This class implements an amplitude-based slicer, with various customisable options and conditions to detect absolute amplitude changes as onsets and offsets. It is part of the Fluid Decomposition Toolkit of the FluCoMa project.footnote::This was made possible thanks to the FluCoMa project ( http://www.flucoma.org/ ) funded by the European Research Council ( https://erc.europa.eu/ ) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 725899).::
This class implements an amplitude-based slicer, with various customisable options and conditions to detect absolute amplitude changes as onsets and offsets. It is part of the LINK:: Guides/FluidDecomposition:: of LINK:: Guides/FluCoMa::. For more explanations, learning material, and discussions on its musicianly uses, visit http://www.flucoma.org/
FluidAmpSlice is based on an envelop follower on a highpassed version of the signal, which is then going through a Schmidt trigger and state-aware time contraints. The example code below is unfolding the various possibilites in order of complexity.
This class implements an amplitude-based slicer, with various customisable options and conditions to detect relative amplitude changes as onsets. It is part of the Fluid Decomposition Toolkit of the FluCoMa project.footnote::This was made possible thanks to the FluCoMa project ( http://www.flucoma.org/ ) funded by the European Research Council ( https://erc.europa.eu/ ) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 725899).::
This class implements an amplitude-based slicer, with various customisable options and conditions to detect relative amplitude changes as onsets. It is part of the LINK:: Guides/FluidDecomposition:: of LINK:: Guides/FluCoMa::. For more explanations, learning material, and discussions on its musicianly uses, visit http://www.flucoma.org/
FluidAmpSlice is based on two envelop followers on a highpassed version of the signal: one slow that gives the trend, and one fast. Each have features that will interact. The example code below is unfolding the various possibilites in order of complexity.
This class implements an amplitude-based slicer, with various customisable options and conditions to detect absolute amplitude changes as onsets and offsets. It is part of the Fluid Decomposition Toolkit of the FluCoMa project.footnote::This was made possible thanks to the FluCoMa project ( http://www.flucoma.org/ ) funded by the European Research Council ( https://erc.europa.eu/ ) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 725899).::
This class implements an amplitude-based slicer, with various customisable options and conditions to detect absolute amplitude changes as onsets and offsets. It is part of the LINK:: Guides/FluidDecomposition:: of LINK:: Guides/FluCoMa::. For more explanations, learning material, and discussions on its musicianly uses, visit http://www.flucoma.org/
FluidBufAmpGate is based on an envelop follower on a highpassed version of the signal, which is then going through a Schmidt trigger and state-aware time contraints. The example code below is unfolding the various possibilites in order of complexity.
This class implements an amplitude-based slicer,with various customisable options and conditions to detect relative amplitude changes as onsets. It is part of the Fluid Decomposition Toolkit of the FluCoMa project.footnote::This was made possible thanks to the FluCoMa project ( http://www.flucoma.org/ ) funded by the European Research Council ( https://erc.europa.eu/ ) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 725899).::
This class implements an amplitude-based slicer,with various customisable options and conditions to detect relative amplitude changes as onsets. It is part of the LINK:: Guides/FluidDecomposition:: of LINK:: Guides/FluCoMa::. For more explanations, learning material, and discussions on its musicianly uses, visit http://www.flucoma.org/
FluidBufAmpSlice is based on two envelop followers on a highpassed version of the signal: one slow that gives the trend, and one fast. Each have features that will interact. The example code below is unfolding the various possibilites in order of complexity.
A FluidBufCompose object provides a flexible utility for combining the contents of buffers on the server. It can be used for thing like mixing down multichannel buffers, or converting from left-right stereo to mid-side. It is used extensively in all the example code of LINK::Guides/FluidDecomposition:: as part of the FluCoMa project. footnote::
This was made possible thanks to the FluCoMa project ( http://www.flucoma.org/ ) funded by the European Research Council ( https://erc.europa.eu/ ) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 725899).::
A FluidBufCompose object provides a flexible utility for combining the contents of buffers on the server. It can be used for thing like mixing down multichannel buffers, or converting from left-right stereo to mid-side. It is used extensively in all the example code of LINK::Guides/FluidDecomposition:: as part of the FluCoMa project. For more explanations, learning material, and discussions on its musicianly uses, visit http://www.flucoma.org/
At its most simple, the object copies the content of a source buffer into a destination buffer. The flexibility comes from the various flags controlling which portions and channels of the source to use, and by applying gains (which can be positive or negative) to the source data and the portion of the destination that would be overwritten.
