minst-dataset

The MNIST of music instrument sounds.

What's going on here?

The goal of this project is to consolidate various disparate solo instrument collections into one big, normalized dataset for ease of use, namely with machine learning in mind. Simply put, this aims to be the MNIST for music audio processing.

Installing Dependencies

$ pip install git+git://github.com/ejhumphrey/minst-dataset.git
$ cd minst-dataset

$ pip install -U -r requirements.txt
OR
$ make deps

Testing your Install / Setup

This project ships with just enough data to test itself.

$ make test

Building the dataset from scratch

Directory Structure

This library expects a certain directory structure for everything to work nicely. The following is a flyover of what this should look like locally by default (according to Makefile):

{DATA_DIR}/
  uiowa/
    ...
  RWC Instruments/
    ...
  philharmonia/
    ...

The only value in the Makefile you may need to update is DATA_DIR, in the case that you would prefer the data live elsewhere when downloaded, e.g. a different hard drive.

Get the data

This project uses four different solo instrument datasets.

• University of Iowa - MIS
• Philharmonia
• RWC - Instruments
• Good Sounds

We provide "manifest" files with which one can download the first two collections. For access to the third (RWC), you should contact the kind folks at AIST. Access to Good-Sounds is public, but requires that you fill out a form first.

To download the available data, you can invoke the following from your cloned repository:

$ make download

(which is equivalent to:)

$ python scripts/download.py data/uiowa.json ~/data/uiowa
...
$ python scripts/download.py data/philharmonia.json ~/data/philharmonia

Preparing note audio using annotated onsets

Assuming your data is downloaded and available, you can use the following to build the index from the downloaded files, and then extract the note audio from it.

Warning: extracting notes could take up to an hour with all four datasets.

$ make build

Afterwards, the annotated notes should be available in these files:

uiowa_notes.csv
phil_notes.csv
rwc_notes.csv
goodsounds_notes.csv

If the dataset is not available on your machine, make build should skip it.

Build final dataset *.csv's for experimenting

$ make dataset

This will generate the following files in your data_dir (~/data/minst):

• master_index.csv A master dataset file containing pointers to the audio and targets/metadata. The "index" is the first column.

• rwc_partitions.csv

• uiowa_partitions.csv

• philharmonia_partitions.csv These csvs designed to be sort of "three-fold" cross validation, where each dataset acts as a hold-out test set. The dataset in the name is the test set.

These csvs contain two columns: "index", and "partition". "Index" is the index in master_index.csv, and "partition" is one of ['train', 'val', 'test'], and specifies which dataset partition to use the files for.

If you are loading using python/pandas, getting the training data would look like this:

master_df = pd.read_csv('master_index.csv', index_col=0)
partition_df = pd.read_csv('rwc_partitions.csv')

train_df = master_df.loc[(partition_df['partition'] == 'train').index]

Note Counts Per Dataset for Accepted Instruments

Instrument	UIowa	Philharmonia	RWC	Good-Sounds
totals	3417	7923	27557	12015
bassoon	122	648	1405
cello	681	776	3196	2118
clarinet	258	770	1433	3359
double-bass	587	781	3465
flute	227	781	1095	2308
guitar	352	71	5618
horn-french	96	546	1896
oboe	104	539	770	494
trombone	66	769	2738
trumpet	212	433	1965	1883
tuba	111	838	540
violin	601	971	3436	1853

Appendix: Computing / finding note onsets

This repository contains some generated / annotated onsets for the instruments we have selected in the taxonomy. If you wish to annotate more, however, you will need to do the following:

Both the UIowa and RWC collections ship as recordings with multiple notes per file. To establish a clean dataset with which to work, it is advisable to split up these recordings (roughly) at note onsets.

Somewhat surprisingly, modern onset detection algorithms have not been optimized for this use case, and it is challenging to find a single parameterization that works well over the entire collection. To overcome this deficiency, we take a human-in-the-loop approach to robustly arrive at good cut-points for these (and future) collections.

First, collect a single dataset:

$ make uiowa_index.csv
OR
$ python scripts/collect_data.py uiowa path/to/download uiowa_index.csv

Optionally, you can run a segmentation algorithm over the resulting index. This will generate a number of best-guess onsets, saved out as CSV files under the same index as the collection, and an new dataframe tracking where these aligned files live locally (uiowa_onsets/segment_index.csv below):

Warning: buggy.

$ python scripts/compute_note_onsets.py uiowa_index.csv uiowa_onsets \
    segment_index.csv --mode logcqt --num_cpus -1 --verbose 50

Either way, you'll want to verify and correct (as needed) the estimated onsets. To do so, drop into the annotation routine by the following:

$ python scripts/annotate.py uiowa_onsets/segment_index.csv

GUI Command Summary:

• spacebar: add / remove a marker at the location of the mouse cursor
• up arrow: move all markers .1s to the left
• down arrow: move all markers .1s to the right
• left arrow: move all markers .01s to the left
• right arrow: move all markers .01s to the right
• d: delete marker within 1s of cursor
• D: delete marker within 5s of cursor
• 1: Replace all markers with envelope_onsets(wait=.008s)
• 2: Replace all markers with envelope_onsets(wait=.01s)
• 3: Replace all markers with envelope_onsets(wait=.02s)
• 4: Replace all markers with envelope_onsets(wait=.05s)
• 6: Replace all markers with logcqt_onsets(wait=.01s)
• 7: Replace all markers with logcqt_onsets(wait=.02s)
• w will write the current markers
• q will close current file without saving
• x will write onset data and close
• Q To kill the process entirely

Improvements / enhancements are more than welcomed.