audio
audioduration (s)
4
4
root_note_name
stringclasses
4 values
chord_type
stringclasses
4 values
inversion
int64
5
64
root_note_is_accidental
bool
2 classes
root_note_pitch_class
int64
0
3
midi_program_num
int64
0
111
midi_program_name
stringclasses
92 values
midi_category
stringclasses
13 values
C
major
5
false
0
0
Acoustic Grand Piano
Piano Timbres
C
major
5
false
0
1
Bright Acoustic Piano
Piano Timbres
C
major
5
false
0
2
Electric Grand Piano
Piano Timbres
C
major
5
false
0
3
Honky-tonk Piano
Piano Timbres
C
major
5
false
0
4
Rhodes Piano
Piano Timbres
C
major
5
false
0
5
Chorused Piano
Piano Timbres
C
major
5
false
0
6
Harpsichord
Piano Timbres
C
major
5
false
0
7
Clavinet
Piano Timbres
C
major
5
false
0
10
Music Box
Chromatic Percussion
C
major
5
false
0
11
Vibraphone
Chromatic Percussion
C
major
5
false
0
12
Marimba
Chromatic Percussion
C
major
5
false
0
13
Xylophone
Chromatic Percussion
C
major
5
false
0
15
Dulcimer
Chromatic Percussion
C
major
5
false
0
16
Hammond Organ
Organ Timbres
C
major
5
false
0
17
Percussive Organ
Organ Timbres
C
major
5
false
0
18
Rock Organ
Organ Timbres
C
major
5
false
0
19
Church Organ
Organ Timbres
C
major
5
false
0
20
Reed Organ
Organ Timbres
C
major
5
false
0
24
Acoustic Nylon Guitar
Guitar Timbres
C
major
5
false
0
25
Acoustic Steel Guitar
Guitar Timbres
C
major
5
false
0
26
Electric Jazz Guitar
Guitar Timbres
C
major
5
false
0
27
Electric Clean Guitar
Guitar Timbres
C
major
5
false
0
28
Electric Muted Guitar
Guitar Timbres
C
major
5
false
0
29
Overdriven Guitar
Guitar Timbres
C
major
5
false
0
30
Distortion Guitar
Guitar Timbres
C
major
5
false
0
32
Acoustic Bass
Bass Timbres
C
major
5
false
0
33
Fingered Electric Bass
Bass Timbres
C
major
5
false
0
34
Plucked Electric Bass
Bass Timbres
C
major
5
false
0
35
Fretless Bass
Bass Timbres
C
major
5
false
0
36
Slap Bass 1
Bass Timbres
C
major
5
false
0
37
Slap Bass 2
Bass Timbres
C
major
5
false
0
38
Synth Bass 1
Bass Timbres
C
major
5
false
0
39
Synth Bass 2
Bass Timbres
C
major
5
false
0
40
Violin
String Timbres
C
major
5
false
0
41
Viola
String Timbres
C
major
5
false
0
42
Cello
String Timbres
C
major
5
false
0
43
Contrabass
String Timbres
C
major
5
false
0
44
Tremolo Strings
String Timbres
C
major
5
false
0
45
Pizzicato Strings
String Timbres
C
major
5
false
0
46
Orchestral Harp
String Timbres
C
major
5
false
0
48
String Ensemble 1
Ensemble Timbres
C
major
5
false
0
49
String Ensemble 2
Ensemble Timbres
C
major
5
false
0
50
Synth Strings 1
Ensemble Timbres
C
major
5
false
0
51
Synth Strings 2
Ensemble Timbres
C
major
5
false
0
52
Choir "Aah"
Ensemble Timbres
C
major
5
false
0
53
Choir "Ooh"
Ensemble Timbres
C
major
5
false
0
54
Synth Voice
Ensemble Timbres
C
major
5
false
0
56
Trumpet
Brass Timbres
C
major
5
false
0
57
Trombone
Brass Timbres
C
major
5
false
0
58
Tuba
Brass Timbres
C
major
5
false
0
59
Muted Trumpet
Brass Timbres
C
major
5
false
0
60
French Horn
Brass Timbres
C
major
5
false
0
61
Brass Section
Brass Timbres
C
major
5
false
0
62
Synth Brass 1
Brass Timbres
C
major
5
false
0
63
Synth Brass 2
Brass Timbres
C
major
5
false
0
64
Soprano Sax
Reed Timbres
C
major
5
false
0
65
Alto Sax
Reed Timbres
C
major
5
false
0
66
Tenor Sax
Reed Timbres
C
major
5
false
0
67
Baritone Sax
Reed Timbres
C
major
5
false
0
68
Oboe
Reed Timbres
C
major
5
false
0
69
English Horn
Reed Timbres
C
major
5
false
0
70
Bassoon
Reed Timbres
C
major
5
false
0
71
Clarinet
Reed Timbres
C
major
5
false
0
72
Piccolo
Pipe Timbres
C
major
5
false
0
73
Flute
Pipe Timbres
C
major
5
false
0
74
Recorder
Pipe Timbres
C
major
5
false
0
75
Pan Flute
Pipe Timbres
C
major
5
false
0
76
Bottle Blow
Pipe Timbres
C
major
5
false
0
79
Ocarina
Pipe Timbres
C
major
5
false
0
80
Square Wave Lead
Synth Lead
C
major
5
false
0
81
Sawtooth Wave Lead
Synth Lead
C
major
5
false
0
82
Calliope Lead
Synth Lead
C
major
5
false
0
83
Chiff Lead
Synth Lead
C
major
5
false
0
84
Charang Lead
Synth Lead
C
major
5
false
0
85
Voice Lead
Synth Lead
C
major
5
false
0
87
Bass Lead
Synth Lead
C
major
5
false
0
88
New Age Pad
Synth Pad
C
major
5
false
0
89
Warm Pad
Synth Pad
C
major
5
false
0
90
Polysynth Pad
Synth Pad
C
major
5
false
0
91
Choir Pad
Synth Pad
C
major
5
false
0
92
Bowed Pad
Synth Pad
C
major
5
false
0
93
Metallic Pad
Synth Pad
C
major
5
false
0
94
Halo Pad
Synth Pad
C
major
5
false
0
95
Sweep Pad
Synth Pad
C
major
5
false
0
104
Sitar
Ethnic Timbres
C
major
5
false
0
105
Banjo
Ethnic Timbres
C
major
5
false
0
106
Shamisen
Ethnic Timbres
C
major
5
false
0
107
Koto
Ethnic Timbres
C
major
5
false
0
108
Kalimba
Ethnic Timbres
C
major
5
false
0
109
Bagpipe
Ethnic Timbres
C
major
5
false
0
110
Fiddle
Ethnic Timbres
C
major
5
false
0
111
Shanai
Ethnic Timbres
C
major
6
false
0
0
Acoustic Grand Piano
Piano Timbres
C
major
6
false
0
1
Bright Acoustic Piano
Piano Timbres
C
major
6
false
0
2
Electric Grand Piano
Piano Timbres
C
major
6
false
0
3
Honky-tonk Piano
Piano Timbres
C
major
6
false
0
4
Rhodes Piano
Piano Timbres
C
major
6
false
0
5
Chorused Piano
Piano Timbres
C
major
6
false
0
6
Harpsichord
Piano Timbres
C
major
6
false
0
7
Clavinet
Piano Timbres

