# Submission information
submission:

  # Submission label
  # Label is used to index submissions.
  # Generate your label following way to avoid overlapping codes among submissions:
  # [Last name of corresponding author]_[Abbreviation of institute of the corresponding author]_task[task number]_[index number of your submission (1-4)]
  label: Primus_CP-JKU_task2_2

  # Submission name
  # This name will be used in the results tables when space permits.
  name: Best Outlier Exposed ResNet per Machine Type

  # Submission name abbreviated
  # This abbreviated name will be used in the results table when space is tight.
  # Use a maximum of 10 characters.
  abbreviation: OER

  # Authors of the submitted system.
  # Mark authors in the order you want them to appear in submission lists.
  # One of the authors has to be marked as corresponding author, this will be listed next to the submission in the results tables.
  authors:

    # First author
    - lastname: Primus
      firstname: Paul
      email: paul.primus@jku.at                # Contact email address
      corresponding: true                         # Mark true for one of the authors

      # Affiliation information for the author
      affiliation:
        institution: JKU
        department: Computational Perception
        location: Austria, Linz
# System information
system:

  # System description, metadata provided here will be used to do a meta-analysis of the submitted system.
  # Use general level tags, when possible use the tags provided in comments.
  # If information field is not applicable to the system, use "!!null".
  description:

    # Audio input
    # Please specify all sampling rates (comma-separated list).
    # e.g. 16kHz, 22.05kHz, 44.1kHz
    input_sampling_rate: 16kHz

    # Data augmentation methods
    # Please specify all methods used (comma-separated list).
    # e.g. mixup, time stretching, block mixing, pitch shifting, ...
    data_augmentation: !!null

    # Front-end (preprocessing) methods
    # Please specify all methods used (comma-separated list).
    # e.g. HPSS, WPE, NMF, NN filter, RPCA, ...
    front_end: !!null

    # Acoustic representation
    # one or multiple labels, e.g. MFCC, log-mel energies, spectrogram, CQT, raw waveform, ...
    acoustic_features: log-mel energies

    # Embeddings
    # Please specify all embedings used (comma-separated list).
    # one or multiple, e.g. VGGish, OpenL3, ...
    embeddings: !!null

    # Machine learning
    # In case using ensemble methods, please specify all methods used (comma-separated list).
    # e.g. AE, VAE, GAN, GMM, k-means, OCSVM, normalizing flow, CNN, LSTM, random forest, ensemble, ...
    machine_learning_method: CNN

    # Method for aggregating predictions over time
    # Please specify all methods used (comma-separated list).
    # e.g. average, median, maximum, minimum, ...
    aggregation_method: average

    # Ensemble method subsystem count
    # In case ensemble method is not used, mark !!null.
    # e.g. 2, 3, 4, 5, ...
    ensemble_method_subsystem_count: !!null

    # Decision making in ensemble
    # e.g. average, median, maximum, minimum, ...
    decision_making: !!null

    # External data usage method
    # Please specify all usages (comma-separated list).
    # e.g. simulation of anomalous samples, embeddings, pre-trained model, ...
    external_data_usage: !!null

    # Usage of the development dataset
    # Please specify all usages (comma-separated list).
    # e.g. development, pre-training, fine-tuning
    development_data_usage: development

  # System complexity, metadata provided here may be used to evaluate submitted systems from the computational load perspective.
  complexity:

    # Total amount of parameters used in the acoustic model.
    # For neural networks, this information is usually given before training process in the network summary.
    # For other than neural networks, if parameter count information is not directly available, try estimating the count as accurately as possible.
    # In case of ensemble approaches, add up parameters for all subsystems.
    # In case embeddings are used, add up parameter count of the embedding extraction networks and classification network.
    # Use numerical value.
    total_parameters: 12000000

  # List of external datasets used in the submission.
  # Development dataset is used here only as an example, list only external datasets
  external_datasets:

    # Dataset name
    - name: DCASE 2020 Challenge Task 2 Development Dataset

      # Dataset access URL
      url: https://zenodo.org/record/3678171

  # URL to the source code of the system [optional, highly recommended]
  # Reproducibility will be used to evaluate submitted systems.
  source_code: https://gitlab.cp.jku.at/paulp/dcase2020_task2

# System results
results:
  development_dataset:

    # System results for development dataset.
    # Full results are not mandatory, however, they are highly recommended as they are needed for a thorough analysis of the challenge submissions.
    # If you are unable to provide all results, also incomplete results can be reported.

    # Average of AUCs over all Machine IDs [%]
    # No need to round numbers
	fan:
	  averaged_auc: 0.9286317167841518
	  averaged_pauc: 0.8352913487070679
	pump:
	  averaged_auc: 0.9297781495399142
	  averaged_pauc: 0.8722867745313565
	slider:
	  averaged_auc: 0.9894779962546816
	  averaged_pauc: 0.9454464813719693
	ToyCar:
	  averaged_auc: 0.9566950093931226
	  averaged_pauc: 0.8961968600747151
	ToyConveyor:
	  averaged_auc: 0.8526503235962499
	  averaged_pauc: 0.7259891865658302
	valve:
	  averaged_auc: 0.9776656162464985
	  averaged_pauc: 0.9357400855078873