@ -16,11 +16,7 @@ The algorithm takes a buffer in, and divides it into two or three outputs, depen
## a percussive component;
## a residual of the previous two if the flag is set to inter-dependant thresholds. See the maskingMode below.::
It is part of the Fluid Decomposition Toolkit of the FluCoMa project. footnote::
This was made possible thanks to the FluCoMa project ( http://www.flucoma.org/ ) funded by the European Research Council ( https://erc.europa.eu/ ) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 725899).
::
More information on median filtering, and on HPSS for musicianly usage, are availabe in LINK::Guides/FluCoMa:: overview file.
It is part of the LINK:: Guides/FluidDecomposition:: of LINK:: Guides/FluCoMa::. For more explanations, learning material, and discussions on its musicianly uses, visit http://www.flucoma.org/
This class implements two loudness descriptors, computing the true peak of the signal as well as applying the filters proposed by broadcasting standards to emulate the perception of amplitude. It is part of the Fluid Decomposition Toolkit of the FluCoMa project.FOOTNOTE::This was made possible thanks to the FluCoMa project ( http://www.flucoma.org/ ) funded by the European Research Council ( https://erc.europa.eu/ ) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 725899).::
This class implements two loudness descriptors, computing the true peak of the signal as well as applying the filters proposed by broadcasting standards to emulate the perception of amplitude. It is part of the LINK:: Guides/FluidDecomposition:: of LINK:: Guides/FluCoMa::. For more explanations, learning material, and discussions on its musicianly uses, visit http://www.flucoma.org/
The process will return a multichannel buffer with two channels per input channel, one for loudness and one for the true peak value of the frame, both in dBfs. More information on broadcasting standardisation of loudness measurement is available at the reference page FOOTNOTE::https://tech.ebu.ch/docs/tech/tech3341.pdf:: and in more musician-friendly explantions here FOOTNOTE::http://designingsound.org/2013/02/06/loudness-and-metering-part-1/::. Each sample represents a value, which is every hopSize. Its sampling rate is STRONG::sourceSR / hopSize::.
This class implements a classic spectral descriptor, the Mel-Frequency Cepstral Coefficients (https://en.wikipedia.org/wiki/Mel-frequency_cepstrum). The input is first filtered in to STRONG::numBands:: perceptually-spaced bands, as in LINK::Classes/FluidMelBands::. It is then analysed into STRONG::numCoeffs:: number of cepstral coefficients. It has the avantage of being amplitude invarient, except for the first coefficient. It is part of the Fluid Decomposition Toolkit of the FluCoMa project.FOOTNOTE:: This was made possible thanks to the FluCoMa project ( http://www.flucoma.org/ ) funded by the European Research Council ( https://erc.europa.eu/ ) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 725899).::
This class implements a classic spectral descriptor, the Mel-Frequency Cepstral Coefficients (https://en.wikipedia.org/wiki/Mel-frequency_cepstrum). The input is first filtered in to STRONG::numBands:: perceptually-spaced bands, as in LINK::Classes/FluidMelBands::. It is then analysed into STRONG::numCoeffs:: number of cepstral coefficients. It has the advantage of being amplitude invariant, except for the first coefficient. It is part of the LINK:: Guides/FluidDecomposition:: of LINK:: Guides/FluCoMa::. For more explanations, learning material, and discussions on its musicianly uses, visit http://www.flucoma.org/
The process will return a single multichannel buffer of STRONG::numCoeffs:: per input channel. Each frame represents a value, which is every hopSize.
This class implements a spectral shape descriptor where the amplitude is given for a number of equally spread perceptual bands. The spread is based on the Mel scale (https://en.wikipedia.org/wiki/Mel_scale) which is one of the first attempt to mimic pitch perception scientifically. This implementation allows to select the range and number of bands dynamically. It is part of the Fluid Decomposition Toolkit of the FluCoMa project.FOOTNOTE:: This was made possible thanks to the FluCoMa project ( http://www.flucoma.org/ ) funded by the European Research Council ( https://erc.europa.eu/ ) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 725899).::
This class implements a spectral shape descriptor where the amplitude is given for a number of equally spread perceptual bands. The spread is based on the Mel scale (https://en.wikipedia.org/wiki/Mel_scale) which is one of the first attempt to mimic pitch perception scientifically. This implementation allows to select the range and number of bands dynamically. It is part of the LINK:: Guides/FluidDecomposition:: of LINK:: Guides/FluCoMa::. For more explanations, learning material, and discussions on its musicianly uses, visit http://www.flucoma.org/
The process will return a single multichannel buffer of STRONG::numBands:: per input channel. Each frame represents a value, which is every hopSize.
