to the progress monitoring of a graph. It is possible to instantiate multiple instances of this UGen, in which cases their individual progress reports will simply be added up (and clipped to the range from zero to one).

in: progress fraction from zero to one
trig: trigger that causes the UGen to submit a snapshot of the progress to the control instance.
label: the label can be used to distinguish the contributions of different progress UGens

final case class Real1FFT(in: GE, size: GE, padding: GE = 0, mode: GE = 0) extends FFTHalfUGen with Product with Serializable

Forward short-time Fourier transform UGen for a real-valued input signal.

Forward short-time Fourier transform UGen for a real-valued input signal. The FFT size is equal to size + padding. The output is a succession of complex half-spectra, i.e. from DC to Nyquist. Depending on mode, the output window size is either size + padding or size + padding + 2.

in: the real signal to transform. If overlapping windows are desired, a Sliding should already have been applied to this signal, as well as multiplication with a window function.
size: the input window size
padding: amount of zero padding for each input window.
mode: packing mode. 0 (default) is standard "packed" mode, whereby the real part of the bin at Nyquist is stored in the imaginary slot of the DC. This mode allows perfect reconstruction with a Real1IFFT using the same mode. 1 is "unpacked" mode, whereby the output windows are made two samples longer, so that the Nyquist bin is included in the very end. By definition, the imaginary parts of DC and Nyquist are zero. This mode allows perfect reconstruction with a Real1IFFT using the same mode. 2 is "discarded" mode, whereby the Nyquist bin is omitted. While it doesn't allow a perfect reconstruction, this mode is useful for analysis, because the output window size is equal to the fft-size, and the imaginary part of DC is correctly zero'ed.

final case class Real1FullFFT(in: GE, size: GE, padding: GE = 0) extends FFTFullUGen with Product with Serializable

final case class Real1FullIFFT(in: GE, size: GE, padding: GE = 0) extends FFTFullUGen with Product with Serializable

final case class Real1IFFT(in: GE, size: GE, padding: GE = 0, mode: GE = 0) extends FFTHalfUGen with Product with Serializable

final case class Real2FFT(in: GE, rows: GE, columns: GE = 0, mode: GE = 0) extends FFT2HalfUGen with Product with Serializable

Forward short-time Fourier transform UGen for a real-valued input signal.

Forward short-time Fourier transform UGen for a real-valued input signal. The FFT size is equal to rows * columns. The output is a succession of complex half-spectra, i.e. from DC to Nyquist.

XXX TODO: Depending on mode, the output window size is either size + padding or size + padding + 2.

in: the real signal to transform. If overlapping windows are desired, a Sliding should already have been applied to this signal, as well as multiplication with a window function.
rows: the input matrix number of rows
columns: the input matrix number of columns
mode: packing mode. 0 (default) is standard "packed" mode, whereby the real part of the bin at Nyquist is stored in the imaginary slot of the DC. This mode allows perfect reconstruction with a Real2IFFT using the same mode. 1 is "unpacked" mode, whereby the output windows are made two samples longer, so that the Nyquist bin is included in the very end. By definition, the imaginary parts of DC and Nyquist are zero. This mode allows perfect reconstruction with a Real2IFFT using the same mode. 2 is "discarded" mode, whereby the Nyquist bin is omitted. While it doesn't allow a perfect reconstruction, this mode is useful for analysis, because the output window size is equal to the fft-size, and the imaginary part of DC is correctly zero'ed.

final case class Real2FullFFT(in: GE, rows: GE, columns: GE = 0) extends FFT2FullUGen with Product with Serializable

final case class Real2FullIFFT(in: GE, rows: GE, columns: GE = 0) extends FFT2FullUGen with Product with Serializable

final case class Real2IFFT(in: GE, rows: GE, columns: GE = 0, mode: GE = 0) extends FFT2HalfUGen with Product with Serializable

final case class RepeatWindow(in: GE, size: GE = 1, num: GE = 2) extends SingleOut with Product with Serializable