Dataset Card for SynTheory

Dataset Summary

SynTheory is a synthetic dataset of music theory concepts, specifically rhythmic (tempos and time signatures) and tonal (notes, intervals, scales, chords, and chord progressions).

Each of these 7 concepts has its own config.

tempos consist of 161 total integer tempos (bpm) ranging from 50 BPM to 210 BPM (inclusive), 5 percussive instrument types (click_config_name), and 5 random start time offsets (offset_time).

time_signatures consist of 8 time signatures (time_signature), 5 percussive instrument types (click_config_name), 10 random start time offsets (offset_time), and 3 reverb levels (reverb_level). The 8 time signatures are 2/2, 2/4, 3/4, 3/8, 4/4, 6/8, 9/8, and 12/8.

notes consist of 12 pitch classes (root_note_name), 9 octaves (octave), and 92 instrument types (midi_program_name). The 12 pitch classes are C, C#, D, D#, E, F, F#, G, G#, A, A# and B.

intervals consist of 12 interval sizes (interval), 12 root notes (root_note_name), 92 instrument types (midi_program_name), and 3 play styles (play_style_name). The 12 intervals are minor 2nd, Major 2nd, minor 3rd, Major 3rd, Perfect 4th, Tritone, Perfect 5th, minor 6th, Major 6th, minor 7th, Major 7th, and Perfect octave.