@ -27,10 +27,7 @@ In this implementation, the components are reconstructed by masking the original
The whole process can be related to a channel vocoder where, instead of fixed bandpass filters, we get more complex filter shapes that are learned from the data, and the activations correspond to channel envelopes.
More information on possible musicianly uses of NMF are availabe in LINK::Guides/FluCoMa:: overview file.
FluidBufNMF is part of the Fluid Decomposition Toolkit of the FluCoMa project. footnote::
This was made possible thanks to the FluCoMa project ( http://www.flucoma.org/ ) funded by the European Research Council ( https://erc.europa.eu/ ) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 725899). ::
FluidBufNMF is part of the LINK:: Guides/FluidDecomposition:: of LINK:: Guides/FluCoMa::. For more explanations, learning material, and discussions on its musicianly uses, visit http://www.flucoma.org/
This class implements a non-real-time slicer using an algorithm assessing novelty in the signal to estimate the slicing points. A novelty curve is being derived from running a kernel across the diagonal of the similarity matrix, and looking for peak of changes. It implements the seminal results published in 'Automatic Audio Segmentation Using a Measure of Audio Novelty' by J Foote. It is part of the Fluid Decomposition Toolkit of the FluCoMa project. footnote::This was made possible thanks to the FluCoMa project ( http://www.flucoma.org/ ) funded by the European Research Council ( https://erc.europa.eu/ ) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 725899).::
This class implements a non-real-time slicer using an algorithm assessing novelty in the signal to estimate the slicing points. A novelty curve is being derived from running a kernel across the diagonal of the similarity matrix, and looking for peak of changes. It implements the seminal results published in 'Automatic Audio Segmentation Using a Measure of Audio Novelty' by J Foote. It is part of the LINK:: Guides/FluidDecomposition:: of LINK:: Guides/FluCoMa::. For more explanations, learning material, and discussions on its musicianly uses, visit http://www.flucoma.org/
The process will return a buffer which contains indices (in sample) of estimated starting points of different slices.
This class implements many spectral-based onset detection metrics, most of them taken from the literature. (http://www.dafx.ca/proceedings/papers/p_133.pdf) Some are already available in SuperCollider's LINK::Classes/Onsets:: object yet not as offline processes. It is part of the Fluid Decomposition Toolkit of the FluCoMa project.footnote::This was made possible thanks to the FluCoMa project ( http://www.flucoma.org/ ) funded by the European Research Council ( https://erc.europa.eu/ ) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 725899).::
This class implements many spectral-based onset detection metrics, most of them taken from the literature. (http://www.dafx.ca/proceedings/papers/p_133.pdf) Some are already available in SuperCollider's LINK::Classes/Onsets:: object yet not as offline processes. It is part of the LINK:: Guides/FluidDecomposition:: of LINK:: Guides/FluCoMa::. For more explanations, learning material, and discussions on its musicianly uses, visit http://www.flucoma.org/
The process will return a buffer which contains indices (in sample) of estimated starting points of different slices.
This class implements three popular pitch descriptors, computed as frequency and the confidence in its value. It is part of the Fluid Decomposition Toolkit of the FluCoMa project.FOOTNOTE:: This was made possible thanks to the FluCoMa project ( http://www.flucoma.org/ ) funded by the European Research Council ( https://erc.europa.eu/ ) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 725899).::
This class implements three popular pitch descriptors, computed as frequency and the confidence in its value. It is part of the LINK:: Guides/FluidDecomposition:: of LINK:: Guides/FluCoMa::. For more explanations, learning material, and discussions on its musicianly uses, visit http://www.flucoma.org/
The process will return a multichannel buffer with two channels per input channel, one for pitch and one for the pitch tracking confidence. Each sample represents a value, which is every hopSize. Its sampling rate is sourceSR / hopSize.
This class triggers a Sinusoidal Modelling process on buffers on the non-real-time thread of the server. It implements a mix and match algorithms taken from classic papers. It is part of the Fluid Decomposition Toolkit of the FluCoMa project. footnote:: This was made possible thanks to the FluCoMa project ( http://www.flucoma.org/ ) funded by the European Research Council ( https://erc.europa.eu/ ) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 725899).::
This class triggers a Sinusoidal Modelling process on buffers on the non-real-time thread of the server. It implements a mix of algorithms taken from classic papers. It is part of the LINK:: Guides/FluidDecomposition:: of LINK:: Guides/FluCoMa::. For more explanations, learning material, and discussions on its musicianly uses, visit http://www.flucoma.org/
The algorithm will take a buffer in, and will divide it in two parts: LIST::
## a reconstruction of what it detects as sinusoidal;
## a residual derived from the previous buffer to allow null-summing::
The whole process is based on the assumption that signal is made of pitched steady components that have a long-enough duration and are periodic enough to be perceived as such, that can be tracked, resynthesised and removed from the original, leaving behind what is considered as non-pitched, noisy, and/or transient. It first tracks the peaks, then checks if they are the continuation of a peak in previous spectral frames, by assigning them a track. More information on this model, and on how it links to musicianly thinking, are availabe in LINK::Guides/FluCoMa:: overview file.
