Trait

de.sciss.strugatzki.FeatureSegmentation

ConfigLike

Related Doc: package FeatureSegmentation

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sealed trait ConfigLike extends AnyRef

All durations, spans and spacings are given in sample frames with respect to the sample rate of the audio input file.

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  1. abstract def corrLen: Long

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    The size of the sliding window over which the features are correlated.

    The size of the sliding window over which the features are correlated. That is, for a length of 1.0 second (given in sample frames, hence 44100 for a sample rate of 44100 Hz), at any given point in time, 0.5 seconds left of that point are correlated with 0.5 seconds right of that point. Breaking points are those where correlation is minimised.

  2. abstract def databaseFolder: File

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    The database folder is merely used to retrieve the normalization file, given that normalize is true.

  3. abstract def metaInput: File

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    The XML file holding the extractor parameters corresponding to the audio input file.

    The XML file holding the extractor parameters corresponding to the audio input file. The audio input file's feature vector output file is determined from this meta file.

  4. abstract def minSpacing: Long

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    Minimum spacing between breaks

  5. abstract def normalize: Boolean

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    Whether to apply normalization to the features (recommended)

  6. abstract def numBreaks: Int

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    Maximum number of breaks to report

  7. abstract def span: NonVoid

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    An option which restricts segmentation to a given span within the input file.

    An option which restricts segmentation to a given span within the input file. That is, only breaking points within this span are reported. If None, the whole file is considered.

  8. abstract def temporalWeight: Float

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    The balance between the feature of loudness curve and spectral composition (MFCC).

    The balance between the feature of loudness curve and spectral composition (MFCC). A value of 0.0 means the segmentation is only performed by considering the spectral features, and a value of 1.0 means the segmentation is taking only the loudness into consideration. Values in between give a measure that takes both features into account with the given priorities.

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