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The First Cadenza Challenge (CAD1) - Task 1: Listening music via headphones

Cadenza challenge code for the First Cadenza Challenge (CAD1) Task1.

For more information please visit the challenge website.

1. Data structure

The First Cadenza Challenge - task 1 is using the MUSDB18-HQ dataset. The data is split into train, validation and test following the same split from museval. I.e., 86 songs are for training, 16 for validation and 50 for evaluation.

To download the data, please visit here. The data is split into cadenza_cad1_task1_core_musdb18hq.tar.gz (containing the MUSDB18-HQ dataset) and cadenza_cad1_task1_core_metadata.tar.gz (containing the list of songs and listeners' characteristics per split). Alternatively, you can download the MUSDB18-HQ dataset from the official SigSep website. If you opt for this alternative, be sure to download the uncompressed wav version. Note that you will need both packages to run the baseline system.

If you need additional music data for training your model, please restrict to the use of MedleyDB [4] [5], BACH10 [6] and FMA-small [7]. Theses are shared as cadenza_cad1_task1_augmentation_medleydb.tar.gz, cadenza_cad1_task1_augmentation_bach10.tar.gz and cadenza_cad1_task1_augmentation_fma_small.tar.gz. Keeping the augmentation data restricted to these datasets will ensure that the evaluation is fair for all participants.

Unpack packages under the same root directory using

tar -xvzf <PACKAGE_NAME>

1.1 Necessary data

  • Music contains the MUSDB18-HQ music dataset for training, validation and evaluation.
cadenza_data
└───task1
    └───audio
        └───musdb18hq
            ├───train
            └───test
  • Metadata contains the metadata for the systems.
cadenza_data
└───task1
    └───metadata
        └───musdb18hq
            ├───listeners.train.json
            ├───listeners.valid.json
            ├───musdb18.train.json
            ├───musdb18.valid.json
            └───musdb18.test.json

1.2 Additional optional data

  • MedleyDB contains both MedleyDB versions 1 [4] and 2 [5] datasets.

Tracks from the MedleyDB dataset are not included in the evaluation set. However, is your responsibility to exclude any song that may be already contained in the training set.

cadenza_data
└───task1
    └───audio
        └───MedleyDB
            ├───Audio
            └───Metadata
  • BACH10 contains the BACH10 dataset [6].

Tracks from the BACH10 dataset are not included in MUSDB18-HQ and can all be used as training augmentation data.

cadenza_data
└───task1
    └───audio
        └───fma_small
            ├───000
            ├───001
            ├───...
  • FMA Small contains the FMA small subset of the FMA dataset [7].

Tracks from the FMA small dataset are not included in the MUSDB18-HQ. This dataset does not provide independent stems but only the full mix. However, it can be used to train an unsupervised model to better initialise a supervised model.

cadenza_data
└───task1
    └───audio
        └───fma_small
            ├───000
            ├───001
            ├───...

1.3 Demo data

To help you to start with the challenge, we provide a small subset of the data. The demo_data folder contains a single song and two listeners from the validation set.

To use the demo data, simply download the package cadenza_data_demo.tar.xz from here and unpack it under recipes/cad1/task1/, i.e., one level above the baseline directory. Note that the root.path variable in config.yaml is already set to the demo data by default.

To unpack the demo data, run:

tar -xvf cadenza_data_demo.tar.xz

2. Baseline

In the baseline/ folder, we provide code for running the baseline enhancement system and performing the objective evaluation. Note that we use hydra for config handling.

2.1 Enhancement

We offer two baseline systems:

  1. Using the out-of-the-box time-domain Hybrid Demucs [1] source separation model distributed on TorchAudio
  2. Using the out-of-the-box spectrogram-based Open-Unmix source separation model (version umxhq) distributed through PyTorch Hub

Both system use the same enhancement strategy; using the music separation model, the baseline system estimates the VDBO (vocals, drums, bass and others) stems. Then, they apply a simple NAL-R [2] fitting amplification to each of them. These results on eight mono signals (four from the left channel and four from the right channel). Finally, each signal is downsampled to 24000 Hertz, convert to 16bit precision and encoded using the lossless FLAC compression. These eight signal are then used for the objective evaluation (HAAQI).

The baselines also provide a remixing strategy to generate a stereo signal for each listener. This is done by summing the amplified VDBO stems, where each channel (left and right in stereo) is composed of the addition of the corresponding four stems. This stereo remixed signal is then used for subjective evaluation (listening panel).

To run the baseline enhancement system first, make sure that paths.root in config.yaml points to where you have installed the Cadenza data. This parameter defaults to the working directory. You can also define your own path.exp_folder to store the enhanced signals and evaluated results and select what music separation model you want to employ.

Then run:

python enhance.py

Alternatively, you can provide the root variable on the command line, e.g.,

python enhance.py path.root=/full/path/to/my/cadenza_data

To get a full list of the parameters, run:

python enhance.py --help

The folder enhanced_signals will appear in the exp folder.

2.2 Evaluation

The evaluate.py script takes the eight VDBO signals stored in enhanced_signals and computes the HAAQI [3] score. The final score for the sample is the average of the scores of each stem.

To run the evaluation stage, make sure that path.root is set in the config.yaml file and then run

python evaluate.py

A csv file containing the eight HAAQI scores and the combined score will be generated in the path.exp_folder.

To check the HAAQI code, see here.

Please note: you will not get identical HAAQI scores for the same signals if the random seed is not defined. This is due to the random noises generated within HAAQI, but the differences should be sufficiently small. For reproducibility, in the given recipe, the random seed for each signal is set as the last eight digits of the song md5.

3. Results

The overall HAAQI score for each baseline is:

  • Demucs: 0.2592
  • Open-Unmix: 0.2273

4. References

  • [1] Défossez, A. "Hybrid Spectrogram and Waveform Source Separation". Proceedings of the ISMIR 2021 Workshop on Music Source Separation. doi:10.48550/arXiv.2111.03600
  • [2] Byrne, Denis, and Harvey Dillon. "The National Acoustic Laboratories'(NAL) new procedure for selecting the gain and frequency response of a hearing aid." Ear and hearing 7.4 (1986): 257-265. doi:10.1097/00003446-198608000-00007
  • [3] Kates J M, Arehart K H. "The Hearing-Aid Audio Quality Index (HAAQI)". IEEE/ACM transactions on audio, speech, and language processing, 24(2), 354–365. doi:10.1109/TASLP.2015.2507858
  • [4] R. Bittner, J. Salamon, M. Tierney, M. Mauch, C. Cannam and J. P. Bello, "MedleyDB: A Multitrack Dataset for Annotation-Intensive MIR Research", in 15th International Society for Music Information Retrieval Conference, Taipei, Taiwan, Oct. 2014. pdf
  • [5] Rachel M. Bittner, Julia Wilkins, Hanna Yip and Juan P. Bello, "MedleyDB 2.0: New Data and a System for Sustainable Data Collection" Late breaking/demo extended abstract, 17th International Society for Music Information Retrieval (ISMIR) conference, August 2016. pdf
  • [6] Zhiyao Duan, Bryan Pardo and Changshui Zhang, "Multiple fundamental frequency estimation by modeling spectral peaks and non-peak regions," IEEE Trans. Audio Speech Language Process., vol. 18, no. 8, pp. 2121-2133, 2010. doi:10.1109/TASL.2010.2042119
  • [7] Defferrard, M., Benzi, K., Vandergheynst, P., & Bresson, X. (2016). "FMA: A dataset for music analysis". arXiv preprint arXiv:1612.01840. doi:10.48550/arXiv.1612.01840