The whole process is based on the assumption that signal is made of pitched steady components that have a long-enough duration and are periodic enough to be perceived as such, that can be tracked, resynthesised and removed from the original, leaving behind what is considered as non-pitched, noisy, and/or transient. It first tracks the peaks, then checks if they are the continuation of a peak in previous spectral frames, by assigning them a track.
This class implements seven of the most popular spectral shape descriptors, computed on a linear scale for both amplitude and frequency. It is part of the Fluid Decomposition Toolkit of the FluCoMa project.FOOTNOTE:: This was made possible thanks to the FluCoMa project ( http://www.flucoma.org/ ) funded by the European Research Council ( https://erc.europa.eu/ ) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 725899).::
This class implements seven of the most popular spectral shape descriptors, computed on a linear scale for both amplitude and frequency. It is part of the LINK:: Guides/FluidDecomposition:: of LINK:: Guides/FluCoMa::. For more explanations, learning material, and discussions on its musicianly uses, visit http://www.flucoma.org/
This class implements non-real-time statistical analysis on buffer channels. Typically, a buffer would hold various time series (i.e. descriptors over time), and link::Classes/FluidBufStats:: allows this series to be described statistically. It is part of the Fluid Decomposition Toolkit of the FluCoMa project. footnote::This was made possible thanks to the FluCoMa project ( http://www.flucoma.org/ ) funded by the European Research Council ( https://erc.europa.eu/ ) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 725899).::
This class implements non-real-time statistical analysis on buffer channels. Typically, a buffer would hold various time series (i.e. descriptors over time), and link::Classes/FluidBufStats:: allows this series to be described statistically. It is part of the LINK:: Guides/FluidDecomposition:: of LINK:: Guides/FluCoMa::. For more explanations, learning material, and discussions on its musicianly uses, visit http://www.flucoma.org/
The process returns a buffer where each channel of the STRONG::source:: buffer has been reduced to 7 statistics: mean, standard deviation, skewness, kurtosis, followed by 3 percentiles, by default the minimum value, the median, and the maximum value. Moreover, it is possible to request the same 7 stats to be applied to derivative of the input. These are useful to describe statistically the rate of change of the time series. The STRONG::stats:: buffer will grow accordingly, yielding the seven same statistical description of the n requested derivatives. Therefore, the STRONG::stats:: buffer will have as many channel as the input buffer, and as many frames as 7 times the requested STRONG::numDerivs::.
This class implements a simple tutorial object to illustrate and experiment with the behaviour of the FluidBuf* objects. It is part of the Fluid Decomposition Toolkit of the FluCoMa project.footnote::This was made possible thanks to the FluCoMa project ( http://www.flucoma.org/ ) funded by the European Research Council ( https://erc.europa.eu/ ) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 725899).:: It simply starts a process that will, upon completion of its task, write a single value to the destination buffer. This is the general behaviour of much of the CPU consuming FluidBuf* objects. In this case, as a toy example, the task is simply just wait and do nothing, to simulate a task that would actually take that long in the background.
This class implements a simple tutorial object to illustrate and experiment with the behaviour of the FluidBuf* objects. It is part of the LINK:: Guides/FluidDecomposition:: of LINK:: Guides/FluCoMa::. For more explanations, learning material, and discussions on its musicianly uses, visit http://www.flucoma.org/
It simply starts a process that will, upon completion of its task, write a single value to the destination buffer. This is the general behaviour of much of the CPU consuming FluidBuf* objects. In this case, as a toy example, the task is simply just wait and do nothing, to simulate a task that would actually take that long in the background.
The process will, after waiting for STRONG::time:: millisecond, return its delay lenght as a Float writen at index [0] of the specified destination buffer.
This class implements a non-real-time transient-based slice extractor relying on the same algorithm than Classes/FluidBufTransients using clicks/transients/derivation/anomaly in the signal to estimate the slicing points. It is part of the Fluid Decomposition Toolkit of the FluCoMa project.footnote::This was made possible thanks to the FluCoMa project ( http://www.flucoma.org/ ) funded by the European Research Council ( https://erc.europa.eu/ ) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 725899).::
This class implements a non-real-time transient-based slice extractor relying on the same algorithm than Classes/FluidBufTransients using clicks/transients/derivation/anomaly in the signal to estimate the slicing points. It is part of the LINK:: Guides/FluidDecomposition:: of LINK:: Guides/FluCoMa::. For more explanations, learning material, and discussions on its musicianly uses, visit http://www.flucoma.org/
The process will return a buffer which contains indices (in sample) of estimated starting points of the different slices.
This class triggers a transient extractor on buffers on the non-real-time thread of the server. It implements declicking algorithm from chapter 5 of the classic Digital Audio Restoration by Godsill, Simon J., Rayner, Peter J.W. with some bespoke improvements on the detection function tracking. It is part of the Fluid Decomposition Toolkit of the FluCoMa project. footnote::
This was made possible thanks to the FluCoMa project ( http://www.flucoma.org/ ) funded by the European Research Council ( https://erc.europa.eu/ ) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 725899).::
This class triggers a transient extractor on buffers on the non-real-time thread of the server. It implements declicking algorithm from chapter 5 of the classic Digital Audio Restoration by Godsill, Simon J., Rayner, Peter J.W. with some bespoke improvements on the detection function tracking. It is part of the LINK:: Guides/FluidDecomposition:: of LINK:: Guides/FluCoMa::. For more explanations, learning material, and discussions on its musicianly uses, visit http://www.flucoma.org/
The algorithm will take a buffer in, and will divide it in two outputs: LIST::
## the transients, estimated from the signal and extracted from it;
## the remainder of the material, as estimated by the algorithm.::
The whole process is based on the assumption that a transient is an element that is deviating from the surrounding material, as sort of click or anomaly. The algorithm then estimates what should have happened if the signal had followed its normal path, and resynthesises this estimate, removing the anomaly which is considered as the transient. More information on signal estimation, and on its musicianly usage, are availabe in LINK::Guides/FluCoMa:: overview file.
The whole process is based on the assumption that a transient is an element that is deviating from the surrounding material, as sort of click or anomaly. The algorithm then estimates what should have happened if the signal had followed its normal path, and resynthesises this estimate, removing the anomaly which is considered as the transient.
This class implements a sanity test for the FluCoMa Real-Time Client Wrapper. footnote::This was made possible thanks to the FluCoMa project ( http://www.flucoma.org/ ) funded by the European Research Council ( https://erc.europa.eu/ ) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 725899).::
This class implements a sanity test for the FluCoMa Real-Time Client Wrapper. It is part of the LINK:: Guides/FluidDecomposition:: of LINK:: Guides/FluCoMa::. For more explanations, learning material, and discussions on its musicianly uses, visit http://www.flucoma.org/
@ -14,11 +14,8 @@ The algorithm takes an audio in, and divides it into two or three outputs, depen
## a percussive component;
## a residual of the previous two if the flag is set to inter-dependant thresholds. See the maskingMode below.::
It is part of the Fluid Decomposition Toolkit of the FluCoMa project. footnote::
This was made possible thanks to the FluCoMa project ( http://www.flucoma.org/ ) funded by the European Research Council ( https://erc.europa.eu/ ) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 725899).
::
It is part of the LINK:: Guides/FluidDecomposition:: of LINK:: Guides/FluCoMa::. For more explanations, learning material, and discussions on its musicianly uses, visit http://www.flucoma.org/
More information on median filtering, and on HPSS for musicianly usage, are availabe in LINK::Guides/FluCoMa:: overview file.
This class implements two loudness descriptors, computing the true peak of the signal as well as applying the filters proposed by broadcasting standards to emulate the perception of amplitude. It is part of the Fluid Decomposition Toolkit of the FluCoMa project.FOOTNOTE::This was made possible thanks to the FluCoMa project ( http://www.flucoma.org/ ) funded by the European Research Council ( https://erc.europa.eu/ ) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 725899).::
This class implements two loudness descriptors, computing the true peak of the signal as well as applying the filters proposed by broadcasting standards to emulate the perception of amplitude. It is part of the LINK:: Guides/FluidDecomposition:: of LINK:: Guides/FluCoMa::. For more explanations, learning material, and discussions on its musicianly uses, visit http://www.flucoma.org/
The process will return a multichannel control steam with [loudness, truepeak] values, both in dBfs, which will be repeated if no change happens within the algorithm, i.e. when the hopSize is larger than the server's kr period. More information on broadcasting standardisation of loudness measurement is available at the reference page FOOTNOTE::https://tech.ebu.ch/docs/tech/tech3341.pdf:: and in more musician-friendly explantions here FOOTNOTE::http://designingsound.org/2013/02/06/loudness-and-metering-part-1/::.
