We lifted 2 functions out of this paper's own repositories and ran 1 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| OptimusPrimus/dcase2019_task1b | pwc_unofficial | 1 of 2 |
| Function | Status | Where it lives |
|---|---|---|
| calc_padding | Ran | OptimusPrimus/dcase2019_task1b/models/CP_ResNet.py code served (permissive licence) · get_code("64cc06029bdc345d") |
| load_class | Not yet run | OptimusPrimus/dcase2019_task1b/utils/common.py code served (permissive licence) · get_code("852fd2dad64fcab4") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
This paper introduces the acoustic scene classification task of DCASE 2018 Challenge and the TUT Urban Acoustic Scenes 2018 dataset provided for the task, and evaluates the performance of a baseline system in the task. As in previous years of the challenge, the task is defined for classification of short audio samples into one of predefined acoustic scene classes, using a supervised, closed-set classification setup. The newly recorded TUT Urban Acoustic Scenes 2018 dataset consists of ten different acoustic scenes and was recorded in six large European cities, therefore it has a higher acoustic variability than the previous datasets used for this task, and in addition to high-quality binaural recordings, it also includes data recorded with mobile devices. We also present the baseline system consisting of a convolutional neural network and its performance in the subtasks using the recommended cross-validation setup.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1807.09840")
get_code_for_paper("1807.09840")
have("1807.09840")
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