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Rules

1. Teams​

  • Teams must pre-register and nominate a contact person.
  • Teams can be from one or more institution.
  • Anonymous entries are allowed.
  • You must provide a technical document of up to 2 pages describing the system/model, what data and pre-existing tools, software and models used.
  • We will publish all technical documents (anonymous or otherwise).
  • You are encouraged to make your code open source so others can build on your work.

2. What information can I use?​

2.1. Training and development​

  • There is no limit on the amount of training data that can be generated using our tools.
  • Each task lists what data can be used for developing the systems:
  • You can use standard augmentation techniques such as: randomizing the stems, flipping right and left channels, applying SpecAugmentation [1], pitch shifting, etc.
    • If you are unclear whether an augmentation technique is acceptable, please contact the Cadenza team.
  • You may not use other datasets without prior approval by the Cadenza team.
    • If you think there is a public dataset that would be a good addition to the challenge then please propose it to the Cadenza team. If we agree, we will then make it available to all teams.
  • Pretrained models
    • These can only have been developed with public databases.
    • You must not use pretrained models developed on private datasets.
    • You must not use pretrained models that included any of the CAD2 evaluation data in their training.
  • All the audio or metadata can be used during training and development.
  • You must not use the evaluation data set for training or tuning the system.

2.2. Evaluation​

The only data that can be used during evaluation are:

  • The audiograms giving the listener characterisation for personalisation.
  • The stereo music input signals.
  • For rebalancing classical music the target gains.
  • For lyric intelligibility the 𝛼 value that gives the balance between intelligibility and quality during evaluation.

3. Ranking​

If a system has broken the rules they will not appear in the official rankings.

  • Objective: entries will be ranked according to average score across all signals in the evaluation dataset.
  • Listening panel: entries will be ranked according to average score across all evaluation signals auditioned in the listening tests.

We will also report whether the systems are causal or non-causal in the rank order table and model size.

4. Prizes​

We will be awarding prizes for the best systems in our rankings:

  • Objective score for lyric intelligibility
  • Listening panel score for lyric intelligibility
  • Objective score for rebalancing classical music

A prize pot of $1000 has been made available by the generosity of the IEEE SPS.

Anonymous entries and those with direct links to the Cadenza project team are ineligible for cash prizes, sorry.

5. Computational restrictions​

  • Systems must either be:
    • causal and low latency to allow them to work with live music, or
    • non-causal, for use with recorded music.
  • The latency restrictions for causal entries are that the output from the hearing aid at time t must not use any information from input samples more than 5 ms into the future i.e., no information from input samples >t+5 ms. See this blog post from our sister Clarity project for more.
  • There is no limit on computational cost, but entrants must report model size.
  • Teams must start with the baseline, with the blocks that can be changed labelled Enhancement
  • While we have provided metrics for evaluation, other metrics and approaches can be used by the teams during training.

6. Submitting multiple entries for a task​

This will be allowed in two circumstances:

  • If very different approaches are used.
  • For the lyric intelligibility, you could submit a system tuned for objective evaluation and another tuned for the listening panel.

7. Intellectual property​

The following terms apply to participation in this machine learning challenge (“Challenge”). Entrants may create original solutions, prototypes, datasets, scripts, or other content, materials, discoveries or inventions. Entrants retain ownership of all intellectual and industrial property rights (including moral rights) in and to these.

The submission constitutes the audio files submitted to the challenge and the accompanying technical report.

The Challenge Organiser is the Cadenza Project team.

As a condition of submission, Entrant grants the Challenge Organiser, its subsidiaries, agents and partner companies, a perpetual, irrevocable, worldwide, royalty-free, and non-exclusive licence to use, reproduce, adapt, modify, publish, distribute, publicly perform, create a derivative work from, and publicly display the submission.

Entrants provide Submissions on an “AS IS” BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied, including, without limitation, any warranties or conditions of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A PARTICULAR PURPOSE.

References​

[1] Park, D.S., Zhang, Y., Chiu, C.C., Chen, Y., Li, B., Chan, W., Le, Q.V. and Wu, Y., 2020, May. Specaugment on large scale datasets. In ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 6879-6883). IEEE.