This class implements a classic spectral descriptor, the Mel-Frequency Cepstral Coefficients (https://en.wikipedia.org/wiki/Mel-frequency_cepstrum). The input is first filtered in to STRONG::numBands:: perceptually-spaced bands, as in LINK::Classes/FluidMelBands::. It is then analysed into STRONG::numCoeffs:: number of cepstral coefficients. It has the avantage of being amplitude invarient, except for the first coefficient. It is part of the Fluid Decomposition Toolkit of the FluCoMa project.FOOTNOTE:: This was made possible thanks to the FluCoMa project ( http://www.flucoma.org/ ) funded by the European Research Council ( https://erc.europa.eu/ ) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 725899).::
This class implements a classic spectral descriptor, the Mel-Frequency Cepstral Coefficients (https://en.wikipedia.org/wiki/Mel-frequency_cepstrum). The input is first filtered in to STRONG::numBands:: perceptually-spaced bands, as in LINK::Classes/FluidMelBands::. It is then analysed into STRONG::numCoeffs:: number of cepstral coefficients. It has the avantage of being amplitude invarient, except for the first coefficient. It is part of the LINK:: Guides/FluidDecomposition:: of LINK:: Guides/FluCoMa::. For more explanations, learning material, and discussions on its musicianly uses, visit http://www.flucoma.org/
The process will return a multichannel control steam of STRONG::maxNumCoeffs::, which will be repeated if no change happens within the algorythm, i.e. when the hopSize is larger than the server's kr period.
This class implements a spectral shape descriptor where the amplitude is given for a number of equally spread perceptual bands. The spread is based on the Mel scale (https://en.wikipedia.org/wiki/Mel_scale) which is one of the first attempt to mimic pitch perception scientifically. This implementation allows to select the range and number of bands dynamically. It is part of the Fluid Decomposition Toolkit of the FluCoMa project.FOOTNOTE:: This was made possible thanks to the FluCoMa project ( http://www.flucoma.org/ ) funded by the European Research Council ( https://erc.europa.eu/ ) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 725899).::
This class implements a spectral shape descriptor where the amplitude is given for a number of equally spread perceptual bands. The spread is based on the Mel scale (https://en.wikipedia.org/wiki/Mel_scale) which is one of the first attempt to mimic pitch perception scientifically. This implementation allows to select the range and number of bands dynamically. It is part of the LINK:: Guides/FluidDecomposition:: of LINK:: Guides/FluCoMa::. For more explanations, learning material, and discussions on its musicianly uses, visit http://www.flucoma.org/
The process will return a multichannel control steam of size STRONG::maxNumBands::, which will be repeated if no change happens within the algorythm, i.e. when the hopSize is larger than the server's kr period.
@ -12,9 +12,7 @@ NMF has been a popular technique in signal processing research for things like s
The whole process can be related to a channel vocoder where, instead of fixed bandpass filters, we get more complex filter shapes and the activations correspond to channel envelopes.
More information on possible musicianly uses of NMF are availabe in LINK::Guides/FluCoMa:: overview file.
FluidBufNMF is part of the Fluid Decomposition Toolkit of the FluCoMa project. footnote::This was made possible thanks to the FluCoMa project ( http://www.flucoma.org/ ) funded by the European Research Council ( https://erc.europa.eu/ ) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 725899). ::
FluidBufNMF is part of the LINK:: Guides/FluidDecomposition:: of LINK:: Guides/FluCoMa::. For more explanations, learning material, and discussions on its musicianly uses, visit http://www.flucoma.org/
@ -12,9 +12,7 @@ NMF has been a popular technique in signal processing research for things like s
The whole process can be related to a channel vocoder where, instead of fixed bandpass filters, we get more complex filter shapes and the activations correspond to channel envelopes.
More information on possible musicianly uses of NMF are availabe in LINK::Guides/FluCoMa:: overview file.
