Tutorials and Topics - Peabody Computer Music

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Tutorial 26Frequency domain signalprocessing using pfft~The following example records spectral analysis data into two channels of a stereo buffer~and then allows you to resynthesize the recording at a different speed.Recording and playback in a pfft~ subpatch216
Tutorial 26Frequency domain signalprocessing using pfft~The example subpatcher records spectral data into a buffer~, and the second reads datafrom that buffer~. In the recording portion of the subpatch you will notice that we don’tjust record the amplitude and phase as output from cartopol~, but instead use theframedelta~ object to compute the phase difference (sometimes referred to as the phasedeviation, or phase derivative). The phase difference is quite simply the difference inphase between equivalent bin locations in successive FFT frames. The output offramedelta~ is then fed into a phasewrap~ object to ensure that the data is properlyconstrained between - and . Messages can be sent to the record~ object from the parentpatch via the send object in order to start and stop recording and turn on looping.In the playback part of the subpatch we use a non-signal inlet to specify the framenumber for the resynthesis. This number is multiplied by the spectral frame size andadded to the output of a count~ object which counts from 0 to the spectral frame sizeminus 1 in order to be able to recall each frequency bin in the given frame successivelyusing index~ to read both channels of our buffer~. (We could also have used the syncoutlet of the fftin~ object in place of count~, but are using the current method for the sakeof visually separating the recording and playback parts of our subpatch, as well as to givean example of how to make use of count~ in the context of a spectral subpatch.) You’llnotice that we reconstruct the phase using the frameaccum~ object, which accumulates a“running phase” value by performing the inverse of framedelta~. We need to do thisbecause we might not be reading the analysis frames successively at the original rate inwhich they were recorded. The signals are then converted back into real and imaginaryvalues for fftout~ by the poltocar~ object.This is a simple example of what is known as a phase vocoder. Phase vocoders allow youto time- stretch and compress signals independently of their pitch by manipulating FFTdata rather than time-domain segments. If you think of each frame of an FFT analysis as asingle frame in a film, you can easily see how moving through the individual frames atdifferent rates can change the apparent speed at which things happen. This is more or lesswhat a phase vocoder does.Note that because pfft~ does window overlapping, the amount of data that can be storedin the buffer~ is dependent on the settings of the pfft~ object. This can make setting thebuffer size correctly a rather tricky matter, especially since the spectral frame size (i.e. thesignal vector size) inside a pfft~ is half the FFT size indicated as its second argument, andbecause the spectral subpatch is processing samples at a different rate to its parent patch!If we create a stereo buffer~ with 1000 milliseconds of sample memory, we will have 44100samples available for our analysis data. If our FFT size is 1024 then each spectral framewill take up 512 samples of our buffer’s memory, which amounts to 86 frames of analysisdata(44100 / 512 = 86.13). Those 86 frames do not represent one second of sound,however! If we are using 4-times overlap, we are processing one spectral frame every 256samples, so 86 frames means roughly 22050 samples, or a half second’s worth of time withrespect to the parent patch. As you can see this all can get rather complicated...217
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MSPTutorials and TopicsVersion 4.5.
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Table of ContentsCopyright and Trad
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Table of ContentsExercise 3 .......
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Table of ContentsVarying parameters
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IntroductionWith the addition of th
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IntroductionThe Max/MSP Examples fo
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How Digital Audio Works• The fluc
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How Digital Audio WorksBecause the
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How Digital Audio Worksthough, the
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How Digital Audio Worksnumbers from
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How Digital Audio Worksthreshold. A
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How Digital Audio WorksIn this case
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How Digital Audio Worksquality”)
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How Digital Audio WorksManipulating
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How MSP WorksMax patches and theMSP
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Audio I/OAudio input andoutput with
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Tutorial 1: Fundamentals—Test ton
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Tutorial 1Fundamentals:Test toneWav
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Tutorial 1Fundamentals:Test tonesou
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Tutorial 2Fundamentals:Adjustable o
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Tutorial 3Fundamentals:Wavetable os
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Tutorial 4Fundamentals:Routing sign
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Tutorial 5Fundamentals:Turning sign
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Tutorial 6A review of fundamentals1
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Tutorial 6A review of fundamentalsW
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Tutorial 6A review of fundamentalsS
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Tutorial 7Synthesis:Additive synthe
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Tutorial 7Synthesis:Additive synthe
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Multiplying signalsTutorial 8: Synt
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Tutorial 8Synthesis:Tremolo and rin
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Tutorial 9: Synthesis—Amplitude m
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Tutorial 9Synthesis:Amplitude modul
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Tutorial 10: Synthesis—Vibrato an
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Elements of FM synthesisTutorial 11
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Tutorial 11Synthesis:Frequency modu
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Tutorial 11Synthesis:Frequency modu
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Tutorial 12Synthesis:WaveshapingThe
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Tutorial 12Synthesis:Waveshaping•
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Sound input: adc~Tutorial 13: Sampl
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Tutorial 13Sampling:Recording and p
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Tutorial 13Sampling:Recording and p
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Playing samples with groove~Tutoria
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Tutorial 14Sampling:Playback with l
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Tutorial 15Sampling:Variable-length
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Tutorial 16Sampling:Record and play
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Tutorial 16Sampling:Record and play
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Tutorial 17Sampling reviewThe compu
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Tutorial 17Sampling reviewThe keyst
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Tutorial 18: MIDI control—Mapping
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Tutorial 18MIDI Control:Mapping MID
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Tutorial 18MIDI Control:Mapping MID
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Implementing standard MIDI messages
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Tutorial 19MIDI Control:Synthesizer
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Tutorial 20: MIDI control—Sampler
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Tutorial 20MIDI Control:SamplerThis
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Page 186 and 187: Tutorial 23Analysis:Viewing signal
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Page 196 and 197: Tutorial 25Analysis:Using the FFTAl
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Page 202 and 203: Tutorial 26Frequency domain signalp
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Page 222 and 223: Tutorial 27Processing:Delay linesEq
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Page 228 and 229: Tutorial 29Processing:FlangeWhile t
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Page 234 and 235: Tutorial 30Processing:ChorusMultipl
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Page 238 and 239: Tutorial 31Processing:Comb filterTh
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Tutorials and Topics - Peabody Computer Music

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