Audio Research Group / Tampere University of Technology
Authors
| Name | ||
|---|---|---|
| Toni Heittola | Baseline system, DCASE Framework, Documentation | toni.heittola@tut.fi, http://www.cs.tut.fi/~heittolt/, https://github.com/toni-heittola |
| Aleksandr Diment | Dataset synthesis (Task 2) | aleksandr.diment@tut.fi, http://www.cs.tut.fi/~diment/ |
| Annamaria Mesaros | Documentation | annamaria.mesaros@tut.fi, http://www.cs.tut.fi/~mesaros/ |
See https://tut-arg.github.io/DCASE2017-baseline-system/ for detailed instruction, manuals and tutorials.
- Clone repository from Github or download latest release.
- Install requirements with command:
pip install -r requirements.txt - Run the application with default settings:
python applications/task1.py
This is the baseline system for the Detection and Classification of Acoustic Scenes and Events 2017 (DCASE2017) challenge tasks.
The baseline system is intended to lower the hurtle to participate the DCASE challenges. It provides an entry-level approach which is simple but relatively close to the state of the art systems to give reasonable performance for all the tasks. High-end performance is left for the challenge participants to find.
In the baseline, one single low-level approach is shared across the tasks by application specific extensions. Main idea of this is to show the parallelism in the tasks settings, and show how easily one can jump between tasks during the system development.
The main approach implemented in the baseline system:
- Acoustic features: Log Mel-band energies extracted in 40ms windows with 20ms hop size.
- Machine learning: neural network approach using multilayer perceptron (MLP) type of network (2 layers with 50 neurons each, and 20% dropout between layers).
.
├── applications # Task specific applications (task1.py, task2.py, and task3.py)
│ └── parameters # Default parameters for the applications
├── dcase_framework # DCASE Framework code
├── docker # Docker container to reproduce baseline results
├── docs # Docs in HTML format
├── documentation # Documentation sources (Sphinx)
├── examples # Examples how to extend the DCASE Framework
├── tests # Unit tests
├── EULA.pdf # End-user license agreement
├── README.md # This file
└── requirements.txt # External module dependencies
The system is developed for Python 2.7 and Python 3.6. The baseline system is tested to work with Linux, Windows and MacOS operating systems.
To ensure that all external modules are installed, run command:
pip install -r requirements.txt
See more detailed instructions from documentation.
- Introduced recognizer classes: SceneRecognizer, EventRecognizer
- Introduced Keras callbacks: ProgressLoggerCallback, ProgressPlotterCallback, StopperCallback, StasherCallback
- Added new learner classes: SceneClassifierKerasSequential, EventDetectorKerasSequential
- Full support for Keras sequential API
- Epoch-by-epoch processing, with external metric evaluation (using sed_eval)
- DataProcessor class to embed feature processing and data processing chains
- Feature generators to allow loading feature data from disk during training procedure
- Added possibility to use constant and simple math equations in the keras based learner parameters
- feature_stacker parameter hash added to the data paths. This broke backward compatibility, previously stored models and results has to be re-calculated or moved to correct paths.
- Added Docker container to reproduce baseline results
- Fixed training data ordering (might have been different on different computer setups)
- Added Windows compatibility
- First public release
The DCASE Framework and the baseline system is released only for academic research under EULA.pdf from Tampere University of Technology.