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Original music21 feature extractors.


class music21.features.native.ChordBassMotionFeature(dataOrStream=None, *arguments, **keywords)

A twelve element feature that reports the fraction of all chord motion of music21.harmony.Harmony objects that move up by i-half-steps. (a half-step motion down would be stored in i = 11). i = 0 is always 0.0 since consecutive chords on the same pitch are ignored (unless there are 0 or 1 harmonies, in which case it is 1)

Sample test on Dylan’s Blowing In The Wind (not included), showing all motion is 3rds, 6ths, or especially 4ths and 5ths.

s = corpus.parse(‘demos/BlowinInTheWind’) fe = features.native.ChordBassMotionFeature(s) fe.extract().vector

[0.0, 0.0, 0.0, 0.0416..., 0.0416..., 0.166..., 0.0, 0.54166..., 0.0, 0.0, 0.2083... 0.0]

For comparison, the Beatles Here Comes the Sun has more tone motion

[0.0, 0.05..., 0.14..., 0.03..., 0.06..., 0.3..., 0.008..., 0.303..., 0.0, 0.0, 0.07..., 0.008...]

Post 1990s music has a lot more semitone motion.

ChordBassMotionFeature bases

ChordBassMotionFeature methods

Methods inherited from FeatureExtractor:


class music21.features.native.ComposerPopularity(dataOrStream=None, *arguments, **keywords)

composer’s popularity today, as measured by the number of Google search results (log-10)

>>> s = corpus.parse('mozart/k155', 2)
>>> fe = features.native.ComposerPopularity(s)
>>> fe.extract().vector[0] > 6.0 

ComposerPopularity bases

ComposerPopularity methods

Methods inherited from FeatureExtractor:


class music21.features.native.DiminishedSeventhSimultaneityPrevalence(dataOrStream=None, *arguments, **keywords)

Percentage of all simultaneities that are diminished seventh chords.

>>> s = corpus.parse('bwv66.6') 
>>> fe = features.native.DiminishedSeventhSimultaneityPrevalence(s)
>>> fe.extract().vector 

DiminishedSeventhSimultaneityPrevalence bases

DiminishedSeventhSimultaneityPrevalence methods

Methods inherited from FeatureExtractor:


class music21.features.native.DiminishedTriadSimultaneityPrevalence(dataOrStream=None, *arguments, **keywords)

Percentage of all simultaneities that are diminished triads.

>>> s = corpus.parse('bwv66.6')
>>> fe = features.native.DiminishedTriadSimultaneityPrevalence(s)
>>> fe.extract().vector 

DiminishedTriadSimultaneityPrevalence bases

DiminishedTriadSimultaneityPrevalence methods

Methods inherited from FeatureExtractor:


class music21.features.native.DominantSeventhSimultaneityPrevalence(dataOrStream=None, *arguments, **keywords)

Percentage of all simultaneities that are dominant seventh.

>>> s = corpus.parse('bwv66.6') 
>>> fe = features.native.DominantSeventhSimultaneityPrevalence(s)
>>> fe.extract().vector 

DominantSeventhSimultaneityPrevalence bases

DominantSeventhSimultaneityPrevalence methods

Methods inherited from FeatureExtractor:


class music21.features.native.FirstBeatAttackPrevalence(dataOrStream=None, *arguments, **keywords)
>>> s = corpus.parse('bwv66.6') 
>>> fe = features.native.FirstBeatAttackPrevalence(s)
>>> f = fe.extract()
>>> f.vector

TODO: That seems to be a bug!!!

FirstBeatAttackPrevalence bases

FirstBeatAttackPrevalence methods

Methods inherited from FeatureExtractor:


class music21.features.native.IncorrectlySpelledTriadPrevalence(dataOrStream=None, *arguments, **keywords)

Percentage of all triads that are spelled incorrectly.


Mozart k155 movement 2 has a single instance of an incorrectly spelled triad (m. 17, where the C# of an A-major chord has a lower neighbor B# thus temporarily creating an incorrectly spelled A-minor chord).