A UGen that repeats the contents of input windows a number of times.

in: the input signal to group into windows of size size and repeat num times. If the input ends before a full window is filled, the last window is padded with zeroes to obtain an input window if size size
size: the window size. one value is polled per iteration (outputting num windows of size size). the minimum size is one (it will be clipped to this).
num: the number of repetitions of the input windows. one value is polled per iteration (outputting num windows of size size). the minimum num is one (it will be clipped to this).

final case class Resample(in: GE, factor: GE, minFactor: GE = 0, rollOff: GE = 0.86, kaiserBeta: GE = 7.5, zeroCrossings: GE = 15) extends SingleOut with Product with Serializable

A band-limited resampling UGen.

It uses an internal table for the anti-aliasing filter. Table resolution is currently fixed at 4096 filter samples per zero crossing and linear interpolation in the FIR table, but the total filter length can be specified through the zeroCrossings parameter. Note: If filter parameters are changed, the table must be recalculated which is very expensive. However, factor modulation is efficient.

Note: Unlike most other UGens, all parameters but in are read at "output rate". That is particular important for factor modulation. For each frame output, one frame from factor is consumed.

in: the signal to resample
factor: the resampling factor, where values greater than one mean the signal is stretched (sampling-rate increases or pitch lowers) and values less than one mean the signal is condensed (sampling-rate decreases or pitch rises)
minFactor: the minimum expected resampling factor, which controls the amount of buffering needed for the input signal. This is used at initialization time only. The default value of zero makes the UGen settles on the first factor value encountered. It is possible to use a value actually higher than the lowest provided factor, in order to limit the buffer usage. In that case, the FIR anti-aliasing filter will be truncated.
rollOff: the FIR anti-aliasing roll-off width
kaiserBeta: the FIR windowing function's parameter
zeroCrossings: the number of zero-crossings in the truncated and windowed sinc FIR.

final case class ResampleWindow(in: GE, size: GE, factor: GE, minFactor: GE = 0, rollOff: GE = 0.86, kaiserBeta: GE = 7.5, zeroCrossings: GE = 15) extends SingleOut with Product with Serializable

A band-limited resampling UGen for images/matrices.

A band-limited resampling UGen for images/matrices. This works like Resample but processes each window cell across time. Thus is is not _resampling each window by itself_, but each "pixel" or matrix cell in a window across successive windows.

in: the signal to resample
size: the window size. Currently this is only read once upon initialization.
factor: the resampling factor, where values greater than one mean the signal is stretched (sampling-rate increases or pitch lowers) and values less than one mean the signal is condensed (sampling-rate decreases or pitch rises)
minFactor: the minimum expected resampling factor, which controls the amount of buffering needed for the input signal. This is used at initialization time only. The default value of zero makes the UGen settles on the first factor value encountered. It is possible to use a value actually higher than the lowest provided factor, in order to limit the buffer usage. In that case, the FIR anti-aliasing filter will be truncated.
rollOff: the FIR anti-aliasing roll-off width
kaiserBeta: the FIR windowing function's parameter
zeroCrossings: the number of zero-crossings in the truncated and windowed sinc FIR.

final case class ResizeWindow(in: GE, size: GE, start: GE = 0, stop: GE = 0) extends SingleOut with Product with Serializable

A UGen that resizes the windowed input signal by trimming each windows boundaries (if start is greater than zero or stop is less than zero) or padding the boundaries with zeroes (if start is less than zero or stop is greater than zero).

A UGen that resizes the windowed input signal by trimming each windows boundaries (if start is greater than zero or stop is less than zero) or padding the boundaries with zeroes (if start is less than zero or stop is greater than zero). The output window size is thus size - start + stop.

in: the signal to window and resize
size: the input window size
start: the delta window size at the output window's beginning
stop: the delta window size at the output window's ending

final case class ReverseWindow(in: GE, size: GE, clump: GE = 1) extends SingleOut with Product with Serializable

final case class RotateFlipMatrix(in: GE, rows: GE, columns: GE, mode: GE) extends SingleOut with Product with Serializable

A UGen that can apply horizontal and vertical flip and 90-degree step rotations to a matrix.

