music21.audioSearch

Base routines used throughout audioSearching and score-following.

Requires numpy and matplotlib. Installing scipy makes the process faster and more accurate using FFT convolve.

Functions

music21.audioSearch.autocorrelationFunction(recordedSignal, recordSampleRateIn)

Converts the temporal domain into a frequency domain. In order to do that, it uses the autocorrelation function, which finds periodicities in the signal in the temporal domain and, consequently, obtains the frequency in each instant of time.

>>> import wave
>>> import numpy  # you need to have numpy, scipy, and matplotlib installed to use this

>>> wv = wave.open(str(common.getSourceFilePath() /
...                     'audioSearch' / 'test_audio.wav'), 'r')
>>> data = wv.readframes(1024)
>>> samples = numpy.frombuffer(data, dtype=numpy.int16)
>>> finalResult = audioSearch.autocorrelationFunction(samples, 44100)
>>> wv.close()
>>> print(finalResult)
143.6276...
music21.audioSearch.decisionProcess(partsList, notePrediction, beginningData, lastNotePosition, countdown, firstNotePage=None, lastNotePage=None)

Decides which of the given parts of the score has the best match with the recorded part of the song. If there is not a part of the score with a high probability to be the correct part, it starts a “countdown” in order stop the score following if the bad matching persists. In this case, it does not match the recorded part of the song with any part of the score.

Inputs: partsList, contains all the possible parts of the score, sorted from the higher probability to be the best matching at the beginning to the lowest probability. notePrediction is the position of the score in which the next note should start. beginningData is a list with all the beginnings of the used fragments of the score to find the best matching. lastNotePosition is the position of the score in which the last matched fragment of the score finishes. Countdown is a counter of consecutive errors in the matching process.

Outputs: Returns the beginning of the best matching fragment of score and the countdown.

>>> scNotes = corpus.parse('luca/gloria').parts[0].flatten().notes.stream()
>>> scoreStream = scNotes

>>> tf = 'test_audio.wav'
>>> freqFromAQList = audioSearch.getFrequenciesFromAudioFile(waveFilename=tf)
>>> chrome = scale.ChromaticScale('C4')
>>> detectedPitchesFreq = audioSearch.detectPitchFrequencies(freqFromAQList, useScale=chrome)
>>> detectedPitchesFreq = audioSearch.smoothFrequencies(detectedPitchesFreq)
>>> (detectedPitches, listPlot) = audioSearch.pitchFrequenciesToObjects(
...                                             detectedPitchesFreq, useScale=chrome)
>>> (notesList, durationList) = audioSearch.joinConsecutiveIdenticalPitches(detectedPitches)
>>> transcribedScore, qle = audioSearch.notesAndDurationsToStream(notesList, durationList,
...                                             scNotes=scNotes, qle=None)
>>> hop = 6
>>> tn_recording = 24
>>> totScores = []
>>> beginningData = []
>>> lengthData = []
>>> for i in range(4):
...     excerpt = scoreStream[i * hop + 1:i * hop + tn_recording + 1]
...     scNotes = stream.Part(excerpt)
...     name = str(i)
...     beginningData.append(i * hop + 1)
...     lengthData.append(tn_recording)
...     scNotes.id = name
...     totScores.append(scNotes)
>>> listOfParts = search.approximateNoteSearch(transcribedScore.flatten().notes.stream(),
...                                            totScores)
>>> notePrediction = 0
>>> lastNotePosition = 0
>>> countdown = 0
>>> positionInList, countdown = audioSearch.decisionProcess(
...          listOfParts, notePrediction, beginningData, lastNotePosition, countdown)
>>> print(positionInList)
0

The countdown result is 1 because the song used is completely different from the score!!

>>> print(countdown)
1
music21.audioSearch.detectPitchFrequencies(freqFromAQList, useScale=None)

Detects the pitches of the notes from a list of frequencies, using thresholds which depend on the useScale option. If useScale is None, the default value is the Major Scale beginning C4.

Returns the frequency of each pitch after normalizing them.

>>> freqFromAQList=[143.627689055, 99.0835452019, 211.004784689, 4700.31347962, 2197.9431119]
>>> cMaj = scale.MajorScale('C4')
>>> pitchesList = audioSearch.detectPitchFrequencies(freqFromAQList, useScale=cMaj)
>>> for i in range(5):
...     print(int(round(pitchesList[i])))
147
98
220
4699
2093
music21.audioSearch.getFrequenciesFromAudioFile(waveFilename='xmas.wav')

gets a list of frequencies from a complete audio file.

