A large-scale and high-quality dataset of annotated musical notes.

Contents

Motivation

Recent breakthroughs in generative modeling of images have been predicated on the availability of high-quality and large-scale datasebts such as MNIST, CIFAR and ImageNet. We recognized the need for an audio dataset that was as approachable as those in the image domain.

Audio signals found in the wild contain multi-scale dependencies that prove particularly difficult to model, leading many previous efforts at data-driven audio synthesis to focus on more constrained domains such as texture synthesis or training small parametric models.

We encourage the broader community to use NSynth as a benchmark and entry point into audio machine learning. We also view NSynth as a building block for future datasets and envision a high-quality multi-note dataset for tasks like generation and transcription that involve learning complex language-like dependencies.

Description

NSynth is an audio dataset containing 305,979 musical notes, each with a unique pitch, timbre, and envelope. For 1,006 instruments from commercial sample libraries, we generated four second, monophonic 16kHz audio snippets, referred to as notes, by ranging over every pitch of a standard MIDI pian o (21-108) as well as five different velocities (25, 50, 75, 100, 127). The note was held for the first three seconds and allowed to decay for the final second.

Some instruments are not capable of producing all 88 pitches in this range, resulting in an average of 65.4 pitches per instrument. Furthermore, the commercial sample packs occasionally contain duplicate sounds across multiple velocities, leaving an average of 4.75 unique velocities per pitch.

We also annotated each of the notes with three additional pieces of information based on a combination of human evaluation and heuristic algorithms:

Source: The method of sound production for the note’s instrument. This can be one of acoustic or electronic for instruments that were recorded from acoustic or electronic instruments, respectively, or synthetic for synthesized instruments. See their frequencies below.

Family: The high-level family of which the note’s instrument is a member. Each instrument is a member of exactly one family. See the complete list and their frequencies below.

Qualities: Sonic qualities of the note. See the quality descriptions and their co-occurrences below. Each note is annotated with zero or more qualities.

Format

Files

The NSynth dataset can be download in two formats:

TFRecord files of serialized TensorFlow Example protocol buffers with one Example proto per note.

JSON files containing non-audio features alongside 16-bit PCM WAV audio files.

The full dataset is split into three sets:

Train [tfrecord | json/wav]: A training set with 289,205 examples. Instruments do not overlap with valid or test.

[tfrecord | json/wav]: A training set with 289,205 examples. Instruments do not overlap with valid or test. Valid [tfrecord | json/wav]: A validation set with 12,678 examples. Instruments do not overlap with train.

[tfrecord | json/wav]: A validation set with 12,678 examples. Instruments do not overlap with train. Test [tfrecord | json/wav]: A test set with 4,096 examples. Instruments do not overlap with train.

Below we detail how the note features are encoded in the Example protocol buffers and JSON files.

Example Features

Each Example contains the following features.

Feature Type Description note int64 A unique integer identifier for the note. note_str bytes A unique string identifier for the note in the format <instrument_str>-<pitch>-<velocity> . instrument int64 A unique, sequential identifier for the instrument the note was synthesized from. instrument_str bytes A unique string identifier for the instrument this note was synthesized from in the format <instrument_family_str>-<instrument_production_str>-<instrument_name> . pitch int64 The 0-based MIDI pitch in the range [0, 127]. velocity int64 The 0-based MIDI velocity in the range [0, 127]. sample_rate int64 The samples per second for the audio feature. audio* [float] A list of audio samples represented as floating point values in the range [-1,1]. qualities [int64] A binary vector representing which sonic qualities are present in this note. qualities_str [bytes] A list IDs of which qualities are present in this note selected from the sonic qualities list. instrument_family int64 The index of the instrument family this instrument is a member of. instrument_family_str bytes The ID of the instrument family this instrument is a member of. instrument_source int64 The index of the sonic source for this instrument. instrument_source_str bytes The ID of the sonic source for this instrument.

* Note: the “audio” feature is ommited from the JSON-encoded examples since the audio data is stored separately in WAV files keyed by the “note_str”.

Feature Encodings

This section includes tables specifying the feature names and indicies used in the Example protos.

Instrument Sources

The method of sound production for the note’s instrument. Each instrument (and all of its notes) are labeled with exactly one.

Index ID 0 acoustic 1 electronic 2 synthetic





Instrument Families

The high-level family of which the note’s instrument is a member. Each instrument (and all of its notes) are labeled with exactly one.

