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238 lines
6.4 KiB
Markdown
238 lines
6.4 KiB
Markdown
# Samplebrain Manual
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A custom sample mashing app designed by Aphex Twin.
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Samplebrain chops samples up into a 'brain' of interconnected small
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sections called blocks which are connected into a network by
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similarity. It processes a target sample, chopping it up into blocks
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in the same way, and tries to match each block with one in it's brain
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to play in realtime.
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This allows you to interpret a sound with a different one. As we
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worked on it (during 2015 and 2016) we gradually added more and more
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tweakable parameters until it became slightly out of control.
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## Brain tweaks:
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These settings control how the block search works.
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### fft / mfcc
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Choose whether to match blocks using FFT (raw frequency analysis) or
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MFCC (Mel-frequency cepstral coefficients, parameters that attempt to
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model perception of sound). Which is best depends on the sounds you
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are using, so you can blend between them to use a mix. Setting this to
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0% or 100% switches off the other search option, so is a bit more CPU
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friendly.
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### freq & dynamics / freq only
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Search using both frequency (pitch) and dynamics (volume changes over
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time), or only frequency - which uses normalised blocks. Generally you
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want the first option.
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### fft subsection
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You can select a subrange of the (100) frequency bins we use for
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scoring potential blocks, potentially allowing you to target a
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specific frequency range. Not that useful in practice so far.
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### novelty
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Often the same block or set of blocks tend to be overused if there
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isn't enough variation in the brain. You can use 'novelty' to bias the
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selection away from similarity, and prioritise similar blocks we
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haven't used yet.
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### boredom
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This increases the speed at which novelty wears off, creating a wider
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spread of possible blocks to be used.
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If you turn novelty and boredom all the way up it will ignore the
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target completely and just play brain samples in some odd semi-random
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order.
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### stickyness
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If the error of the next block in the sequence after the one we have
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just used is under the stickyness threshold, we will use that rather
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than the closest in our search. This will have the effect of
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elongating chunks of brain samples that you hear.
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### search stretch
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Repeats blocks in the target a fixed amount, like a simple timestretch
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- using the above controls it won't necessarily repeat the same brain
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block, and in synaptic mode (see below) this gives the system repeated
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attempts to find a closer match in the network.
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### algorithm
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_basic_
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Searches all sample blocks. Not usable with large brains.
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_reversed_
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Searches all samples in the brain, but selects the least closest match
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instead of the closest. In practice this needs work, as silent or very
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quiet blocks tend to be the least similar.
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_synaptic_
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As brains get larger with more blocks, they get slower to search. This
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mode provides a constant search time over arbitrarily large brains. To
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do this we connect the blocks together into a network via similarity
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(via connections called synapses). We store a current "playback
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position" in the network and only search the nearby blocks the playing
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block is connected to. This assumes that sounds tend to change
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gradually, or at least more gradually than the smaller block lengths.
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This can leads to the output changing with each repetition of the
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target, as we wander around the synapse network.
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_slide_
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Similar to synaptic mode above but if we can't find a close enough
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match (based on synaptic slide error) we repeat the target block
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again, stretching it until we land on a block that is close
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enough. This mode unpredictably warps the timing of the target sound.
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### num synapses
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How many connections to check in synaptic or slide mode, up to a
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maximum of 1000 - they are ordered by closeness.
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### synaptic slide error
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The acceptable error to consider a block as "close enough" in slide mode.
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## Target sound:
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These settings control how the target sound is broken up into blocks.
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### load target
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Load a target sound to try and match
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### block size
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The size of the blocks in samples. This does not need to match the
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brain block size.
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### block overlap
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Proportion to overlap the block generation.
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### window shape
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The windowing function for the target blocks, the volume envelope
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applied to them before analysis. Dodgy is a kind of linear fade in and
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out, which was left in for legacy compatibility with prototype
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versions.
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### (re)generate blocks
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Compute the target blocks.
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### use mic input
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Attempts to stream blocks live from the microphone. This has not been
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tested fully.
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## Mix:
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These are settings that control things happening after the search.
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### autotune
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Attempt to pitch bend the chosen brain block to better match the target.
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### normalised
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Mix in normalised brain blocks - removing all dynamics. Designed to
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work with frequency only search.
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### brain / target
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Mix in the target blocks to the output - for cheating, or testing
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purposes.
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### stereo mode
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Run separate searches for left and right speaker.
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## Brain contents
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These settings allow you to build your sample brain, and switch in and
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out specific samples live during playback.
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### all/none
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You can select which samples to use without regenerating the
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brain. This selects all or none of the samples.
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### load sound/directory/clear
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Load sounds into the brain, either individually or entire directories
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in one go.
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### block size
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The size of the blocks in samples. This does not need to match the
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target block size.
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### block overlap
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Proportion to overlap the block generation.
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### window shape
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The windowing function for the brain blocks, the volume envelope
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applied to them before analysis. Dodgy is a kind of linear fade in and
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out, which was left in for legacy compatibility with prototype
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versions.
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### (re)generate blocks
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Compute the brain blocks.
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### load brain/save brain
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You can save and load brains separately to the targets.
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## Lower bar
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General playback settings.
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### play/pause/record/stop
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Start/stop and record.
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### volume
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Global volume.
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### load/save session
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Load and save the entire session.
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## Net tab
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This allows you to control multiple instances of samplebrain over the
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network all running their own brains simultaneously. This feature has
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not been tested well.
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