Each sample is a window of audioSearch.audioChunkLength long.

>>> audioSearch.audioChunkLength
1024

>>> readPath = common.getSourceFilePath() / 'audioSearch' / 'test_audio.wav'
>>> freq = audioSearch.getFrequenciesFromAudioFile(waveFilename=readPath)
>>> print(freq)
[143.627..., 99.083..., 211.004..., 4700.313..., ...]
music21.audioSearch.getFrequenciesFromMicrophone(length=10.0, storeWaveFilename=None)

records for length (=seconds) a set of frequencies from the microphone.

If storeWaveFilename is not None, then it will store the recording on disk in a wave file.

Returns a list of frequencies detected.

TODO – find a way to test or at least demo

music21.audioSearch.getFrequenciesFromPartialAudioFile(waveFilenameOrHandle='temp', length=10.0, startSample=0)

It calculates the fundamental frequency at every instant of time of an audio signal extracted either from the microphone or from an already recorded song. It uses a period of time defined by the variable “length” in seconds.

It returns a list with the frequencies, a variable with the file descriptor, and the end sample position.

>>> readFile = 'pachelbel.wav'
>>> fTup  = audioSearch.getFrequenciesFromPartialAudioFile(readFile, length=1.0)
>>> frequencyList, pachelbelFileHandle, currentSample = fTup
>>> for frequencyIndex in range(5):
...     print(frequencyList[frequencyIndex])
143.627...
99.083...
211.004...
4700.313...
767.827...
>>> print(currentSample)  # should be near 44100, but probably not exact
44032

Now read the next 1 second:

>>> fTup = audioSearch.getFrequenciesFromPartialAudioFile(pachelbelFileHandle, length=1.0,
...                                                       startSample=currentSample)
>>> frequencyList, pachelbelFileHandle, currentSample = fTup
>>> for frequencyIndex in range(5):
...     print(frequencyList[frequencyIndex])
187.798...
238.263...
409.700...
149.958...
101.989...
>>> print(currentSample)  # should be exactly double the previous
88064
music21.audioSearch.histogram(data, bins)

Partition the list in data into a number of bins defined by bins and return the number of elements in each bin and a set of bins + 1 elements where the first element (0) is the start of the first bin, the last element (-1) is the end of the last bin, and every remaining element (i) is the dividing point between one bin and another.

>>> data = [1, 1, 4, 5, 6, 0, 8, 8, 8, 8, 8]
>>> outputData, bins = audioSearch.histogram(data, 8)
>>> print(outputData)
[3, 0, 0, 1, 1, 1, 0, 5]
>>> bins
[0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0]
>>> print([int(b) for b in bins])
[0, 1, 2, 3, 4, 5, 6, 7, 8]

>>> outputData, bins = audioSearch.histogram(data, 4)
>>> print(outputData)
[3, 1, 2, 5]
>>> print([int(b) for b in bins])
[0, 2, 4, 6, 8]
music21.audioSearch.interpolation(correlation, peak)

Interpolation for estimating the true position of an inter-sample maximum when nearby samples are known.

Correlation is a vector and peak is an index for that vector.

Returns the x coordinate of the vertex of that parabola.

>>> import numpy
>>> f = [2, 3, 1, 6, 4, 2, 3, 1]
>>> peak = numpy.argmax(f)
>>> peak  # f[3] is 6, which is the max.
3
>>> audioSearch.interpolation(f, peak)
3.21428571...
music21.audioSearch.joinConsecutiveIdenticalPitches(detectedPitchObjects)

takes a list of equally-spaced Pitch objects and returns a tuple of two lists, the first a list of Note or Rest objects (each of quarterLength 1.0) and a list of how many were joined together to make that object.

N.B. the returned list is NOT a Stream.

>>> readPath = common.getSourceFilePath() / 'audioSearch' / 'test_audio.wav'
>>> freqFromAQList = audioSearch.getFrequenciesFromAudioFile(waveFilename=readPath)
>>> chrome = scale.ChromaticScale('C4')
>>> detectedPitchesFreq = audioSearch.detectPitchFrequencies(freqFromAQList, useScale=chrome)
>>> detectedPitchesFreq = audioSearch.smoothFrequencies(detectedPitchesFreq)
>>> (detectedPitches, listPlot) = audioSearch.pitchFrequenciesToObjects(
...        detectedPitchesFreq, useScale=chrome)
>>> len(detectedPitches)
861
>>> notesList, durationList = audioSearch.joinConsecutiveIdenticalPitches(detectedPitches)
>>> len(notesList)
24
>>> print(notesList)
[<music21.note.Rest quarter>, <music21.note.Note C>, <music21.note.Note C>,
 <music21.note.Note D>, <music21.note.Note E>, <music21.note.Note F>,
 <music21.note.Note G>, <music21.note.Note A>, <music21.note.Note B>,
 <music21.note.Note C>, ...]
>>> print(durationList)
[71, 6, 14, 23, 34, 40, 27, 36, 35, 15, 17, 15, 6, 33, 22, 13, 16, 39, 35, 38, 27, 27, 26, 8]
music21.audioSearch.normalizeInputFrequency(inputPitchFrequency, thresholds=None, pitches=None)