Unless mode is 90-degree rotation (4 or 5) and the matrix is not square, this needs one internal matrix buffer, otherwise two internal matrix buffers are needed.

in: the matrix / matrices to rotate
rows: the number of rows in the input
columns: the number of columns in the input
mode: 0: pass, 1: flip horizontally, 2: flip vertically, 3: rotate 180 degrees, 4: rotate clockwise, 8: rotate anti-clockwise. See the companion object for constants. If you combine flipping and rotation, flipping is performed first, so a mode of 5 means flip horizontally, followed by rotate clockwise.

final case class RotateWindow(in: GE, size: GE, amount: GE = 0) extends SingleOut with Product with Serializable

A UGen that rotates the contents of a window, wrapping around its boundaries.

A UGen that rotates the contents of a window, wrapping around its boundaries. For example, it can be used to align the phases prior to FFT so that the sample that was formerly in the centre of the window moves to the beginning of the window.

in: the signal to window and resize
size: the input window size
amount: the rotation amount in sample frames. Positive values "move" the contents to the right, negative values "move" the contents to the left. The amount is taken modulus size.

final case class RunningMax(in: GE, trig: GE = 0) extends SingleOut with Product with Serializable

A UGen that tracks the maximum value seen so far, until a trigger resets it.

Note: This is different from SuperCollider's RunningMax UGen which outputs the maximum of a sliding window instead of waiting for a reset trigger. To track a sliding maximum, you can use PriorityQueue.

in: the signal to track
trig: a trigger occurs when transitioning from non-positive to positive. At this point (an initially) the internal state is set to negative infinity.

final case class RunningMin(in: GE, trig: GE = 0) extends SingleOut with Product with Serializable

A UGen that tracks the minimum value seen so far, until a trigger resets it.

Note: This is different from SuperCollider's RunningMin UGen which outputs the maximum of a sliding window instead of waiting for a reset trigger. To track a sliding minimum, you can use PriorityQueue.

in: the signal to track
trig: a trigger occurs when transitioning from non-positive to positive. At this point (an initially) the internal state is set to positive infinity.

final case class RunningSum(in: GE, trig: GE = 0) extends SingleOut with Product with Serializable

A UGen that integrates the values seen so far, until a trigger resets it.

Note: This is different from SuperCollider's RunningSum UGen which outputs the sum of a sliding window instead of waiting for a reset trigger. To track a sliding sum, you can subtract a delayed version of RunningSum from a non-delayed RunningSum.

in: the signal to track
trig: a trigger occurs when transitioning from non-positive to positive. At this point (an initially) the internal state is set to zero.

final case class RunningWindowMax(in: GE, size: GE, trig: GE = 0) extends SingleOut with Product with Serializable

A UGen that like RunningMax calculates the maximum observed value of the running input.

A UGen that like RunningMax calculates the maximum observed value of the running input. However, it operates on entire windows, i.e. it outputs windows that contain the maximum elements of all the past windows observed.

in: the windowed signal to monitor
size: the window size. This should normally be a constant. If modulated, the internal buffer will be re-allocated, essentially causing a reset trigger.
trig: a trigger signal that clears the internal state. When a trigger is observed, the _currently processed_ window is reset altogether until its end, beginning accumulation again from the successive window. Normally one will thus want to emit a trigger in sync with the _start_ of each window. Emitting multiple triggers per window does not have any effect different from emitting the first trigger in each window.

final case class RunningWindowMin(in: GE, size: GE, trig: GE = 0) extends SingleOut with Product with Serializable

A UGen that like RunningMin calculates the minimum observed value of the running input.

