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Evaluation metrics for machine-composed symbolic music. Paper: "The Jazz Transformer on the Front Line: Exploring the Shortcomings of AI-Composed Music through Quantitative Measures", ISMIR 2020

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MusDr.

Evaluation metrics for machine-composed symbolic music.

Python implementation of the evaluation metrics proposed in Section 5 of our paper:

  • The Jazz Transformer on the Front Line: Exploring the Shortcomings of AI-composed Music through Quantitative Measures
    Shih-Lun Wu and Yi-Hsuan Yang
    The 21st International Society for Music Information Retrieval Conference (ISMIR), 2020.

Metrics

Computed from Symbolic Music

The supported input format is event token sequences that can be mapped to MIDIs, rather than general MIDIs. See this paper (Huang and Yang, 2020) for a thorough introduction.

  • Pitch-Class Histogram Entropy (H)
    -- measures erraticity of pitch usage in shorter timescales (e.g., 1 or 4 bars)
  • Grooving Pattern Similarity (GS)
    -- measures consistency of rhythm across the entire piece
  • Chord Progression Irregularity (CPI)
    -- measures consistency of harmony across the entire piece

Computed from Audio (and the Resulting "Fitness Scape Plot")

  • Structureness Indicator (SI)
    -- detects presence of repeated structures within a specified range of timescale

Directory Structure

  • initPaths.m: for MATLAB (SM Toolbox) imports
  • sm_compute_scapeplot.m: MATLAB script for computing SSMs and scape plots
  • run_matlab_scapeplot.py: Python program that invokes the MATLAB script above
  • run_python_scapeplot.py: Python-native equivalent of run_matlab_scapeplot.py
  • vis_scapeplot.py: visualizes the computed scape plots
  • run_all_metrics.py: runs all evaluation metrics and outputs results
  • musdr/
    • testdata/: contains example testdata
    • eval_metrics.py: contains the implementation of the 4 metrics mentioned above
    • side_utils.py: some I/O and computation utilities
  • mueller_audio_tools/: contains the Python equivalents of the required utilities in SM Toolbox, retrieved from FMP Notebooks

Usage Notes

Prerequisites

NOTE: all of the following commands run on the example testdata under musdr/testdata/

Computation of Fitness Scape Plots

-- required for metric SI

Run with MATLAB

  • More tedious setup, but runs faster

  • Get MATLAB (if you don't have one)

  • Download the "SM Toolbox" (Müller et. al, 2013) for scape plot computation

    • URL: https://www.audiolabs-erlangen.de/resources/MIR/SMtoolbox/
    • Unzip it (directly, not into a new directory) under the repo root directory
    • Revise the file MATLAB_SM-Toolbox_1.0/MATLAB-Chroma-Toolbox_2.0/wav_to_audio.m (line 107, 108)
      -- due to compatibility issues with newer MATLAB versions
      • Remove
         if strcmp(ext,'.wav')
            [f_audio,fs,nbits] = wavread(strcat(dirAbs,dirRel,wavfilename));
      • Add
         if ~( strcmp(ext,'.mp3') && strcmp(ext, '.wav') )
            [f_audio,fs] = audioread(strcat(dirAbs,dirRel,wavfilename));
            nbits = 24;
  • Run the Python script that invokes the MATLAB function to compute scape plots

python3 run_matlab_scapeplot.py \
   -a musdr/testdata/audio  \
   -s musdr/testdata/ssm   \
   -p musdr/testdata/scplot  \
   -j 1
  • -a: input audio directory
  • -s: output directory for SSMs
  • -p: output directory for scape plots
  • -j: number of processes spawned for scape plot computation (optional, defaults to 1; 2~4 is recommended)

Run with Python

  • No additional setup required, but runs slowly on longer songs
python3 run_python_scapeplot.py \
   -a musdr/testdata/audio  \
   -s musdr/testdata/ssm   \
   -p musdr/testdata/scplot  \
   -j 1
  • -a: input audio directory
  • -s: output directory for SSMs
  • -p: output directory for scape plots
  • -j: number of processes spawned for scape plot computation (optional, defaults to 1; 2~4 is recommended)

Visualize the Scape Plots

  • Works on scape plots in both .mat and .npy formats
python3 vis_scapeplot.py \
   -p musdr/testdata/scplot  \ # input scape plots directory
   -f musdr/testdata/scfig     # scape plot figures output directory

Run All Evaluation Metrics and Get the Report

  • Run run_all_metrics.py when you have computed the fitness scape plots
python3 run_all_metrics.py \
   -s musdr/testdata/symbolic  \
   -p musdr/testdata/scplot  \
   -o testout.csv \
   --timescale_bounds 3 8 15
  • -s: input symbolic music directory
  • -p: input scape plots directory (having the same pieces as the symbolic music directory)
  • -o: path to output file for results
  • --timescale_bounds: timescale bounds for short-, mid-, and long-term SI metric (optional, defaults to 3 8 15)

Release Notes

  • July 8th, 2020
    • The metrics H, GS, and CPI now only natively support event-based representations defined in the Jazz Transformer
      -- we welcome contributions to extend the compatibility to other representations, or even general MIDIs
    • Metric SI now relies on MATLAB and audio
      -- planning to re-implement it with Python-native package librosa
      -- considering adding an option to take pianorolls (obtained from MIDIs) as inputs
  • July 22nd, 2020
    • Add Python support for SSM and fitness scape plot computation

Acknowledgements

This repository makes use of the following open-source utilities:

  • MATLAB SM Toolbox
  • Python FMP Notebooks
    • FMP Notebooks: Educational Material for Teaching and Learning Fundamentals of Music Processing
      Meinard Müller and Frank Zalkow
      In Proceedings of the 20th International Society for Music Information Retrieval Conference (ISMIR), 2019.
    • URL: https://www.audiolabs-erlangen.de/resources/MIR/FMP/C0/C0.html
    • Special thanks to Wen-Yi Hsiao (@ Taiwan AILabs, personal GitHub) for retrieving the required functions for this repository

See Also

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Evaluation metrics for machine-composed symbolic music. Paper: "The Jazz Transformer on the Front Line: Exploring the Shortcomings of AI-Composed Music through Quantitative Measures", ISMIR 2020

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