@ -20,9 +20,7 @@ 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.
More information on median filtering, and on HPSS for musicianly usage, are availabe in LINK::Guides/FluCoMa:: overview file.
CLASSMETHODS::
@ -66,10 +64,10 @@ ARGUMENT:: percFiltSize
ARGUMENT:: modeFlag
The way the masking is applied to the original spectrogram. (0,1,2)
table::
## 0 || Fitzgerald's original method of 'Wiener-inspired' filtering. Compllimentary, soft masks are made for the harmonic and percussive parts by allocating some fraction of a point in time-frequency to each. This provides the fewest artefacts, but the weakest separation. The two resulting buffers will sum to exactly the original material.
## 0 || The traditional soft mask used in Fitzgerald's original method of 'Wiener-inspired' filtering. Complimentary, soft masks are made for the harmonic and percussive parts by allocating some fraction of a point in time-frequency to each. This provides the fewest artefacts, but the weakest separation. The two resulting buffers will sum to exactly the original material.
## 1 || Relative mode - Better separation, with more artefacts. The harmonic mask is constructed using a binary decision, based on whether a threshold is exceeded at a given time-frequency point (these are set using htf1, hta1, htf2, hta2, see below). The percussive mask is then formed as the inverse of the harmonic one, meaning that as above, the two components will sum to the original sound.
## 2 || Inter-dependent mode - Thresholds can be varied independently, but are coupled in effect. Binary masks are made for each of the harmonic and percussive components, but these aren't gurranteed to cover the whole sound. In this case the 'leftovers' will placed into a third buffer. This method tuneable, but hardest to control.
## 2 || Inter-dependent mode - Thresholds can be varied independently, but are coupled in effect. Binary masks are made for each of the harmonic and percussive components, and the masks are converted to soft at the end so that everything null sums even if the params are independent, that is what makes it harder to control. These aren't guranteed to cover the whole sound; in this case the 'leftovers' will placed into a third buffer.
The FluidBufNMF object decomposes the spectrum of a sound into a number of components using Non-Negative Matrix Factorisation (NMF) footnote:: Lee, Daniel D., and H. Sebastian Seung. 1999. ‘Learning the Parts of Objects by Non-Negative Matrix Factorization’. Nature 401 (6755): 788–91. https://doi.org/10.1038/44565.
::. NMF has been a popular technique in signal processing research for things like source separation and transcription, although its creative potential is so far relatively unexplored.
::. NMF has been a popular technique in signal processing research for things like source separation and transcription footnote:: Smaragdis and Brown, Non-Negative Matrix Factorization for Polyphonic Music Transcription.::, although its creative potential is so far relatively unexplored.
The algorithm takes a buffer in and divides it into a number of components, determined by the rank argument. It works iteratively, by trying to find a combination of spectral templates ('dictionaries') and envelopes ('activations') that yield the original magnitude spectrogram when added together. By and large, there is no unique answer to this question (i.e. there are different ways of accounting for an evolving spectrum in terms of some set of templates and envelopes). In its basic form, NMF is a form of unsupervised learning: it starts with some random data and then converges towards something that minimizes the distance between its generated data and the original:it tends to converge very quickly at first and then level out. Fewer iterations mean less processing, but also less predictable results.
The object can provide back either or all of the following: LIST::
The object can return either or all of the following: LIST::
## a spectral contour of each component in the form of a magnitude spectrogram (called a dictionary in NMF lingo);
## an amplitude envolope of each component in the form of gains for each consecutive frame of the unterlying spectrogram (called an activation in NMF lingo);
## a reconstruction of each rank in the time domain. ::
## an amplitude envelope of each component in the form of gains for each consecutive frame of the underlying spectrogram (called an activation in NMF lingo);
## an audio reconstruction of each components in the time domain. ::
The dictionaries and activations can be used to make a kind of vocoder based on what NMF has 'learned' from the original data. Alternatively, taking the matrix product of a dictionary and an activation will yield a synthetic magnitude spectrogram of a component (which could be reconsructed, given some phase informaiton from somewhere).