A UGen that like RunningMin calculates the minimum observed value of the running input. However, it operates on entire windows, i.e. it outputs windows that contain the minimum elements of all the past windows observed.

in: the windowed signal to monitor
size: the window size. This should normally be a constant. If modulated, the internal buffer will be re-allocated, essentially causing a reset trigger.
trig: a trigger signal that clears the internal state. When a trigger is observed, the _currently processed_ window is reset altogether until its end, beginning accumulation again from the successive window. Normally one will thus want to emit a trigger in sync with the _start_ of each window. Emitting multiple triggers per window does not have any effect different from emitting the first trigger in each window.

final case class RunningWindowSum(in: GE, size: GE, trig: GE = 0) extends SingleOut with Product with Serializable

A UGen that like RunningSum calculates the sum of the running input.

A UGen that like RunningSum calculates the sum of the running input. However, it operates on entire windows, i.e. it outputs windows that contain the sum elements of all the past windows observed.

in: the windowed signal to monitor
size: the window size. This should normally be a constant. If modulated, the internal buffer will be re-allocated, essentially causing a reset trigger.
trig: a trigger signal that clears the internal state. When a trigger is observed, the _currently processed_ window is reset altogether until its end, beginning accumulation again from the successive window. Normally one will thus want to emit a trigger in sync with the _start_ of each window. Emitting multiple triggers per window does not have any effect different from emitting the first trigger in each window.

final case class ScanImage(in: GE, width: GE, height: GE, x: GE = 0, y: GE = 0, next: GE = 0, wrap: GE = 1, rollOff: GE = 0.86, kaiserBeta: GE = 7.5, zeroCrossings: GE = 15) extends SingleOut with Product with Serializable

A UGen that scans the pixels of an image using an x and y input signal.

A UGen that scans the pixels of an image using an x and y input signal. It uses either a sinc-based band-limited resampling algorithm, or bicubic interpolation, depending on the zeroCrossings parameter.

All window defining parameters (width, height) are polled once per matrix. All scanning and filter parameters are polled one per output pixel.

in: the image to scan
width: the width (number of columns) of the input matrix
height: the height (number of rows) of the input matrix
x: horizontal position of the dynamic scanning signal
y: vertical position of the dynamic scanning signal
next: a trigger that causes the UGen to read in a new image from in.
wrap: if non-zero, wraps coordinates around the input images boundaries. TODO: currently wrap = 0 is broken if using sinc interpolation!
rollOff: the FIR anti-aliasing roll-off width. Between zero and one.
kaiserBeta: the FIR windowing function's parameter
zeroCrossings: the number of zero-crossings in the truncated and windowed sinc FIR. If zero, algorithm uses bicubic interpolation instead.

See also: AffineTransform2D
Slices

final case class SegModPhasor(freqN: GE, phase: GE = 0.0) extends SingleOut with Product with Serializable

A phasor UGen that takes new frequency values at the beginning of each cycle.

A phasor UGen that takes new frequency values at the beginning of each cycle. It can be used to implement the 'segmod' program of Döbereiner and Lorenz. In contrast, LFSaw continuously reads frequency values, it output values go from zero to one, and the phase argument is only used internally (the output signal always starts at zero).

To turn this, for example, into a sine oscillator, you can use (SegModPhasor(...) * 2 * math.Pi).sin.

Warning: passing in zero frequencies will halt the process. XXX TODO

freqN: normalized frequency (f/sr). One value per output cycle is read. Also note that the UGen terminates when the freqN signal ends. Thus, to use a constant frequency, wrap it in a DC or DC(...).take.
phase: phase offset in radians. The phase offset is only used internally to determine when to change the frequency. For example, with a phase of 0.25, when the output phase reaches 0.25, the next frequency value is read. With the default phase of 0.0, a frequency value is read after one complete cycle (output reaches 1.0).

final case class SetResetFF(trig: GE, reset: GE = 0) extends SingleOut with Product with Serializable

A flip-flop UGen with two inputs, one (set) triggering an output of 1, the other (reset) triggering an output of 0.