Index ID 0 bass 1 brass 2 flute 3 guitar 4 keyboard 5 mallet 6 organ 7 reed 8 string 9 synth_lead 10 vocal





Note Qualities

We provide quality annotations for the 10 different note qualities described below. None of the tags are mutually exclusive by definition except for “bright” and “dark”. However, it is possible for a note to be neither “bright” nor “dark”.

Index ID Description 0 bright A large amount of high frequency content and strong upper harmonics. 1 dark A distinct lack of high frequency content, giving a muted and bassy sound. Also sometimes described as ‘Warm’. 2 distortion Waveshaping that produces a distinctive crunchy sound and presence of many harmonics. Sometimes paired with non-harmonic noise. 3 fast_decay Amplitude envelope of all harmonics decays substantially before the ‘note-off’ point at 3 seconds. 4 long_release Amplitude envelope decays slowly after the ‘note-off’ point, sometimes still present at the end of the sample 4 seconds. 5 multiphonic Presence of overtone frequencies related to more than one fundamental frequency. 6 nonlinear_env Modulation of the sound with a distinct envelope behavior different than the monotonic decrease of the note. Can also include filter envelopes as well as dynamic envelopes. 7 percussive A loud non-harmonic sound at note onset. 8 reverb Room acoustics that were not able to be removed from the original sample. 9 tempo-synced Rhythmic modulation of the sound to a fixed tempo.





Example

Below is a string view of the Example protocol buffer for an individual note in the dataset, with the audio portion suppressed:

{ # (tensorflow.Example) size=250.4K features : { # (tensorflow.Features) size=250.4K feature : { # (tensorflow.Features.FeatureEntry) size=21B key : "sample_rate" # size=11 value : { # (tensorflow.Feature) size=6B int64_list : { # (tensorflow.Int64List) size=4B value : [ 16000 ] } # features.feature[0].value.int64_list } # features.feature[0].value } # features.feature[0] feature : { # (tensorflow.Features.FeatureEntry) size=25B key : "qualities_str" # size=13 value : { # (tensorflow.Feature) size=8B bytes_list : { # (tensorflow.BytesList) size=6B value : [ "dark" ] } # features.feature[1].value.bytes_list } # features.feature[1].value } # features.feature[1] feature : { # (tensorflow.Features.FeatureEntry) size=42B key : "note_str" # size=8 value : { # (tensorflow.Feature) size=30B bytes_list : { # (tensorflow.BytesList) size=28B value : [ "bass_synthetic_033-022-050" ] # size=26 } # features.feature[2].value.bytes_list } # features.feature[2].value } # features.feature[2] feature : { # (tensorflow.Features.FeatureEntry) size=27B key : "qualities" # size=9 value : { # (tensorflow.Feature) size=14B int64_list : { # (tensorflow.Int64List) size=12B value : [ 0 , 1 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 ] } # features.feature[3].value.int64_list } # features.feature[3].value } # features.feature[3] feature : { # (tensorflow.Features.FeatureEntry) size=250.0K key : "audio" # size=5 value : { # (tensorflow.Feature) size=250.0K float_list : { # (tensorflow.FloatList) size=250.0K value : [ - 1.3311218e-07 , ..., 1.3244664e-07 ] } # features.feature[4].value.float_list } # features.feature[4].value } # features.feature[4] feature : { # (tensorflow.Features.FeatureEntry) size=26B key : "instrument_family" # size=17 value : { # (tensorflow.Feature) size=5B int64_list : { # (tensorflow.Int64List) size=3B value : [ 0 ] } # features.feature[5].value.int64_list } # features.feature[5].value } # features.feature[5] feature : { # (tensorflow.Features.FeatureEntry) size=14B key : "pitch" # size=5 value : { # (tensorflow.Feature) size=5B int64_list : { # (tensorflow.Int64List) size=3B value : [ 22 ] } # features.feature[6].value.int64_list } # features.feature[6].value } # features.feature[6] feature : { # (tensorflow.Features.FeatureEntry) size=26B key : "instrument_source" # size=17 value : { # (tensorflow.Feature) size=5B int64_list : { # (tensorflow.Int64List) size=3B value : [ 2 ] } # features.feature[7].value.int64_list } # features.feature[7].value } # features.feature[7] feature : { # (tensorflow.Features.FeatureEntry) size=40B key : "instrument_str" # size=14 value : { # (tensorflow.Feature) size=22B bytes_list : { # (tensorflow.BytesList) size=20B value : [ "bass_synthetic_033" ] # size=18 } # features.feature[8].value.bytes_list } # features.feature[8].value } # features.feature[8] feature : { # (tensorflow.Features.FeatureEntry) size=38B key : "instrument_source_str" # size=21 value : { # (tensorflow.Feature) size=13B bytes_list : { # (tensorflow.BytesList) size=11B value : [ "synthetic" ] # size=9 } # features.feature[9].value.bytes_list } # features.feature[9].value } # features.feature[9] feature : { # (tensorflow.Features.FeatureEntry) size=15B key : "note" value : { # (tensorflow.Feature) size=7B int64_list : { # (tensorflow.Int64List) size=5B value : [ 201034 ] # 196.32Ki; [if seconds]: 2 days 7 hours } # features.feature[10].value.int64_list } # features.feature[10].value } # features.feature[10] feature : { # (tensorflow.Features.FeatureEntry) size=20B key : "instrument" # size=10 value : { # (tensorflow.Feature) size=6B int64_list : { # (tensorflow.Int64List) size=4B value : [ 417 ] } # features.feature[11].value.int64_list } # features.feature[11].value } # features.feature[11] feature : { # (tensorflow.Features.FeatureEntry) size=33B key : "instrument_family_str" # size=21 value : { # (tensorflow.Feature) size=8B bytes_list : { # (tensorflow.BytesList) size=6B value : [ "bass" ] } # features.feature[12].value.bytes_list } # features.feature[12].value } # features.feature[12] feature : { # (tensorflow.Features.FeatureEntry) size=17B key : "velocity" # size=8 value : { # (tensorflow.Feature) size=5B int64_list : { # (tensorflow.Int64List) size=3B value : [ 50 ] } # features.feature[13].value.int64_list } # features.feature[13].value } # features.feature[13] } # features }

