Streamsep

Library for polyphonic stream segregation in OpenMusic.

Includes a subclass of 'analysis' to work interactively with stream separation within segments.

Check the tutorial files for examples of usage.

Install in the usual ways for OM libraries.

Anders Vinjar, 2021

The terms 'Stream separation' or 'Stream segregation" are used here, meaning more or less the same as polyphonic voice separation or voice following.

The core algorithm is based on Machine Learning clustering techniques, using 'Single Link Agglomerative Clustering' grouping individual notes into streams of voices. This approach is well adapted to follow arbitrary musical voices, which may wander across registers and also cross each other.

Currently the algorithm outputs monophonic voices. Any overlapping notes are clustered into separate streams, where each note belongs to one and only one output stream. A dynamic variable overlap-tolerance holds a factor (0-1.0) to control how forgiving the algorithm is with respect to what counts as simultaneous.

The feature vectors compared for similarity are built from pitch and time info in input data, using a function to measure similarity. The default distance metrics - #'euclidian-distance - compares pitches using a mel frequency scale, to do measurements based on perceived similarity. The similarity function can be tuned with adjustable weightings for pitch and time to fit various types of input.

Typical input in OpenMusic is a 'chord-seq'. Basic output is a list of chord-seqs, one for each voice found.

Currently, CPU usage grows exponentially with larger input data sets T(n)= ϴ(n³) (an optimization for HAC based on a Next-Best-Match array will be implemented when time permits). If things start to get too slow select subsets of data to work on, or preferably use the provided analysis-class: 'stream-seg' to interactively segment data before applying the stream-separation analysis per segment.

'stream-seg' - a sub-class of OMs 'analysis' class - is provided to work interactively with stream-segregation within segmentation bounds of segmented data. Functions are provided to return the output from this analysis as a list of lists of chord-seqs, the whole sequence of analysed segments concatenated into one multi-seq, or data to feed a Maquette.

Help is available directly from the graphical boxes (hit 'd'), or through the provided tutorials.

Anders Vinjar - April 2017