A flip-flop UGen with two inputs, one (set) triggering an output of 1, the other (reset) triggering an output of 0. Subsequent triggers happening within the same input slot have no effect. If both inputs receive a trigger at the same time, the reset input takes precedence.

final case class SinOsc(freqN: GE, phase: GE = 0.0) extends SingleOut with Product with Serializable

Sine oscillator.

Sine oscillator. Note that the frequency is not in Hertz but the normalized frequency as we do not maintained one global sample rate. For a frequency in Hertz, freqN would be that frequency divided by the assumed sample rate.

freqN: normalized frequency (f/sr).
phase: phase offset in radians

final case class Slices(in: GE, spans: GE) extends SingleOut with Product with Serializable

A UGen that assembles slices of an input signal in random access fashion.

A UGen that assembles slices of an input signal in random access fashion. It does so by buffering the input to disk.

in: the signal to re-arrange.
spans: successive frame start (inclusive) and stop (exclusive) frame positions determining the spans that are output by the UGen. This parameter is read on demand. First, the first two values are read, specifying the first span. Only after this span has been output, the next two values from spans are read, and so on. Values are clipped to zero (inclusive) and the length of the input signal (exclusive). If a start position is greater than a stop position, the span is output in reversed order.

See also: ScanImage

final case class Sliding(in: GE, size: GE, step: GE) extends SingleOut with Product with Serializable

A UGen that produces a sliding window over its input.

in: the input to be repacked into windows
size: the window size
step: the stepping factor in the input, between windows

See also: OverlapAdd

final case class SlidingPercentile(in: GE, len: GE = 3, frac: GE = 0.5, interp: GE = 0) extends SingleOut with Product with Serializable

A UGen that reports a percentile of a sliding window across its input.

A UGen that reports a percentile of a sliding window across its input. The UGen starts outputting values immediately, even if the median window len is not yet reached. This is because len can be modulated. If one wants to discard the initial values, use a drop.

Note that for an even median length and no interpolation, the reported median may be either the value at index len/2 or len/2 + 1 in the sorted window.

All arguments are polled at the same rate. Changing the frac value may cause an internal table rebuild and can thus be expensive.

in: the input to analyze
len: the length of the sliding median window
frac: the percentile from zero to one. The default of 0.5 produces the median.
interp: if zero (default), uses nearest-rank, otherwise uses linear interpolation. Note: currently not implemented, must be zero

final case class SlidingWindowPercentile(in: GE, winSize: GE, medianLen: GE = 3, frac: GE = 0.5, interp: GE = 0) extends SingleOut with Product with Serializable

A UGen that reports a percentile of a sliding window across every cell of a window'ed input (such as an image).

The UGen starts outputting values immediately, even if the medianLen is not yet reached. This is because medianLen can be modulated (per input window). If one wants to discard the initial values, use a drop, for example for medianLen/2 * winSize frames.

Note that for an even median length and no interpolation, the reported median may be either the value at index medianLen/2 or medianLen/2 + 1 in the sorted window.

All arguments but in are polled per input window. Changing the frac value may cause an internal table rebuild and can thus be expensive.

in: the window'ed input to analyze
winSize: the size of the input windows
medianLen: the length of the sliding median window (the filter window applied to every cell of the input window)
frac: the percentile from zero to one. The default of 0.5 produces the median.
interp: if zero (default), uses nearest-rank, otherwise uses linear interpolation. Note: currently not implemented, must be zero

final case class SortWindow(keys: GE, values: GE, size: GE) extends SingleOut with Product with Serializable

A UGen that sorts the input data window by window.

keys: the sorting keys; output will be in ascending order
values: the values corresponding with the keys and eventually output by the UGen. It is well possible to use the same signal both for keys and values.
size: the window size.

final case class StrongestLocalMaxima(in: GE, size: GE, minLag: GE, maxLag: GE, thresh: GE = 0.0, octaveCost: GE = 0.0, num: GE = 14) extends MultiOut with Product with Serializable

A peak detection UGen, useful for implementing the auto-correlation based pitch detection method of Paul Boersma (1993).