Takes in an inputFrequency, a set of threshold values, and a set of allowable pitches (given by prepareThresholds) and returns a tuple of the normalized frequency and the pitch detected (as a Pitch object)

It will convert the frequency to be within the range of the default frequencies (usually C4 to C5) but the pitch object will have the correct octave.

>>> audioSearch.normalizeInputFrequency(441.72)
(440.0, <music21.pitch.Pitch A4>)

If you will be doing this often, it’s best to cache your thresholds and pitches by running prepareThresholds once first:

>>> thresholds, pitches = audioSearch.prepareThresholds(scale.ChromaticScale('C4'))
>>> for fq in [450, 510, 550, 600]:
...      print(audioSearch.normalizeInputFrequency(fq, thresholds, pitches))
(440.0, <music21.pitch.Pitch A4>)
(523.25113..., <music21.pitch.Pitch C5>)
(277.18263..., <music21.pitch.Pitch C#5>)
(293.66476..., <music21.pitch.Pitch D5>)
music21.audioSearch.notesAndDurationsToStream(notesList, durationList, scNotes=None, removeRestsAtBeginning=True, qle=None)

take a list of Note objects or rests and an equally long list of how long each one lasts in terms of samples and returns a Stream using the information from quarterLengthEstimation and quantizeDurations.

returns a Score object, containing a metadata object and a single Part object, which in turn contains the notes, etc. Does not run makeNotation() on the Score.

>>> durationList = [20, 19, 10, 30, 6, 21]
>>> n = note.Note
>>> noteList = [n('C#4'), n('D5'), n('B4'), n('F#5'), n('C5'), note.Rest()]
>>> s,lengthPart = audioSearch.notesAndDurationsToStream(noteList, durationList)
>>> s.show('text')
{0.0} <music21.metadata.Metadata object at ...>
{0.0} <music21.stream.Part ...>
    {0.0} <music21.note.Note C#>
    {1.0} <music21.note.Note D>
    {2.0} <music21.note.Note B>
    {2.5} <music21.note.Note F#>
    {4.0} <music21.note.Note C>
    {4.25} <music21.note.Rest quarter>
music21.audioSearch.pitchFrequenciesToObjects(detectedPitchesFreq, useScale=None)

Takes in a list of detected pitch frequencies and returns a tuple where the first element is a list of :class:~`music21.pitch.Pitch` objects that best match these frequencies and the second element is a list of the frequencies of those objects that can be plotted for matplotlib

TODO: only return the former. The latter can be generated in other ways.

>>> readPath = common.getSourceFilePath() / 'audioSearch' / 'test_audio.wav'
>>> freqFromAQList = audioSearch.getFrequenciesFromAudioFile(waveFilename=readPath)

>>> detectedPitchesFreq = audioSearch.detectPitchFrequencies(
...   freqFromAQList, useScale=scale.ChromaticScale('C4'))
>>> detectedPitchesFreq = audioSearch.smoothFrequencies(detectedPitchesFreq)
>>> (detectedPitchObjects, listPlot) = audioSearch.pitchFrequenciesToObjects(
...   detectedPitchesFreq, useScale=scale.ChromaticScale('C4'))
>>> [str(p) for p in detectedPitchObjects]
['A5', 'A5', 'A6', 'D6', 'D4', 'B4', 'A4', 'F4', 'E-4', 'C#3', 'B3', 'B3', 'B3', 'A3', 'G3',...]
music21.audioSearch.prepareThresholds(useScale=None)

returns two elements. The first is a list of threshold values for one octave of a given scale, useScale, (including the octave repetition) (Default is a ChromaticScale). The second is the pitches of the scale.

A threshold value is the fractional part of the log-base-2 value of the frequency.

For instance if A = 440 and B-flat = 460, then the threshold between A and B-flat will be 450. Notes below 450 should be considered As and those above 450 should be considered B-flats.

