multipitch_weaklysup

This is a Pytorch code repository accompanying the following paper:

@inproceedings{KrauseSM23,
  author    = {Michael Krause and Sebastian Strahl and Meinard M{\"u}ller},
  title     = {Weakly Supervised Multi-Pitch Estimation Using Cross-Version Alignment},
  booktitle = {Proceedings of the International Society for Music Information Retrieval Conference ({ISMIR})},
  pages     = {XXX--XXX},
  address   = {Milan, Italy},
  year      = {2023}
}

This repository contains code and trained models for all of the paper's experiments. The dataset used in the paper is partially available: Schubert Winterreise Dataset (SWD). The codebase builds upon the multipitch_mctc repository by Christof Weiß. We further use the CUDA implementation of SoftDTW by Mehran Maghumi.

For details and references, please see the paper.

Getting Started

Installation

cd multipitch_weaklysup
conda env create -f environment.yml
conda activate multipitch_weaklysup

Data Preparation

Obtain the complete Schubert Winterreise Dataset (SWD) and extract it in the data/ subdirectory of this repository.
Precompute inputs and targets:

python data_prep/01_extract_hcqt_pitch_schubert_winterreise.py
python data_prep/02_extract_cqt_target_schubert_winterreise.py

Extract data/Schubert_Winterreise/audio_audio_sync.zip in that same directory.

After precomputation, your data directory should contain at least the following:

├── data
    └── Schubert_Winterreise
        ├── 01_RawData
        │   └── audio_wav
        ├── 02_Annotations
        │   ├── ann_audio_note
        │   └── ann_audio_globalkey.csv
        ├── audio_audio_sync
        ├── cqt_hs512
        ├── hcqt_hs512_o6_h5_s1
        └── pitch_hs512_nooverl

Here, 01_RawData and 02_Annotations originate from the SWD. audio_audio_sync contains alignment paths between different versions of a song (computed using audio-audio synchronization and provided as part of this repository). hcqt_hs512_o6_h5_s1 contains precomputed HCQT representations used as network input (denoted InputRep in the paper). pitch_hs512_nooverl contains aligned pitch annotations (required for evaluation). cqt_hs512 contains the CQT representations used as alignment targets in the proposed approach (denoted TargetRep in the paper).

Experiments

In the experiments folder, all scripts for experiments from the paper can be found. The subfolder models contains trained models for all these experiments, and corresponding log files and the filewise results are also provided. Please note that re-training requires a GPU as well as the pre-processed training data (see Data Preparation).

Run scripts using, e.g., the following commands:

export CUDA_VISIBLE_DEVICES=0
python experiments/07_schubert_cva_ov_b.py
python experiments/schubert_tune_threshold.py -exp 07_schubert_cva_ov_b

The schubert_tune_threshold.py script is required for finding the optimal detection threshold (denoted \tau^* in the paper) for an experiment.

Setup / Training / Evaluation

All experiments are configured in the respective scripts. The following options are most important to our experiments:

label_type: which data to use as optimization target
- 'pitch_aligned': strong pitch annotations (binary, frame-wise aligned; used for the Sup experiment in our paper)
- 'cqt_same_version': magnitude CQT representation of the input excerpt (real-valued, frame-wise aligned; used for the AE experiment in our paper)
- 'cqt_other_version': magnitude CQT representation of another version than the input excerpt (real-valued, boundaries of segments aligned, #frames typically different)
- 'cqt_other_version_stretched': like 'cqt_other_version', but rescaled to the #frames of the input (used for the CVA experiments in our paper)
apply_overtone_model: whether to apply the overtone model (denoted +Ov in the paper)
add_bias: bias added after overtone model (set to 0.0 to disable; denoted +B in the paper)

The steps which should performed are configured by the flags do_train, do_val, do_test. Note that if threshold tuning should be applied, store_predictions must be set to True.

Evaluation On Other Data

In preprocessing_prediction_evaluation.ipynb, we demonstrate how to preprocess one's own audio files, load a trained model and how to predict pitches and evaluate the estimates.