A peak detection UGen, useful for implementing the auto-correlation based pitch detection method of Paul Boersma (1993). Taking an already calculated auto-correlation of size size, the UGen looks for local maxima within a given range.

The UGen has two outputs. The first output gives the lag times or periods of the n strongest peaks per window (to obtain a frequency, divide the sampling rate by these lag times). The second output gives the intensities of these n candidates. If there are less than n candidates, the empty slots are output as zeroes.

in: the auto-correlation windows
size: the size of the auto-correlation windows. must be at least 2.
minLag: the minimum lag time in sample frames, corresponding to the maximum frequency accepted
maxLag: the maximum lag time in sample frames, corresponding to the minimum frequency accepted
thresh: the "voicing" threshold for considered for local maxima within minLag maxLag.
octaveCost: a factor for favouring higher frequencies. use zero to turn off this feature.
num: number of candidate periods output. This is clipped to be at least 1. see PitchesToViterbi see Viterbi

final case class Take(in: GE, length: GE) extends SingleOut with Product with Serializable

final case class TakeRight(in: GE, length: GE) extends SingleOut with Product with Serializable

sealed trait Then[+A] extends Lazy

final case class Timer(trig: GE) extends SingleOut with Product with Serializable

A UGen that outputs the number of sample frames passed since last triggered.

A UGen that outputs the number of sample frames passed since last triggered. If no trigger is used, it simply outputs a linearly rising ramp.

trig: trigger signal to reset the counter. Note that the UGen shuts down when trig finishes, so to use a constant like 0, it has to be wrapped in a DC, for example.

final case class TransposeMatrix(in: GE, rows: GE, columns: GE) extends SingleOut with Product with Serializable

A UGen that transposes 2-dimensional matrices.

A UGen that transposes 2-dimensional matrices. This is a 2-dimensional windowed process, where each window has length rows * columns. Input is assumed to be "left-to-right, top-to-bottom", so the first samples constitute the first row, the next samples constitute the second row, etc.

Note: This is an in-place process which uses less memory but possibly more time. A faster variant would be to use RotateMirrorMatrix

The output matrix is transposed (rows and columns exchanged). So an input of (a, b, c, d, e, f) with rows = 2 and columns = 3 is interpreted as ((a, b, c), (d, e, f)), transposed as ((a, d), (b, e), (c, f)) and output flat as (a, d, b, e, c, f).

To rotate an image ninety degrees clockwise, you would have rows = height and columns = width.

in: the input matrices
rows: the number of rows of the _input_
columns: the number of columns of the _input_

sealed trait UGenInGroup extends UGenInLike

final case class UGenOutProxy(ugen: MultiOut, outputIndex: Int) extends UGenProxy with Product with Serializable

A UGenOutProxy refers to a particular output of a multi-channel UGen.

A UGenOutProxy refers to a particular output of a multi-channel UGen. A sequence of these form the representation of a multi-channel-expanded UGen.

sealed trait UGenProxy extends UGenIn

final case class UnaryOp(op: Int, in: GE) extends SingleOut with Product with Serializable

final case class UnzipWindow(in: GE, size: GE = 1) extends GE.Lazy with Product with Serializable

final case class UnzipWindowN(numOutputs: Int, in: GE, size: GE = 1) extends MultiOut with Product with Serializable

final case class ValueDoubleSeq(elems: Double*) extends SingleOut with Product with Serializable

Loops the given values.

final case class ValueIntSeq(elems: Int*) extends SingleOut with Product with Serializable

Loops the given values.

final case class ValueLongSeq(elems: Long*) extends SingleOut with Product with Serializable

Loops the given values.

final case class Viterbi(mul: GE = 1.0, add: GE, numStates: GE, numFrames: GE = -1) extends SingleOut with Product with Serializable

A UGen performing a generalized Viterbi algorithm.