This is a view of the same Example in JSON format:

"bass_synthetic_033-022-050" : { "note" : 201034 , "sample_rate" : 16000 , "instrument_family" : 0 , "qualities" : [ 0 , 1 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 ], "instrument_source_str" : "synthetic" , "note_str" : "bass_synthetic_033-022-050" , "instrument_family_str" : "bass" , "instrument_str" : "bass_synthetic_033" , "pitch" : 22 , "instrument" : 417 , "velocity" : 50 , "instrument_source" : 2 , "qualities_str" : [ "dark" ] }

Statistics

Instrument Classes

Frequency counts for instrument classes with Sources as columns an Families as rows.

Family Acoustic Electronic Synthetic Total Bass 200 8,387 60,368 68,955 Brass 13,760 70 0 13,830 Flute 6,572 35 2,816 9,423 Guitar 13,343 16,805 5,275 35,423 Keyboard 8,508 42,645 3,838 54,991 Mallet 27,722 5,581 1,763 35,066 Organ 176 36,401 0 36,577 Reed 14,262 76 528 14,866 String 20,510 84 0 20,594 Synth Lead 0 0 5,501 5,501 Vocal 3,925 140 6,688 10,753 Total 108,978 110,224 86,777 305,979





Qualitiy Co-occurrences

Co-occurrence probabilities and marginal frequencies of quality annotations. Both are presented as percentages.

Quality Bright Dark Distortion Fast Decay Long Release Multiphonic Nonlinear Envelope Percussive Reverb Tempo-Synced Dark 0.0 Distortion 25.9 2.5 Fast Decay 10.0 7.5 8.1 Long Release 9.0 5.2 9.8 0.0 Multiphonic 6.0 1.5 5.4 2.8 6.9 Nonlinear Envelope 8.5 1.4 6.6 2.1 6.7 8.6 Percussive 6.2 5.1 3.0 52.0 0.8 2.4 0.9 Reverb 6.6 8.9 0.3 13.0 13.7 0.7 3.5 12.4 Tempo-Synced 2.4 1.8 5.2 0.4 6.4 9.3 2.3 1.5 0.0 Frequency 13.5 11.0 17.0 14.7 8.5 3.4 3.2 10.2 16.8 1.8





License

The dataset is made available by Google Inc. under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.

How to Cite

If you use the NSynth dataset in your work, please cite the paper where it was introduced:

Jesse Engel, Cinjon Resnick, Adam Roberts, Sander Dieleman, Douglas Eck, Karen Simonyan, and Mohammad Norouzi. "Neural Audio Synthesis of Musical Notes with WaveNet Autoencoders." 2017.

You can also use the following BibTeX entry:

@misc{nsynth2017, Author = {Jesse Engel and Cinjon Resnick and Adam Roberts and Sander Dieleman and Douglas Eck and Karen Simonyan and Mohammad Norouzi}, Title = {Neural Audio Synthesis of Musical Notes with WaveNet Autoencoders}, Year = {2017}, Eprint = {arXiv:1704.01279}, }