scales consist of 7 modes (mode), 12 root notes (root_note_name), 92 instrument types (midi_program_name), and 2 play styles (play_style_name). The 7 modes are Ionian, Dorian, Phrygian, Lydian, Mixolydian, Aeolian, and Locrian.

chords consist of 4 chord quality (chord_type), 3 inversions (inversion), 12 root notes (root_note_name), and 92 instrument types (midi_program_name). The 4 chord quality types are major, minor, augmented, and diminished. The 3 inversions are root position, first inversion, and second inversion.

simple_progressions consist of 19 chord progressions (chord_progression), 12 root notes (key_note_name), and 92 instrument types (midi_program_name). The 19 chord progressions consist of 10 chord progressions in major mode and 9 in natural minor mode. The major mode chord progressions are (I–IV–V–I), (I–IV–vi–V), (I–V–vi–IV), (I–vi–IV–V), (ii–V–I–Vi), (IV–I–V–Vi), (IV–V–iii–Vi), (V–IV–I–V), (V–vi–IV–I), and (vi–IV–I–V). The natural minor mode chord progressions are (i–ii◦–v–i), (i–III–iv–i), (i–iv–v–i), (i–VI–III–VII), (i–VI–VII–i), (i–VI–VII–III), (i–VII–VI–IV), (iv–VII–i–i), and (VII–vi–VII–i).

Supported Tasks and Leaderboards

  • audio-classification: This can be used towards music theory classification tasks.
  • feature-extraction: Our samples can be fed into pretrained audio codecs to extract representations from the model, which can be further used for downstream MIR tasks.

How to use

The datasets library allows you to load and pre-process your dataset in pure Python, at scale. The dataset can be downloaded and prepared in one call to your local drive by using the load_dataset function.

For example, to download the notes config, simply specify the corresponding language config name (i.e., "notes"):

from datasets import load_dataset

notes = load_dataset("meganwei/syntheory", "notes")

Using the datasets library, you can also stream the dataset on-the-fly by adding a streaming=True argument to the load_dataset function call. Loading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire dataset to disk.

from datasets import load_dataset

notes = load_dataset("meganwei/syntheory", "notes", streaming=True)

print(next(iter(notes)))

Bonus: create a PyTorch dataloader directly with your own datasets (local/streamed).

Local:

from datasets import load_dataset
from torch.utils.data.sampler import BatchSampler, RandomSampler
from torch.utils.data import DataLoader

notes = load_dataset("meganwei/syntheory", "notes")
batch_sampler = BatchSampler(RandomSampler(notes), batch_size=32, drop_last=False)
dataloader = DataLoader(notes, batch_sampler=batch_sampler)

Streaming:

from datasets import load_dataset
from torch.utils.data import DataLoader

notes = load_dataset("meganwei/syntheory", "notes", streaming=True)
dataloader = DataLoader(notes, batch_size=32)

To find out more about loading and preparing audio datasets, head over to hf.co/blog/audio-datasets.

Example scripts

[More Information Needed]

Dataset Structure

Data Fields

[More Information Needed]

Dataset Creation

Curation Rationale

[More Information Needed]

Source Data

Initial Data Collection and Normalization

[More Information Needed]

Who are the source language producers?

[More Information Needed]

Annotations

Annotation process

[More Information Needed]

Who are the annotators?

[More Information Needed]

Personal and Sensitive Information

[More Information Needed]

Considerations for Using the Data

Social Impact of Dataset

[More Information Needed]

Discussion of Biases

[More Information Needed]

Other Known Limitations

For the notes music theory concept, there are 9,936 distinct note configurations. However, our dataset contains 9,848 non-silent samples. The 88 silent samples at extreme registers are unvoiceable with our soundfont. With a more complete soundfont, all 9,936 configurations are realizable to audio.

