README.md

PitchSpellingModel

Code Coverage

Contents

Encodings

Encodings implements mappings between models of music, music notation and my Pitch Spelling Model. Models of music and music notation drawn from the dn-m ecosystem (cf. Music, NotationModel), .

NetworkStructures

NetworkStructures implements all the network operations and algorithms needed for the performance of Pitch Spelling Algorithms in forward and inverse directions.

SpellingNetworks

SpellingNetworks specializes the Encodings and NetworkStructures modules to the use cases of the Pitch Spelling model.

Workflow Sketch

First, make an InvertingSpellingNetwork (inputs can be spellings: [Pitch], spellings: [Int: Pitch] or spellings:[[Pitch]], where the last definition splits spellings easily into groups.

let invertingSpellingNetwork = InvertingSpellingNetwork(spellings: [
            1: Pitch.Spelling(.f,.sharp),
            2: Pitch.Spelling(.a,.sharp),
            
            ...
            
            ])

Second, generate a PitchSpellingNetworkFactory. sets parameter forces arrays of PitchedEdge values to be equal in the inverting process (stabilization). A preset parameter can also be used to fix certain PitchedEdge weights in the process.

let factory = invertingSpellingNetwork.pitchSpellingNetworkFactory(
sets: [
            [
                PitchedEdge(.source, .internal(.init(1, .down))),
                PitchedEdge(.source, .internal(.init(6, .down))),
                PitchedEdge(.internal(.init(3, .up)), .sink),
                PitchedEdge(.internal(.init(10, .up)), .sink),
            ],

...
        ]
        )

Third, generate a PitchSpellingNetwork from the factory. withPhantomPitches feeds in pitches that cannot be adjusted by the user, but will affect the spelling. The phantom pitches will always take their default spelling - hence [0,4] is acting as [Pitch.Spelling(.c), Pitch.Spelling(.e)]

let pitchSpellingNetwork = factory.build(from: [6,10,1], withPhantomPitches: [0,4])

Fourth, adjust PitchSpellingNetwork weights using mask schemes. Using the following mask function.

    // Adjusts edge weights based on an external scaling rule
    func mask <T> (scheme: FlowNetworkScheme<T>, _ lens: @escaping (Int) -> T)

scheme defines some weight scale factors based on Edge<T> values. These are pulled back to the PitchSpellingNetwork's weight scheme via the lens: @escaping (Int) -> T parameter, which gives us a map from the pitch index Int to the index of the newly supplied FlowNetworkScheme.

Fifth, spell! preferring is an optional parameter, .sharps by default. It determines whether the search to determine the minimum cut found by the Edmonds-Karp maximum flow algorithm is taken from the .source or the .sink.

let spellings: [Int: SpelledPitch] = pitchSpellingNetwork.spell(preferring: .flats)