Some additional options and flexibility can be found through combinations of the dictFlag and actFlag arguments. If this flag is set to 1, then object expects to be supplied with pre-formed spectra (resp. envelopes) that will be used as seeds for the decomposition, providing more guided results. When set to 2, the supplied buffers won't be updated, so become templates to match against instead. Note that having both dictionaries and activations set to 2 doesn't make sense, so the object will complain.
Some additional options and flexibility can be found through combinations of the dictFlag and actFlag arguments. If these flags are set to 1, the object expects to be supplied with pre-formed spectra (or envelopes) that will be used as seeds for the decomposition, providing more guided results. When set to 2, the supplied buffers won't be updated, so become templates to match against instead. Note that having both dictionaries and activations set to 2 doesn't make sense, so the object will complain.
If supplying pre-formed data, it's up to the user to make sure that the supplied buffers are the right size: LIST::
## dictionaries must be STRONG::(fft size / 2) + 1:: frames and STRONG::(rank * input channels):: channels
@ -63,7 +63,7 @@ ARGUMENT:: dstBufNum
The index of the buffer where the different reconstructed ranks will be reconstructed. The buffer will be resized to STRONG::rank * numChannelsProcessed:: channels and STRONG::sourceDuration:: lenght. If STRONG::nil:: is provided, the reconstruction will not happen.
ARGUMENT:: dictBufNum
The index of the buffer where the different dictionaries will be written to and/or read from: the behaviour is set in the following argument. If STRONG::nil:: is provided, no dictionary will be provided.
The index of the buffer where the different dictionaries will be written to and/or read from: the behaviour is set in the following argument. If STRONG::nil:: is provided, no dictionary will be returned.
ARGUMENT:: dictFlag
This flag decides of how the dictionnary buffer passed as the previous argument is treated.
@ -74,7 +74,7 @@ ARGUMENT:: dictFlag
::
ARGUMENT:: actBufNum
The index of the buffer where the different activations will be written to and/or read from: the behaviour is set in the following argument. If STRONG::nil:: is provided, no activation will be provided.
The index of the buffer where the different activations will be written to and/or read from: the behaviour is set in the following argument. If STRONG::nil:: is provided, no activation will be returned.
ARGUMENT:: actFlag
This flag decides of how the activation buffer passed as the previous argument is treated.
@ -85,10 +85,10 @@ ARGUMENT:: actFlag
::
ARGUMENT:: rank
The number of elements the NMF algorythim will try to divide the spectrogram of the source in.
The number of elements the NMF algorithm will try to divide the spectrogram of the source in.
ARGUMENT:: nIter
The NMF process is iterative, trying to converge to the smallest error in its factorisation. The number of iterations will decide how many times it tries to adjust its guestimates. Higher numbers here will be more CPU expensive, lower numbers will be more unpredictable in quality.
The NMF process is iterative, trying to converge to the smallest error in its factorisation. The number of iterations will decide how many times it tries to adjust its estimates. Higher numbers here will be more CPU expensive, lower numbers will be more unpredictable in quality.
ARGUMENT:: sortFlag
This allows to choose between the different methods of sorting the ranks in order to get similar sonic qualities on a given rank (not implemented yet)
This class implements a non-real-time slicer implementing an algorithm assessing novelty in the signal to estimate the slicing points. 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 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).::
The process will return a buffer which contains indices (in sample) of estimated starting points of different slices.
@ -40,7 +40,7 @@ ARGUMENT:: kernelSize
The granularity of the window in which the algorithm looks for change, in samples. A small number will be sensitive to short term changes, and a large number should look for long term changes.
ARGUMENT:: thresh
The threshold in the novelty curve, the latter being derived from running the kernel across the diagonal of the similarity matrix, and looking for peak of changes (NOT CLEAR)
The normalised threshold, between 0 an 1, to consider a peak as a sinusoidal component from the in the novelty curve.