The silent samples are the following audio files: 0_0_C_10_Music_Box.wav, 0_0_C_56_Trumpet.wav, 0_0_C_68_Oboe.wav, 1_0_C#_10_Music_Box.wav, 1_0_C#_56_Trumpet.wav, 1_0_C#_68_Oboe.wav, 2_0_D_10_Music_Box.wav, 2_0_D_56_Trumpet.wav, 2_0_D_68_Oboe.wav, 3_0_D#_10_Music_Box.wav, 3_0_D#_56_Trumpet.wav, 3_0_D#_68_Oboe.wav, 4_0_E_10_Music_Box.wav, 4_0_E_56_Trumpet.wav, 4_0_E_68_Oboe.wav, 5_0_F_10_Music_Box.wav, 5_0_F_56_Trumpet.wav, 5_0_F_68_Oboe.wav, 6_0_F#_10_Music_Box.wav, 6_0_F#_56_Trumpet.wav, 6_0_F#_68_Oboe.wav, 7_0_G_10_Music_Box.wav, 7_0_G_56_Trumpet.wav, 7_0_G_68_Oboe.wav, 8_0_G#_10_Music_Box.wav, 8_0_G#_56_Trumpet.wav, 8_0_G#_68_Oboe.wav, 9_0_A_10_Music_Box.wav, 9_0_A_56_Trumpet.wav, 9_0_A_68_Oboe.wav, 10_0_A#_10_Music_Box.wav, 10_0_A#_56_Trumpet.wav, 10_0_A#_68_Oboe.wav, 11_0_B_10_Music_Box.wav, 11_0_B_56_Trumpet.wav, 11_0_B_68_Oboe.wav, 12_0_C_68_Oboe.wav, 13_0_C#_68_Oboe.wav, 14_0_D_68_Oboe.wav, 15_0_D#_68_Oboe.wav, 16_0_E_68_Oboe.wav, 17_0_F_68_Oboe.wav, 18_0_F#_68_Oboe.wav, 19_0_G_68_Oboe.wav, 20_0_G#_68_Oboe.wav, 21_0_A_68_Oboe.wav, 22_0_A#_68_Oboe.wav, 23_0_B_68_Oboe.wav, 24_0_C_68_Oboe.wav, 25_0_C#_68_Oboe.wav, 26_0_D_68_Oboe.wav, 27_0_D#_68_Oboe.wav, 28_0_E_68_Oboe.wav, 29_0_F_68_Oboe.wav, 30_0_F#_68_Oboe.wav, 31_0_G_68_Oboe.wav, 32_0_G#_68_Oboe.wav, 33_0_A_68_Oboe.wav, 34_0_A#_68_Oboe.wav, 35_0_B_68_Oboe.wav, 80_2_G#_67_Baritone_Sax.wav, 81_2_A_67_Baritone_Sax.wav, 82_2_A#_67_Baritone_Sax.wav, 83_2_B_67_Baritone_Sax.wav, 84_2_C_67_Baritone_Sax.wav, 85_2_C#_67_Baritone_Sax.wav, 86_2_D_67_Baritone_Sax.wav, 87_2_D#_67_Baritone_Sax.wav, 88_2_E_67_Baritone_Sax.wav, 89_2_F_67_Baritone_Sax.wav, 90_2_F#_67_Baritone_Sax.wav, 91_2_G_67_Baritone_Sax.wav, 92_2_G#_67_Baritone_Sax.wav, 93_2_A_67_Baritone_Sax.wav, 94_2_A#_67_Baritone_Sax.wav, 95_2_B_67_Baritone_Sax.wav, 96_2_C_67_Baritone_Sax.wav, 97_2_C#_67_Baritone_Sax.wav, 98_2_D_67_Baritone_Sax.wav, 99_2_D#_67_Baritone_Sax.wav, 100_2_E_67_Baritone_Sax.wav, 101_2_F_67_Baritone_Sax.wav, 102_2_F#_67_Baritone_Sax.wav, 103_2_G_67_Baritone_Sax.wav, 104_2_G#_67_Baritone_Sax.wav, 105_2_A_67_Baritone_Sax.wav, 106_2_A#_67_Baritone_Sax.wav, and 107_2_B_67_Baritone_Sax.wav.

Additional Information

Dataset Curators

[More Information Needed]

Licensing Information

[More Information Needed]

Citation Information

@inproceedings{Wei2024-music,
  title={Do Music Generation Models Encode Music Theory?},
  author={Wei, Megan and Freeman, Michael and Donahue, Chris and Sun, Chen},
  booktitle={International Society for Music Information Retrieval},
  year={2024}
}

Data Statistics

Concept Number of Samples
Tempo 4,025
Time Signatures 1,200
Notes 9,936
Intervals 39,744
Scales 15,456
Chords 13,248
Chord Progressions 20,976
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