Running IDyOM¶

• Running IDyOM
  • idyom:idyom
    • Required parameters
    • Statistical modelling parameters
    • Training parameters
    • Viewpoint selection parameters
    • Output parameters
  • Examples
    • Mean melody IC
    • Write note IC to file
    • Conklin & Witten (1995)
  • Viewpoint Selection

idyom:idyom¶

The main workhorse function is idyom:idyom, which has three required arguments and a number of optional keyword arguments.

Required parameters¶

• dataset-id: a dataset id, e.g. 1.
• target-viewpoints: a list of basic viewpoints to predict, e.g. '(:cpitch :bioi)
• source-viewpoints: a list of viewpoints to use in prediction, e.g. '((:cpintfref :cpint) :bioi)
  • Passing :select will trigger viewpoint selection (see further options below)

See the List of viewpoints for a description of the various viewpoints available in IDyOM. A simple call to IDyOM would be:

CL-USER> (idyom:idyom 0 '(cpitch) '(cpitch))
2.2490792
(1.9049941 2.427845 2.0234334 1.7971386 1.8213106 1.9313766 2.3758402 1.8310248
...

This predicts the pitch values in dataset 1, based on previous pitches (cpitch) and pitch intervals (cpint). IDyOM computes the information content for each note, and by default returns two values: the first is a mean note IC for the dataset, the second a list of mean note ICs for the individual compositions. The first value is calculated as the mean of the second.

Statistical modelling parameters¶

See Pearce [2005, chapter 6] for further description and explanation of these parameters.

• models: the type of IDyOM model to use. Options are:
  • :stm - short-term model only, trained on the current composition.
  • :ltm - long-term model only, trained on the pretraining and resampling training data.
  • :ltm+ - the long-term model, with additional incremental training on the test set;
  • :both - a combination of :stm and :ltm;
  • :both+ - a combination of :stm and :ltm+ (this is the default).

The LTM and STM can be configured using the ltmo and stmo parameters. These accept a property list with the following properties - the defaults are used if a property is omitted or no parameter list is supplied:

• :order-bound: an integer indicating the bound on the order of the model, i.e. the number of past events used by the model. The default is nil, no bound.
• :mixtures: whether to use mixtures for the model. (Default t).
• :update-exclusion: whether to use update exclusion. (LTM default nil, STM default t.)
• :escape: the model's escape method. One of :a :b :c :d :x. (LTM default :c, STM default :x.)

For example, the following command would combine the STM and LTM, without incremental training for the latter and an STM order bound of 4:

CL-USER> (idyom:idyom 1 '(cpitch) '(cpitch) :models :both :stmo '(:order-bound 4))

Training parameters¶

When using IDyOM to estimate note IC for a given dataset, the long-term models can be trained on other datasets (pretraining) and/or on the current dataset, i.e. via resampling (cross-validation). In the latter case, the dataset is partitioned into a training set (used to train the LTMs) and a test set (for which note IC is computed). This split is called a fold, and the modelling process can be repeated with a number of different folds in order to model the entire dataset.

• pretraining-ids: a list of dataset ids used to pretrain the long-term models (done before resampling).
• k: the number of resampling (cross-validation) folds to use. The default value is 10.
  • 1 = no resampling, but also no training set unless the models are pretrained;
  • :full = as many folds as there are compositions in the dataset
• resampling-indices: a list of numbers designating which resampling folds to use, i.e. a subset of [0, 1, ..., k - 1]. By default, all folds are used.

Note that cross-validation only applies to the dataset being analysed (i.e., the one specified by the dataset-id argument). If a value of k=1 is supplied, the long-term models are not trained, unless a pretraining set is used.

Viewpoint selection parameters¶

• basis: Identifies a set of viewpoints to be used in viewpoint selection, i.e. it will attempt to find the 'best' viewpoint system combining these, including by linking them. The parameter can be a list or one of the following keywords:
  • :pitch-viewsA - The basis is a list of viewpoints useful for predicting pitch in Western music: cpitch, cpitch-class, tessitura, cpint, cpint-size, cpcint, cpcint-size, contour, newcontour, cpintfip, cpintfref, inscale.
  • :pitch-viewsB - A shorter version of the above: cpitch, cpitch-class, cpint, cpint-size, contour, newcontour.
  • :ioi-views - For predicting Inter-Onset Interval (IOI): bioi, bioi-ratio, bioi-contour.
  • :auto - the basis is chosen to be the set of viewpoints that are defined in terms of one or more of the target viewpoints. This is the default.
• dp: the number of decimal places to use when comparing information contents in viewpoint selection. Full floating point precision is used if this is nil (the default)
• max-links: the maximum number of links to use when creating linked viewpoints in viewpoint selection. The default is 2.

Output parameters¶

• output-path: a string indicating a directory in which to write the output
  • output is only written to the console if this is nil
• detail: an integer which determines how the information content is averaged in the output:
  • 1: averaged over the entire dataset
  • 2: and also averaged over each composition
  • 3: and also with raw IC values for each event in each composition

Examples¶

Mean melody IC¶

To get mean information contents for each melody of dataset 0 in a list

CL-USER> (idyom:idyom 0 '(cpitch) '(cpintfref cpint) :detail 2)
2.493305
(2.1368716 2.8534691 2.6938546 2.6491673 2.4993074 2.6098127 2.7728052 2.772861
 2.5921957 2.905856 2.3591626 2.957503 2.4042292 2.7562473 2.3996017 2.8073587
 2.114944 1.7434102 2.2310295 2.6374347 2.361792 1.9476132 2.501488 2.5472867
 2.1056154 2.8225484 2.134257 2.9162033 3.0715692 2.9012227 2.7291088 2.866882
 2.8795822 2.4571223 2.9277062 2.7861307 2.6623116 2.3304622 2.4217033
 2.0556943 2.4048684 2.914848 2.7182267 3.0894585 2.873869 1.8821808 2.640174
 2.8165438 2.5423129 2.3011856 3.1477294 2.655349 2.5216308 2.0667994 3.2579045
 2.573013 2.6035044 2.202191 2.622113 2.2621205 2.3617425 2.7526956 2.3281655
 2.9357266 2.3372407 3.1848125 2.67367 2.1906006 2.7835917 2.6332111 3.206142
 2.1426969 2.194259 2.415167 1.9769101 2.0870917 2.7844474 2.2373738 2.772138
 2.9702199 1.724408 2.473073 2.2464263 2.2452457 2.688889 2.6299863 2.2223835
 2.8082614 2.673671 2.7693706 2.3369458 2.5016947 2.3837066 2.3682225 2.795649
 2.9063463 2.5880773 2.0457468 1.8635312 2.4522712 1.5877498 2.8802161
 2.7988417 2.3125513 1.7245895 2.2404804 2.1694546 2.365556 1.5905867 1.3827317
 2.2706041 3.023884 2.2864542 2.1259797 2.713626 2.1967313 2.5721254 2.5812547
 2.8233812 2.3134546 2.6203637 2.945946 2.601433 2.1920888 2.3732007 2.440137
 2.4291563 2.3676903 2.734724 3.0283954 2.8076048 2.7796154 2.326931 2.1779459
 2.2570527 2.2688026 1.3976555 2.030298 2.640235 2.568248 2.6338177 2.157162
 2.3915367 2.7873137 2.3088667 2.2176988 2.4402564 2.8062992 2.784044 2.4296925
 2.3520193 2.6146257)

Write note IC to file¶

To write the information contents for each note of each melody in dataset 0 to a file

CL-USER> (idyom:idyom 0 '(cpitch) '(cpintfref cpint)) :detail 3 :output-path "/tmp/")

Conklin & Witten (1995)¶

To simulate the experiments of Conklin & Witten (1995)

CL-USER> (resampling:conkwit95)
Simulation of the experiments of Conklin & Witten (1995, Table 4).
System 1; Mean Information Content: 2.33 
System 2; Mean Information Content: 2.36 
System 3; Mean Information Content: 2.09 
System 4; Mean Information Content: 2.01 
System 5; Mean Information Content: 2.08 
System 6; Mean Information Content: 1.90 
System 7; Mean Information Content: 1.88 
System 8; Mean Information Content: 1.86 
NIL

Compare with Conklin & Witten [1995, JNMR, table 4]

Viewpoint Selection¶

The top-level idyom:idyom function supports viewpoint selection (see above), i.e. searching a space of viewpoints. This uses two functions: run-hill-climber and run-best-first, which take 4 arguments:

• a list of viewpoints: the algorithm searches through the space of combinations of these viewpoints
• a start state (usually nil, the empty viewpoint system)
• an evaluation function returning a numeric performance metric: e.g., the mean information content of the dataset returned by dataset-prediction
• a symbol describing which way to optimise the metric: :desc mean lower values are better :asc mean greater values are better

Here is an example:

CL-USER> (viewpoint-selection:run-hill-climber 
          '(:cpitch :cpintfref :cpint :contour)
          nil
          #'(lambda (viewpoints)
              (utils:round-to-nearest-decimal-place 
               (resampling:output-information-content 
                (resampling:dataset-prediction 0 '(cpitch) viewpoints :k 10 :models :both+) 
                1)
               2))
          :desc)

 =============================================================================
   System                                                Score
 -----------------------------------------------------------------------------
   NIL                                                   NIL
   (CPITCH)                                              2.52
   (CPINT CPITCH)                                        2.43
   (CPINTFREF CPINT CPITCH)                              2.38
 =============================================================================
#S(VIEWPOINT-SELECTION::RECORD :STATE (:CPINTFREF :CPINT :CPITCH) :WEIGHT 2.38)

Since this can be quite a time consuming process, there are also functions for caching the results.

(initialise-vs-cache)
(load-vs-cache filename package)
(store-vs-cache filename package)