ARGUMENT:: winSize
The window size. As novelty estimation relies on spectral frames, we need to decide what precision we give it spectrally and temporally, in line with Gabor Uncertainty principles. http://www.subsurfwiki.org/wiki/Gabor_uncertainty
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).::
The algorithm will take a buffer in, and will divide it in two parts: LIST::
## a reconstruction of what its detects as sinusoidal;
## 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.
@ -25,7 +25,7 @@ ARGUMENT:: srcBufNum
The index of the buffer to use as the source material to be decomposed through the NMF process. The different channels of multichannel buffers will be processing sequentially.
ARGUMENT:: startAt
Where in the srcBuf should the NMF process start, in sample.
Where in the srcBuf should the process start, in sample.
ARGUMENT:: nFrames
How many frames should be processed.
@ -43,13 +43,13 @@ ARGUMENT:: resBufNum
The index of the buffer where the residual of the sinusoidal component will be reconstructed.
ARGUMENT:: bandwidth
The width in bins (OR IN PERCENT IN NEW INTERFACE) of the fragment of the fft window that is considered a normal deviation for a potential continuous sinusoidal track. It has an effect on CPU cost: the widest is more accurate but more computationally expensive.
The width in bins of the fragment of the fft window that is considered a normal deviation for a potential continuous sinusoidal track. It has an effect on CPU cost: the widest is more accurate but more computationally expensive.
ARGUMENT:: thresh
The normalised threshold, between 0 an 1, to consider a peak as a sinusoidal component from the normalized cross-correlation.
ARGUMENT:: minTrackLen
The minimum duration, in spectral frames, for a sinusoidal track to be accepted as a real pitch component. It allows to remove space-monkeys, but is more CPU intensive and might reject quick pitch material.
The minimum duration, in spectral frames, for a sinusoidal track to be accepted as a partial. It allows to remove space-monkeys, but is more CPU intensive and might reject quick pitch material.
ARGUMENT:: magWeight
The weight of the magnitude proximity of a peak when trying to associate it to an existing track (relative to freqWeight - suggested between 0 to 1)
The index of the buffer where the indices (in sample) of the estimated starting points of slices will be written. The first and last points are always the boundary points of the analysis.
ARGUMENT:: order
The order in samples of the impulse response filter used to model the estimated continuous signal. It is how many previous samples are used by the algorythm to predict the next one as reference for the model. The higher the order, the more accurate is its spectral definition, not unlike fft, improving low frequency resolution, but it differs in that it is not conected to its temporal resolution.
The order in samples of the impulse response filter used to model the estimated continuous signal. It is how many previous samples are used by the algorithm to predict the next one as reference for the model. The higher the order, the more accurate is its spectral definition, not unlike fft, improving low frequency resolution, but it differs in that it is not conected to its temporal resolution.
ARGUMENT:: blockSize
The size in samples of frame on which it the algorithm is operating. High values are more cpu intensive, and also determines the maximum transient size, which will not be allowed to be more than half that lenght in size.
@ -55,10 +55,10 @@ ARGUMENT:: threshBack
The threshold of the offset of the smoothed error function. As it proceeds backwards in time, it allows tight ending of the identification of the anomaly.
ARGUMENT:: winSize
The averaging window of the error detection function. It needs smoothing as it is very jittery. The longer the window, the less precise, but the less false positive.
The averaging window of the error detection function. It needs smoothing as it is very jittery. The longer the window, the less precise, but the less false positives.
ARGUMENT:: debounce
The window size in sample within with positive detections will be clumped together to avoid overdetecting in time. No slice will be shorter than this duration.
The window size in sample within which positive detections will be clumped together to avoid overdetecting in time. No slice will be shorter than this duration.
RETURNS::
Nothing, as the destination buffer is declared in the function call.
This class triggers non-real-time 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 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).::
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 algorythm 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. More information on signal estimation, and on its musicianly usage, are availabe in LINK::Guides/FluCoMa:: overview file.