We would expect highly chromatic music such as Reger or Wagner to have a higher percentage, or automatically rendered MIDI transcriptions (which don’t distinguish between D# and Eb).

>>> s = corpus.parse('bwv66.6')
>>> fe = features.native.IncorrectlySpelledTriadPrevalence(s)
>>> fe.extract().vector 

IncorrectlySpelledTriadPrevalence bases

IncorrectlySpelledTriadPrevalence methods

Methods inherited from FeatureExtractor:


class music21.features.native.LandiniCadence(dataOrStream=None, *arguments, **keywords)

Return a bolean if one or more Parts end with a Landini-like cadential figure.

LandiniCadence bases

LandiniCadence methods

Methods inherited from FeatureExtractor:


class music21.features.native.LanguageFeature(dataOrStream=None, *arguments, **keywords)

language of text as a number the number is the index of text.LanguageDetector.languageCodes + 1 or 0 if there is no language.

Detect that the language of a Handel aria is Italian.

>>> s = corpus.parse('handel/rinaldo/lascia_chio_pianga') 
>>> fe = features.native.LanguageFeature(s)
>>> fe.extract().vector

LanguageFeature bases

LanguageFeature methods

Methods inherited from FeatureExtractor:


class music21.features.native.MajorTriadSimultaneityPrevalence(dataOrStream=None, *arguments, **keywords)

Percentage of all simultaneities that are major triads.

>>> s = corpus.parse('bwv66.6') 
>>> fe = features.native.MajorTriadSimultaneityPrevalence(s)
>>> fe.extract().vector

MajorTriadSimultaneityPrevalence bases

MajorTriadSimultaneityPrevalence methods

Methods inherited from FeatureExtractor:


class music21.features.native.MinorTriadSimultaneityPrevalence(dataOrStream=None, *arguments, **keywords)

Percentage of all simultaneities that are minor triads.

>>> s = corpus.parse('bwv66.6') 
>>> fe = features.native.MinorTriadSimultaneityPrevalence(s)
>>> fe.extract().vector # same as major in this work

MinorTriadSimultaneityPrevalence bases

MinorTriadSimultaneityPrevalence methods

Methods inherited from FeatureExtractor:


class music21.features.native.MostCommonNoteQuarterLength(dataOrStream=None, *arguments, **keywords)
>>> s = corpus.parse('bwv66.6') 
>>> fe = features.native.MostCommonNoteQuarterLength(s)
>>> fe.extract().vector 

MostCommonNoteQuarterLength bases

MostCommonNoteQuarterLength methods

Methods inherited from FeatureExtractor:


class music21.features.native.MostCommonNoteQuarterLengthPrevalence(dataOrStream=None, *arguments, **keywords)
>>> s = corpus.parse('bwv66.6') 
>>> fe = features.native.MostCommonNoteQuarterLengthPrevalence(s)
>>> fe.extract().vector 

MostCommonNoteQuarterLengthPrevalence bases

MostCommonNoteQuarterLengthPrevalence methods

Methods inherited from FeatureExtractor:


class music21.features.native.MostCommonPitchClassSetSimultaneityPrevalence(dataOrStream=None, *arguments, **keywords)

Fraction of all pitch class simultaneities that are the most common simultaneity.

>>> s = corpus.parse('bwv66.6') 
>>> fe = features.native.MostCommonPitchClassSetSimultaneityPrevalence(s)
>>> fe.extract().vector

MostCommonPitchClassSetSimultaneityPrevalence bases

MostCommonPitchClassSetSimultaneityPrevalence methods

Methods inherited from FeatureExtractor:


class music21.features.native.MostCommonSetClassSimultaneityPrevalence(dataOrStream=None, *arguments, **keywords)

Fraction of all set class simultaneities that the most common simultaneity occupies.

