Features¶

This module provides methods that allows to analyze symbolic music.

Pitch¶

`get_highest_lowest_pitches`(notes)	Extracts the highest and lowest pitches from a list of notes.
`get_pitch_range`(notes)	Computes the difference between the highest and the lowest pitches in a list of notes.
`get_note_density`(notes)	Counts the total number of onsets in the `Instrument`.
`get_pitch_classes`(notes)	Counts the total number of different note classes `NoteClassBase()`.
`get_note_classes`(notes)	Counts the total number of different note classes `NoteClassBase()`.
`pitch_counts`(notes)	Calculates the number of different pitches or Pitch Counts (PC) of a list of Notes.
`get_last_note_class`(notes)	Returns the latest note name (NoteClassBase) in the sequence.
`pitch_class_histogram`(notes)	Computes the Pitch Class Histogram (PCH) of a list of musicaiz `Note()` objects.
`pitch_class_transition_matrix`(notes)	Computes the Pitch Class Transition Matrix (PCTM) of a list of musicaiz `Note()` objects.
`plot_pitch_class_transition_matrix`(pctm)	Plots the Pitch Class Transition Matrix (PCTM).
`average_pitch_interval`(notes)	Computes the Average Pitch Interval (PI) of a a list of musicaiz `Note()` objects.

Harmony¶

`get_chord_type_from_note_seq`(note_seq)	Gets the chord type `ChordType()` of a list of musicaiz `Note()` objects.
`get_intervals_note_seq`(note_seq)	Get the intervals between pairs of notes (1st note and the rest of the notes in the note seq) of a sorted note seq.
`predict_chords`(note_seq)	Predicts a chord in a note sequence with only note values, so no note durations atr taken into account.
`predict_scales_degrees`(note_seq)	This method is similar to Scales.get_scales_degrees_from_chord method but in this case applied to an input note_seq, not to a chord.
`predict_possible_progressions`(possible_chords)	Get all possible scales and degrees from a chords list (chord progression) We retrieve a list of degrees which items correspond to one time step each.
`predict_progression`(scales, scale)	Uses predict_possible_progressions to predict all the scales and progressions that belong to a note_seq but this method only returns one of them.
`_all_note_seq_permutations`(note_seq)	Returns a list of lists of all possible orders for a note seq.
`_delete_repeated_note_names`(note_seq)	Removes a repeated note in a note_seq.
`_extract_note_positions`(note_seq)	Extracts the note positions in the chromatic scale of the notes in a notes sequence.
`_order_note_seq_by_chromatic_idx`(note_seq)	Sorts a note seq (list of note objects) by the index of the notes in the chromatic scale.
`get_harmonic_density`(note_seq)	Computes the maximum number of notes that are overlapped in the harmonic axis.

Rhythm¶

This submodule contains the implementation of part of the paper:

[1] Roig, C., Tardón, L. J., Barbancho, I., & Barbancho, A. M. (2014). Automatic melody composition based on a probabilistic model of music style and harmonic rules. Knowledge-Based Systems, 71, 419-434. http://dx.doi.org/10.1016/j.knosys.2014.08.018

The implementation follows the paper method to predict rhythmic patterns. This module contains:

Tempo (or bpm) estimation

get IOIs with get_ioi method.

get error ej

Time signature estimation

get the labeled beat vector (or IOI’) from IOIs with get_labeled_beat_vector

get the Bar Split Vectors (BSV) for each beat (k in the paper) with get_split_bar_vector.

k goes from 2 to 12 which are the most-common time_sig numerators. - compute the RSSM with each BSV with compute_rhythm_self_similarity_matrix. - get the time_sig numerator which will be the RSSM with the highest repeated bar instances.

Rhythm extraction

Pitch contour extraction

`get_start_sec`(notes)	Extracts the time start of the notes in a notes sequence.
`get_ioi`(all_note_on[, delete_overlap])	get ioi of a list of time start values
`get_labeled_beat_vector`(iois)	Convert IOI to the labeled beat vector (or IOI')
`compute_rhythm_self_similarity_matrix`(...)	This function computes the Rhythm Self-Similarity Matrix (RMSS).
`plot_rmss`(rmss[, k, save, filename])
`compute_all_rmss`(labeled_beat_vector)	This function computes all the RMSS for time_sig numerators (k) 2 to 12 and outputs the beat (k) which will be the predicted time_sig numerator.
`get_symbolic_length_classes`(notes)	Counts the total number of different note (NoteClassBase) classes.
`note_length_histogram`(notes)	Uses get_note_classes to build a 1D list vector of 12 dimensions in which each element represents the counts of each note name in the chromatic scale of 12 notes.
`note_length_transition_matrix`(notes)	Computes the Note Length Transition MAtrix (NLTM) of a list of Note objects.
`plot_note_length_transition_matrix`(nltm)	Take into account that if you generated a NLTM with triplets = False then the triplet argument in this function will also be False.

Self-Similarity Matrices¶

This submodule presents different implementations of self-similarity matrices.

The papers that are implemented in this sumbodule are the following:

[1] Louie, W. MusicPlot: Interactive Self-Similarity Matrix for Music Structure Visualization. https://wlouie1.github.io/MusicPlot/musicplot_paper.pdf

The process to obtain the SSM with this method is: 1. Group the notes in bars and subdivisions. 2. Extract the highest note in each subdivision. 3. Calculate m_prime = [p1-p2, d2/d1, …] with p the pitch and d the note duration. 4. Compute the SSM function.

`compute_ssm`(file[, ssm_type, measures, ...])	Computes the selected SSM.
`self_similarity_louie`(file)	Computes the SSM with Louie method: https://wlouie1.github.io/MusicPlot/musicplot_paper.pdf
`self_similarity_single_measure`(file, measure)	Computes the SSM with the selected measure for all the bars.
`self_similarity_measures`(file, measures)	Computes the SSM with the selected measure for all the bars.
`plot_ssm`(ssm[, segments, threshold, window, ...])	Plots a SSM.
`_self_similarity`(feature_vector)	Converts a feature vector with the highest notes per subdivision and bar in a Self-Similarity Matrix with the cosine distance.
`binarize_self_similarity_matrix`(ssm, threshold)
`feature_vector`(bar_highest_subdiv_notes)	Computes the m_prim vector which calculates the difference between 2 consecutive note's pitches and the division of the note's durations.
`get_novelty_func`(ssm[, is_normalized])	Computes the novelty function of a SSM.
`get_segment_boundaries`(ssm[, threshold, window])	Gets the segment boundaries of a SSM.
`plot_novelty_from_ssm`(ssm[, segments, ...])	Plots the novelty curve from a SSM.

Graphs¶

This submodule presents different implementations of self-similarity matrices.

The papers that are implemented in this sumbodule are the following:

[1] Jeong, D., Kwon, T., Kim, Y., & Nam, J. (2019) Graph neural network for music score data and modeling expressive piano performance. In International Conference on Machine Learning, 3060-3070 https://proceedings.mlr.press/v97/jeong19a.html

`musa_to_graph`(musa_object)	Converts a Musa object into a Graph where nodes are the notes and edges are connections between notes.
`plot_graph`(graph[, show])	Plots a graph with matplotlib.

Structure¶

This submodule segments symbolic music in its form or structure.

`get_structure_boundaries_notes`(musa_obj, level)	Get the note indexes where a section ends.
`get_structure_boundaries_beats`(musa_obj, level)	Get the beat indexes where a section ends.