CLASSMETHODS::
@ -45,7 +45,7 @@ ARGUMENT:: resBufNum
The index of the buffer where the estimated continuous component will be reconstructed.
ARGUMENT:: order
The order in samples of the impulse response filter used to model the estimated continuous signal. It is how many previous samples are used by the algorythm to predict the next one as reference for the model. The higher the order, the more accurate is its spectral definition, not unlike fft, improving low frequency resolution, but it differs in that it is not conected to its temporal resolution.
The order in samples of the impulse response filter used to model the estimated continuous signal. It is how many previous samples are used by the algorithm to predict the next one as reference for the model. The higher the order, the more accurate is its spectral definition, not unlike fft, improving low frequency resolution, but it differs in that it is not conected to its temporal resolution.
ARGUMENT:: blockSize
The size in samples of frame on which it the algorithm is operating. High values are more cpu intensive, and also determines the maximum transient size, which will not be allowed to be more than half that lenght in size.
@ -66,7 +66,7 @@ ARGUMENT:: winSize
The averaging window of the error detection function. It needs smoothing as it is very jittery. The longer the window, the less precise, but the less false positive.
ARGUMENT:: debounce
The window size in sample within with positive detections will be clumped together to avoid overdetecting in time.
The window size in sample within which positive detections will be clumped together to avoid overdetecting in time.
RETURNS::
Nothing, as the various destination buffers are declared in the function call.
@ -18,9 +18,7 @@ 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.
More information on median filtering, and on HPSS for musicianly usage, are availabe in LINK::Guides/FluCoMa:: overview file.
CLASSMETHODS::
METHOD:: ar
@ -38,10 +36,10 @@ ARGUMENT:: percFiltSize
ARGUMENT:: modeFlag
The way the masking is applied to the original spectrogram. (0,1,2)
table::
## 0 || Fitzgerald's original method of 'Wiener-inspired' filtering. Compllimentary, soft masks are made for the harmonic and percussive parts by allocating some fraction of a point in time-frequency to each. This provides the fewest artefacts, but the weakest separation. The two resulting buffers will sum to exactly the original material.
## 0 || The traditional soft mask used in Fitzgerald's original method of 'Wiener-inspired' filtering. Complimentary, soft masks are made for the harmonic and percussive parts by allocating some fraction of a point in time-frequency to each. This provides the fewest artefacts, but the weakest separation. The two resulting buffers will sum to exactly the original material.
## 1 || Relative mode - Better separation, with more artefacts. The harmonic mask is constructed using a binary decision, based on whether a threshold is exceeded at a given time-frequency point (these are set using htf1, hta1, htf2, hta2, see below). The percussive mask is then formed as the inverse of the harmonic one, meaning that as above, the two components will sum to the original sound.
## 2 || Inter-dependent mode - Thresholds can be varied independently, but are coupled in effect. Binary masks are made for each of the harmonic and percussive components, but these aren't gurranteed to cover the whole sound. In this case the 'leftovers' will placed into a third buffer. This method tuneable, but hardest to control.
## 2 || Inter-dependent mode - Thresholds can be varied independently, but are coupled in effect. Binary masks are made for each of the harmonic and percussive components, and the masks are converted to soft at the end so that everything null sums even if the params are independent, that is what makes it harder to control. These aren't guranteed to cover the whole sound; in this case the 'leftovers' will placed into a third buffer.
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).::
The algorithm will take an audio in, and will divide it in two parts: LIST::
## a reconstruction of what its detects as sinusoidal;
## 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.
@ -22,13 +22,13 @@ ARGUMENT:: in
The input to be processed
ARGUMENT:: bandwidth
The width in bins (OR IN PERCENT IN NEW INTERFACE) of the fragment of the fft window that is considered a normal deviation for a potential continuous sinusoidal track. It has an effect on CPU cost: the widest is more accurate but more computationally expensive.
The width in bins of the fragment of the fft window that is considered a normal deviation for a potential continuous sinusoidal track. It has an effect on CPU cost: the widest is more accurate but more computationally expensive.
ARGUMENT:: thresh
The normalised threshold, between 0 an 1, to consider a peak as a sinusoidal component from the normalized cross-correlation.
ARGUMENT:: minTrackLen
The minimum duration, in spectral frames, for a sinusoidal track to be accepted as a real pitch component. It allows to remove space-monkeys, but is more CPU intensive and might reject quick pitch material.
The minimum duration, in spectral frames, for a sinusoidal track to be accepted as a partial. It allows to remove space-monkeys, but is more CPU intensive and might reject quick pitch material.
ARGUMENT:: magWeight
The weight of the magnitude proximity of a peak when trying to associate it to an existing track (relative to freqWeight - suggested between 0 to 1)
The order in samples of the impulse response filter used to model the estimated continuous signal. It is how many previous samples are used by the algorythm to predict the next one as reference for the model. The higher the order, the more accurate is its spectral definition, not unlike fft, improving low frequency resolution, but it differs in that it is not conected to its temporal resolution.
The order in samples of the impulse response filter used to model the estimated continuous signal. It is how many previous samples are used by the algorithm to predict the next one as reference for the model. The higher the order, the more accurate is its spectral definition, not unlike fft, improving low frequency resolution, but it differs in that it is not conected to its temporal resolution.
ARGUMENT:: blockSize
The size in samples of frame on which it the algorithm is operating. High values are more cpu intensive, and also determines the maximum transient size, which will not be allowed to be more than half that lenght in size.
@ -35,7 +35,7 @@ ARGUMENT:: threshBack
The threshold of the offset of the smoothed error function. As it proceeds backwards in time, it allows tight ending of the identification of the anomaly.
ARGUMENT:: winSize
The averaging window of the error detection function. It needs smoothing as it is very jittery. The longer the window, the less precise, but the less false positive.
The averaging window of the error detection function. It needs smoothing as it is very jittery. The longer the window, the less precise, but the less false positives.
ARGUMENT:: debounce
The window size in sample within with positive detections will be clumped together to avoid overdetecting in time. No slice will be shorter than this duration.
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 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).::
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 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 algorythm 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. More information on signal estimation, and on its musicianly usage, are availabe in LINK::Guides/FluCoMa:: overview file.
CLASSMETHODS::
@ -22,7 +22,7 @@ ARGUMENT:: in
The audio to be processed.
ARGUMENT:: order
The order in samples of the impulse response filter used to model the estimated continuous signal. It is how many previous samples are used by the algorythm to predict the next one as reference for the model. The higher the order, the more accurate is its spectral definition, not unlike fft, improving low frequency resolution, but it differs in that it is not conected to its temporal resolution.
The order in samples of the impulse response filter used to model the estimated continuous signal. It is how many previous samples are used by the algorithm to predict the next one as reference for the model. The higher the order, the more accurate is its spectral definition, not unlike fft, improving low frequency resolution, but it differs in that it is not conected to its temporal resolution.
ARGUMENT:: blockSize
The size in samples of frame on which it the algorithm is operating. High values are more cpu intensive, and also determines the maximum transient size, which will not be allowed to be more than half that lenght in size.
@ -43,7 +43,7 @@ ARGUMENT:: winSize
The averaging window of the error detection function. It needs smoothing as it is very jittery. The longer the window, the less precise, but the less false positive.
ARGUMENT:: debounce
The window size in sample within with positive detections will be clumped together to avoid overdetecting in time.
The window size in sample within which positive detections will be clumped together to avoid overdetecting in time.
RETURNS::
An array of two audio streams: [0] is the transient extracted, [1] is the rest. The latency between the input and the output is (blockSize + padSize - order) samples.
@ -51,7 +51,7 @@ RETURNS::
EXAMPLES::
CODE:
CODE::
//load some buffer
b = Buffer.read(s,File.realpath(FluidTransients.class.filenameSymbol).dirname.replace("Classes","AudioFiles/Tremblay-AaS-SynthTwoVoices-M.wav"));
Pierre Alexandre Tremblay
7 years ago