>>> s = corpus.parse('bwv66.6') 
>>> fe = features.native.MostCommonSetClassSimultaneityPrevalence(s)
>>> fe.extract().vector
>>> s2 = corpus.parse('schoenberg/opus19', 6)
>>> fe2 = features.native.MostCommonSetClassSimultaneityPrevalence(s2)
>>> fe2.extract().vector

MostCommonSetClassSimultaneityPrevalence bases

MostCommonSetClassSimultaneityPrevalence methods

Methods inherited from FeatureExtractor:


class music21.features.native.QualityFeature(dataOrStream=None, *arguments, **keywords)

Extends the jSymbolic QualityFeature to automatically find mode

Set to 0 if the key signature indicates that a recording is major, set to 1 if it indicates that it is minor. A Music21 addition: if no key mode is found in the piece, analyze the piece to discover what mode it is most likely in.

Example: Handel, Rinaldo Aria (musicxml) is explicitly encoded as being in Major:

>>> s = corpus.parse('handel/rinaldo/lascia_chio_pianga') 
>>> fe = features.native.QualityFeature(s)
>>> f = fe.extract()
>>> f.vector

now we will try it with the last movement of Schoenberg’s opus 19 which has no mode explicitly encoded in the musicxml but which our analysis routines believe (having very little to go on) fits the profile of e-minor best.

>>> schoenberg19mvmt6= corpus.parse('schoenberg/opus19', 6)
>>> fe2 = features.native.QualityFeature(schoenberg19mvmt6)
>>> f2 = fe2.extract()
>>> f2.vector

QualityFeature bases

QualityFeature methods

Methods inherited from FeatureExtractor:


class music21.features.native.RangeOfNoteQuarterLengths(dataOrStream=None, *arguments, **keywords)

Difference between the longest and shortest quarter lengths.

>>> s = corpus.parse('bwv66.6') 
>>> fe = features.native.RangeOfNoteQuarterLengths(s)
>>> fe.extract().vector 

RangeOfNoteQuarterLengths bases

RangeOfNoteQuarterLengths methods

Methods inherited from FeatureExtractor:


class music21.features.native.TonalCertainty(dataOrStream=None, *arguments, **keywords)
>>> s = corpus.parse('bwv66.6') 
>>> fe = features.native.TonalCertainty(s)
>>> f = fe.extract()
>>> f.vector

TonalCertainty bases

TonalCertainty methods

Methods inherited from FeatureExtractor:


class music21.features.native.TriadSimultaneityPrevalence(dataOrStream=None, *arguments, **keywords)

Gives the proportion of all simultaneities which form triads (major, minor, diminished, or augmented)

>>> s = corpus.parse('bwv66.6')
>>> fe = features.native.TriadSimultaneityPrevalence(s)
>>> fe.extract().vector 
>>> s2 = corpus.parse('schoenberg/opus19', 2)
>>> fe2 = features.native.TriadSimultaneityPrevalence(s2)
>>> fe2.extract().vector

TriadSimultaneityPrevalence bases

TriadSimultaneityPrevalence methods

Methods inherited from FeatureExtractor:


class music21.features.native.URLOpenerUI(*args, **kwargs)


class music21.features.native.UniqueNoteQuarterLengths(dataOrStream=None, *arguments, **keywords)
>>> s = corpus.parse('bwv66.6') 
>>> fe = features.native.UniqueNoteQuarterLengths(s)
>>> fe.extract().vector 

UniqueNoteQuarterLengths bases

UniqueNoteQuarterLengths methods

Methods inherited from FeatureExtractor:


class music21.features.native.UniquePitchClassSetSimultaneities(dataOrStream=None, *arguments, **keywords)

Number of unique pitch class simultaneities.

>>> s = corpus.parse('bwv66.6') 
>>> fe = features.native.UniquePitchClassSetSimultaneities(s)
>>> fe.extract().vector

UniquePitchClassSetSimultaneities bases

UniquePitchClassSetSimultaneities methods

Methods inherited from FeatureExtractor:


class music21.features.native.UniqueSetClassSimultaneities(dataOrStream=None, *arguments, **keywords)

Number of unique set class simultaneities.

>>> s = corpus.parse('bwv66.6') 
>>> fe = features.native.UniqueSetClassSimultaneities(s)
>>> fe.extract().vector

UniqueSetClassSimultaneities bases

UniqueSetClassSimultaneities methods

Methods inherited from FeatureExtractor: