itembankr is an R package for creating a useful item bank, to be used in psychological testing, from raw data. It is currently focused on musical and particularly monophonic, melodic stimuli. For convenience, we refer to a "corpus" as some raw data, not in itself particularly useful in psychological testing, as the input, and an item bank as the useful output, which is typically a set of dataframes with the stimuli from the corpus organised in a useful way plus some new calculated representations.

Hence, the main function in itembankr is create_item_bank:

Berkowitz <- create_item_bank(corpus_name = 'Berkowitz',
                    midi_file_dir = 'berkowitz_midi_rhythmic_100bpm',
                    musicxml_file_dir = 'berkowitz_musicxml',
                    prefix = 'inst',
                    output_type = 'both')

The output of create_item_bank is a function which allows you to access whichever of the four possible item bank types you requested are:

# a fairly limited item bank consisting of just file references
Berkowitz("files")
# a database of the melodic items split up into ngrams (perhaps most useful for arrhythmic usage)
Berkowitz("ngram")
# a database of the melodic items split up into ngrams PLUS computed melodic features
Berkowitz("main")
# rather than splitting up the corpus into ngrams, split it up based on (a rather crude approximation of) phrase boundaries. this is perhaps more useful for rhythmic usage.
Berkowitz("phrases")

These various item banks can be used as inputs to tests created with the musicassessr package.

Please note, these functions can take a long time to run.

By default, create_item_bank produces both ngram and phrase databases as output. You can request only some of the database types to be created using the output_type argument:

create_item_bank(output_type = "ngram") # only ngram
create_item_bank(output_type = "phrases") # only phrases

Or

files_db <- create_item_bank_from_files(corpus_name = corpus_name,
                                        midi_file_dir = midi_file_dir,
                                        musicxml_file_dir = musicxml_file_dir,
                                        prefix = prefix)
                                        
ngram_db <- split_item_bank_into_ngrams(Berkowitz_files_db) # input should be a files db
freq_db <- count_freqs(Berkowitz_ngram_db) 
main_db <- get_melody_features(Berkowitz_freq_db, mel_sep = ",", durationMeasures = TRUE)
phrases_db <- create_phrases_db(corpus_name = corpus_name, midi_file_dir = add_prefix(paste0('item_banks/', corpus_name, '/', midi_file_dir), prefix), prefix = prefix)

Beaty, R. E., Frieler, K., Norgaard, M., Merseal, H. M., MacDonald, M. C., & Weiss, D. J. (2021). Expert musical improvisations contain sequencing biases seen in language production. Journal of Experimental Psychology. https://doi.org/10.1037/xge0001107

Berkowitz, S., Fontrier, G., Goldstein, P., & Smaldone, E. (2017). A new approach to sight singing (Sixth edition). W. W. Norton & Company

Crayencour, H.-C., Velichkina, O., Frieler, K., Höger, F., Pfleiderer, M., Henry, L., Solis, G., Wolff, D., Weyde, T., Peeters, G., Basaran, D., Smith, J., & Proutskova, P. (2021)

The DTL1000 Jazz Solo Dataset (in prep.). Journal on Computing and Cultural Heritage

Müllensiefen, D. (2009). FANTASTIC: Feature ANalysis Technology Accessing STatistics (In a Corpus; Technical report). 37.

Pfleiderer, M., Frieler, K., Abeßer, J., Zaddach, W.-G., & Burkhart, B. (Hrsg.). (2017). Inside the Jazzomat—New perspectives for jazz research. Schott Campus.

Slonimsky, N. (1947). Thesaurus of scales and melodic patterns. Literary Licensing, LLC.