FluidBufNMF is part of the Fluid Decomposition Toolkit of the FluCoMa project. footnote::This was made possible thanks to the FluCoMa project ( http://www.flucoma.org/ ) funded by the European Research Council ( https://erc.europa.eu/ ) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 725899). ::
FluidBufNMF is part of the LINK:: Guides/FluidDecomposition:: of LINK:: Guides/FluCoMa::. For more explanations, learning material, and discussions on its musicianly uses, visit http://www.flucoma.org/
This class implements a real-time slicer using an algorithm assessing novelty in the signal to estimate the slicing points. A novelty curve is being derived from running a kernel across the diagonal of the similarity matrix, and looking for peak of changes. It implements the seminal results published in 'Automatic Audio Segmentation Using a Measure of Audio Novelty' by J Foote. It is part of the Fluid Decomposition Toolkit of the FluCoMa project. footnote::This was made possible thanks to the FluCoMa project ( http://www.flucoma.org/ ) funded by the European Research Council ( https://erc.europa.eu/ ) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 725899).::
This class implements a real-time slicer using an algorithm assessing novelty in the signal to estimate the slicing points. A novelty curve is being derived from running a kernel across the diagonal of the similarity matrix, and looking for peak of changes. It implements the seminal results published in 'Automatic Audio Segmentation Using a Measure of Audio Novelty' by J Foote. It is part of the LINK:: Guides/FluidDecomposition:: of LINK:: Guides/FluCoMa::. For more explanations, learning material, and discussions on its musicianly uses, visit http://www.flucoma.org/
The process will return an audio steam with sample-long impulses at estimated starting points of the different slices.
This class implements many spectral based onset detection metrics, most of them taken from the literature. (http://www.dafx.ca/proceedings/papers/p_133.pdf) Some are already available in SuperCollider's LINK::Classes/Onsets:: object. It is part of the Fluid Decomposition Toolkit of the FluCoMa project.footnote::This was made possible thanks to the FluCoMa project ( http://www.flucoma.org/ ) funded by the European Research Council ( https://erc.europa.eu/ ) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 725899).::
This class implements many spectral based onset detection metrics, most of them taken from the literature. (http://www.dafx.ca/proceedings/papers/p_133.pdf) Some are already available in SuperCollider's LINK::Classes/Onsets:: object. It is part of the LINK:: Guides/FluidDecomposition:: of LINK:: Guides/FluCoMa::. For more explanations, learning material, and discussions on its musicianly uses, visit http://www.flucoma.org/
The process will return an audio steam with sample-long impulses at estimated starting points of the different slices.
This class implements three popular pitch descriptors, computed as frequency and the confidence in its value. It is part of the Fluid Decomposition Toolkit of the FluCoMa project.FOOTNOTE:: This was made possible thanks to the FluCoMa project ( http://www.flucoma.org/ ) funded by the European Research Council ( https://erc.europa.eu/ ) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 725899).::
This class implements three popular pitch descriptors, computed as frequency and the confidence in its value. It is part of the LINK:: Guides/FluidDecomposition:: of LINK:: Guides/FluCoMa::. For more explanations, learning material, and discussions on its musicianly uses, visit http://www.flucoma.org/
The process will return a multichannel control steam with [pitch, confidence] values, which will be repeated if no change happens within the algorithm, i.e. when the hopSize is larger than the server's kr period.
The process will return a multichannel control steam with [pitch, confidence] values, which will be repeated if no change happens within the algorithm, i.e. when the hopSize is larger than the server's kr period.
This class implements a sanity test for the FluCoMa Real-Time Client FFT/IFFT Wrapper. footnote::This was made possible thanks to the FluCoMa project ( http://www.flucoma.org/ ) funded by the European Research Council ( https://erc.europa.eu/ ) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 725899).::
This class implements a sanity test for the FluCoMa Real-Time Client FFT/IFFT Wrapper. It is part of the LINK:: Guides/FluidDecomposition:: of LINK:: Guides/FluCoMa::. For more explanations, learning material, and discussions on its musicianly uses, visit http://www.flucoma.org/
This class applies a Sinusoidal Modelling process on its audio input. It implements a mix and match algorithms taken from classic papers. It is part of the Fluid Decomposition Toolkit of the FluCoMa project. footnote:: This was made possible thanks to the FluCoMa project ( http://www.flucoma.org/ ) funded by the European Research Council ( https://erc.europa.eu/ ) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 725899).::
This class applies a Sinusoidal Modelling process on its audio input. It implements a mix of algorithms taken from classic papers. It is part of the LINK:: Guides/FluidDecomposition:: of LINK:: Guides/FluCoMa::. For more explanations, learning material, and discussions on its musicianly uses, visit http://www.flucoma.org/
The algorithm will take an audio in, and will divide it in two parts: LIST::
## a reconstruction of what it detects as sinusoidal;
## a residual derived from the previous signal to allow null-summing::
The whole process is based on the assumption that signal is made of pitched steady components that have a long-enough duration and are periodic enough to be perceived as such, that can be tracked, resynthesised and removed from the original, leaving behind what is considered as non-pitched, noisy, and/or transient. It first tracks the peaks, then checks if they are the continuation of a peak in previous spectral frames, by assigning them a track. More information on this model, and on how it links to musicianly thinking, are availabe in LINK::Guides/FluCoMa:: overview file.