A UGen performing a generalized Viterbi algorithm. The Viterbi algorithm tries to find the best path among sequences of states, by evaluating transition probabilities. It runs over a predefined number of frames, accumulating data of different states. It maximizes the likelihood of the terminal state, and then backtracks to reconstruct the likely path (sequence of states). The output is the sequence of state indices (from zero inclusive to numStates exclusive).

Note: This UGen must run until numFrames or the inputs are exhausted, before it can begin outputting values.

This implementation is generalized in the sense that instead of the canonical matrices "sequences of observations", "initial probabilities", "transition matrix", and "emission matrix", it takes two large matrices mul and add that contain the equivalent information. These two matrices allow the UGen to operate in two different modes:

- multiplicative (as in the Wikipedia article) by damping the probabilities over time - accumulative (as used in Praat for pitch tracking) by adding up the weights (if you have "costs", feed in their negative values).

Basically the internal delta matrix is created by the update function delta = (delta_prev * mul) + add (with the corresponding matrix indices).

The initial delta state is zero. Therefore, in order to provide the initial state, you obtain an initial vector v by providing an add matrix of numStates x numStates cells, which is zero except for the first column filled by v (alternatively, each row filled with the next value of v, or a diagonal matrix of v; if v can take negative value, make sure to fill the initial numStates x numStates completely by duplicating v).

For the classical data, set add just to the initial matrix as explained above (multiplying emitted first observations with initial probabilities), and then use exclusively mul by passing in emitted observations multiplied by their transition probabilities:

mul[t][i][j] = transitionProb[i][j] * emissionProb[observation[t]][i]

See https://en.wikipedia.org/wiki/Viterbi_algorithm

mul: the generalized multiplicative matrix (combining transition probabilities, emission probabilities and observations). If only accumulation is used, set this to 1.0.
add: the generalized accumulative matrix (combining transition probabilities, emission probabilities and observations). If only multiplication is used, set this to provide the initial state (see above), followed either by zeroes or by terminating the signal.
numStates: the number of different states, as reflected by the inner dimensions of matrices mul and add.
numFrames: the number of observations. If -1, the UGen runs until the input is exhausted. This happens when both mul and add end. see StrongestLocalMaxima see PitchesToViterbi

final case class WhiteNoise(mul: GE = 1.0) extends SingleOut with Product with Serializable

Nominal signal range is -mul to +mul (exclusive).

final case class WindowApply(in: GE, size: GE, index: GE = 0, mode: GE = 0) extends SingleOut with Product with Serializable

A UGen that extracts for each input window the element at a given index.

in: the window'ed signal to index
size: the window size.
index: the zero-based index into each window. One value per window is polled.
mode: wrap mode. 0 clips indices, 1 wraps them around, 2 folds them, 3 outputs zeroes when index is out of bounds.

final case class WindowIndexWhere(p: GE, size: GE) extends SingleOut with Product with Serializable

A UGen that determines for each input window the first index where a predicate holds.

A UGen that determines for each input window the first index where a predicate holds. It outputs one integer value per window; if the predicate does not hold across the entire window, the index will be -1.

p: a predicate to detect the index
size: the window size.

final case class WindowMaxIndex(in: GE, size: GE) extends SingleOut with Product with Serializable

A UGen that determines for each input window the index of the maximum element.

A UGen that determines for each input window the index of the maximum element. It outputs one integer value per window; if multiple elements have the same value, the index of the first element is reported (notably zero if the window contains only identical elements).

in: the input signal.
size: the window size.

final case class Wrap(in: GE, lo: GE, hi: GE) extends SingleOut with Product with Serializable

final case class ZipWindow(a: GE, b: GE, size: GE = 1) extends SingleOut with Product with Serializable

final case class ZipWindowN(in: GE, size: GE = 1) extends SingleOut with Product with Serializable

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