Audio-to-Score Alignment Using Deep Automatic Music Transcription

This is the code connected with paper:

F. Simonetta, S. Ntalampiras, and F. Avanzini, “Audio-to-Score Alignment Using Deep Automatic Music Transcription,” in Proceeddings of the IEEE MMSP 2021, 2021. Link to PDF

!! Errata Corridge !!

After the publication at MMSP2021, an error was found in the code. I have rerun all the experiments and updated the publication on Arxiv.

In a few words, most of the conclusions of the original publication hold, but I found that in non-piano music, the TAFE and Bytedance EIFE are better than the old SEBA method, as published in the paper.

The following are the updated images. They are contained in the mlruns directory (see Audio-to-midi section):

1. Piano w/o missing notes:

• Onsets
• Offsets

1. Piano w/ missing notes:

• Onsets
• Offsets

1. Multi w/o missing notes:

• Onsets
• Offsets

1. Multi w/ missing notes:

• Onsets
• Offsets

Setup

Python

1. Install pyenv: curl https://pyenv.run | bash; exec $SHELL
2. Install python 3.8.6: PYTHON_CONFIGURE_OPTS="--enable-shared" pyenv install 3.8.6
3. Install poetry: curl -sSL https://raw.githubusercontent.com/python-poetry/poetry/master/get-poetry.py | python -
4. Install project dependencies: poetry install --no-root
5. Install magenta with pip (it breaks dependencies, but we don't mind about them): poetry run pip install magenta

Julia and C/CPP

1. Install a gcc or other C/C++ compiler
2. Install Julia 1.6.0
3. Run poetry run python setup.py build_ext --inplace to build C extensions using Cython and Julia
4. Compile Eita Nakamura code with ./eita_tool/compile.sh

N.B. If Julia has troubles installing, try to install python with the command above, using PYTHON_CONFIGURE_OPTS environment variable; you may need to clean the Julia environmnet (rm -r ~/.julia/environmnts)

Note that Julia is only needed for code not referenced in the paper.

Other dependencies

1. poetry run omnizart download-checkpoints to download omnizart models
2. Install command fluidsynth in your PATH to test seba method
3. poetry run python -m alignment.seba.align to download soundfont for seba method

Datasets

1. To install datasets from ASMD, run poetry run python -m alignment.asmd.asmd.install and follow instructions
2. At the end of the procedure, you'll be able to download the generated ground-truth from the web; however, you can still regenerate them from scratch by running
   • poetry run python -m alignment.asmd.asmd.generate_ground_truth --normal
   • poetry run python -m alignment.asmd.asmd.generate_ground_truth --misalign
3. If you want to recompute statistics, use the --train flag; note that the result will likely be slightly different because statistics are computed on songs where Eita method takes less than 20 seconds, and this varies based on your machine computational power.

Evaluation

Datasets

To evaluate the artificially misalignment approach, use: poetry run python -m alignment.asmd.asmd.alignment_stats

Note that results will likely be different from ours, because the statistics are computed on songs for which the Eita method takes less than 20 seconds. As such, the statistics depend on your computational power.

The following are the L1 errors between the generated data and the matching notes in the real score in our test-set:

	Ons	Offs
HMM	18.6 ± 49.7	20.7 ± 50.6
Hist	7.43 ± 15.5	8.95 ± 15.5

When not sorting nor fixing offsets, the HMM worked better than Hist, but I only computed DTW normalized distance; results are in the ASMD repo (old commits).

Audio-to-midi

1. To evaluate audio-to-score alignment without missing/extra notes on music without solo piano, use: poetry run python -m alignment.evaluate_audio2score
2. To simulate missing/extra notes use the flag --missing
3. To do the same tests on solo piano music, use the flag --piano
4. You can select ASMD datasets by using option --dataset
5. To do experiments published in the paper in one pass, use poetry run ./evaluate_audio2score.sh

Results are shown in mlflow, so you need to run mlflow ui and access it from your browser.

You can also see results from our evaluations by using mlflow ui.

Finally, you can see further statistics by reading the content of files with .notes extensions.