The whole process is based on the assumption that signal is made of pitched steady components that have a long-enough duration and are periodic enough to be perceived as such, that can be tracked, resynthesised and removed from the original, leaving behind what is considered as non-pitched, noisy, and/or transient. It first tracks the peaks, then checks if they are the continuation of a peak in previous spectral frames, by assigning them a track.
This class implements seven of the most popular spectral shape descriptors, computed on a linear scale for both amplitude and frequency. It is part of the Fluid Decomposition Toolkit of the FluCoMa project.FOOTNOTE:: This was made possible thanks to the FluCoMa project ( http://www.flucoma.org/ ) funded by the European Research Council ( https://erc.europa.eu/ ) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 725899).::
This class implements seven of the most popular spectral shape descriptors, computed on a linear scale for both amplitude and frequency. It is part of the LINK:: Guides/FluidDecomposition:: of LINK:: Guides/FluCoMa::. For more explanations, learning material, and discussions on its musicianly uses, visit http://www.flucoma.org/
This class implements a real-time transient-based slice extractor relying on the same algorithm than Classes/FluidBufTransients using clicks/transients/derivation/anomaly in the signal to estimate the slicing points. It is part of the Fluid Decomposition Toolkit of the FluCoMa project.footnote::This was made possible thanks to the FluCoMa project ( http://www.flucoma.org/ ) funded by the European Research Council ( https://erc.europa.eu/ ) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 725899).::
This class implements a real-time transient-based slice extractor relying on the same algorithm than Classes/FluidBufTransients using clicks/transients/derivation/anomaly in the signal to estimate the slicing points. It is part of the LINK:: Guides/FluidDecomposition:: of LINK:: Guides/FluCoMa::. For more explanations, learning material, and discussions on its musicianly uses, visit http://www.flucoma.org/
The process will return an audio steam with sample-long impulses at estimated starting points of the different slices.
The process will return an audio steam with sample-long impulses at estimated starting points of the different slices.
This class applies a real-time transient extractor on its input. It implements declicking algorithm from chapter 5 of the classic Digital Audio Restoration by Godsill, Simon J., Rayner, Peter J.W. with some bespoke improvements on the detection function tracking. It is part of the Fluid Decomposition Toolkit of the FluCoMa project. footnote::
This was made possible thanks to the FluCoMa project ( http://www.flucoma.org/ ) funded by the European Research Council ( https://erc.europa.eu/ ) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 725899). ::
This class applies a real-time transient extractor on its input. It implements declicking algorithm from chapter 5 of the classic Digital Audio Restoration by Godsill, Simon J., Rayner, Peter J.W. with some bespoke improvements on the detection function tracking. It is part of the LINK:: Guides/FluidDecomposition:: of LINK:: Guides/FluCoMa::. For more explanations, learning material, and discussions on its musicianly uses, visit http://www.flucoma.org/
The algorithm will take an audio in, and will divide it in two outputs: LIST::
## the transients, estimated from the signal and extracted from it;
## the remainder of the material, as estimated by the algorithm. ::
The whole process is based on the assumption that a transient is an element that is deviating from the surrounding material, as sort of click or anomaly. The algorithm then estimates what should have happened if the signal had followed its normal path, and resynthesises this estimate, removing the anomaly which is considered as the transient. More information on signal estimation, and on its musicianly usage, are availabe in LINK::Guides/FluCoMa:: overview file.
The whole process is based on the assumption that a transient is an element that is deviating from the surrounding material, as sort of click or anomaly. The algorithm then estimates what should have happened if the signal had followed its normal path, and resynthesises this estimate, removing the anomaly which is considered as the transient.
The Fluid Corpus Manipulation project (FluCoMA) instigates new musical ways of exploiting ever-growing banks of sound and gestures within the digital composition process, by bringing breakthroughs of signal decomposition DSP and machine learning to the toolset of techno-fluent computer composers, creative coders and digital artists.
The Fluid Corpus Manipulation project (FluCoMA) instigates new musical ways of exploiting ever-growing banks of sound and gestures within the digital composition process, by bringing breakthroughs of signal decomposition DSP and machine learning to the toolset of techno-fluent computer composers, creative coders and digital artists. The first set of tools released is the LINK:: Guides/FluidDecomposition::
This was made possible thanks to a grant by the European Research Council ( https://erc.europa.eu/ ) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 725899).
Pierre Alexandre Tremblay
6 years ago