Thus, the list returned has one less element than the number of notes in the scale + octave repetition. If useScale is a ChromaticScale, prepareThresholds will return a 12 element list. If it’s a diatonic scale, it’ll have 7 elements.

>>> pitchThresholds, pitches = audioSearch.prepareThresholds(scale.MajorScale('A3'))
>>> for i in range(len(pitchThresholds)):
...    print(f'{pitches[i]} < {pitchThresholds[i]:.2f} < {pitches[i + 1]}')
A3 < 0.86 < B3
B3 < 0.53 < C#4
C#4 < 0.16 < D4
D4 < 0.28 < E4
E4 < 0.45 < F#4
F#4 < 0.61 < G#4
G#4 < 1.24 < A4
music21.audioSearch.quantizeDuration(length)

round an approximately transcribed quarterLength to a better one in music21.

Should be replaced by a full-featured routine in midi or stream.

See quantize() for more information on the standard music21 methodology.

>>> audioSearch.quantizeDuration(1.01)
1.0
>>> audioSearch.quantizeDuration(1.70)
1.5
music21.audioSearch.quarterLengthEstimation(durationList, mostRepeatedQuarterLength=1.0)

takes a list of lengths of notes (measured in audio samples) and tries to estimate what the length of a quarter note should be in this list.

If mostRepeatedQuarterLength is another number, it still returns the estimated length of a quarter note, but chooses it so that the most common note in durationList will be the other note. See example 2:

Returns a float – and not an int.

>>> durationList = [20, 19, 10, 30, 6, 21]
>>> audioSearch.quarterLengthEstimation(durationList)
20.625

Example 2: suppose these are the inputted durations for a score where most of the notes are half notes. Show how long a quarter note should be:

>>> audioSearch.quarterLengthEstimation(durationList, mostRepeatedQuarterLength=2.0)
10.3125
music21.audioSearch.smoothFrequencies(frequencyList: list[int | float], *, smoothLevels=7, inPlace=False) → list[int] | None

Smooths the shape of the signal in order to avoid false detections in the fundamental frequency. Takes in a list of ints or floats.

The second pitch below is obviously too low. It will be smoothed out:

>>> inputPitches = [440, 220, 440, 440, 442, 443, 441, 470, 440, 441, 440,
...                 442, 440, 440, 440, 397, 440, 440, 440, 442, 443, 441,
...                 440, 440, 440, 440, 440, 442, 443, 441, 440, 440]
>>> result = audioSearch.smoothFrequencies(inputPitches)
>>> result
[409, 409, 409, 428, 435, 438, 442, 444, 441, 441, 441,
 441, 434, 433, 432, 431, 437, 438, 439, 440, 440, 440,
 440, 440, 440, 441, 441, 441, 440, 440, 440, 440]

Original list is unchanged:

>>> inputPitches[1]
220

Different levels of smoothing have different effects. At smoothLevel=2, the isolated 220hz sample is pulling down the surrounding samples:

>>> audioSearch.smoothFrequencies(inputPitches, smoothLevels=2)[:5]
[330, 275, 358, 399, 420]

Doing this enough times will smooth out a lot of inconsistencies.

>>> audioSearch.smoothFrequencies(inputPitches, smoothLevels=28)[:5]
[432, 432, 432, 432, 432]

If inPlace is True then the list is modified in place and nothing is returned:

>>> audioSearch.smoothFrequencies(inputPitches, inPlace=True)
>>> inputPitches[:5]
[409, 409, 409, 428, 435]

Note that smoothLevels=1 is the baseline that does nothing:

>>> audioSearch.smoothFrequencies(inputPitches, smoothLevels=1) == inputPitches
True

And less than 1 raises a ValueError:

>>> audioSearch.smoothFrequencies(inputPitches, smoothLevels=0)
Traceback (most recent call last):
ValueError: smoothLevels must be >= 1

There cannot be more smoothLevels than input frequencies:

>>> audioSearch.smoothFrequencies(inputPitches, smoothLevels=40)
Traceback (most recent call last):
ValueError: There cannot be more smoothLevels (40) than inputPitches (32)

Note that the system runs on O(smoothLevels * len(frequenciesList)), so additional smoothLevels can be costly on a large set.

This function always returns a list of ints – rounding to the nearest hertz (you did want it smoothed right?)

  • Changed in v6: inPlace defaults to False (like other music21 functions) and if done in Place, returns nothing. smoothLevels and inPlace became keyword only.