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pipeline_tag: audio-classification |
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This repository contains the models submitted to Task 1 of the [DCASE 2024 Challenge](https://dcase.community/challenge2024/) |
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## Description |
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The task is to develop a data-efficient and low-complexity acoustic scene classification system. |
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The challenge dataset consists of 1 second audio clips from one of 10 classes: `airport`, `bus`, `metro`, `metro_station`, `park`, `public_square`, `shopping_mall`, `street_pedestrian`, `street_traffic`, `tram`. Five models are trained on splits of the training data: 5%, 10%, 25%, 50%, and 100%, respectively. |
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We chose to use the baseline model architecture and apply a target-specific training process which involves a pretraining dataset that is pruned to match the target dataset. Knowledge distillation is used to transfer knowledge from a pre-trained audio tagging ensemble to the target model. |
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A technical report describing the training process can be found [here](https://dcase.community/documents/challenge2024/technical_reports/DCASE2024_Werning_48_t1.pdf) |
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## Results |
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The full results of all participants can be found here: |
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https://dcase.community/challenge2024/task-data-efficient-low-complexity-acoustic-scene-classification-results |
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The results of our submission compared to the baseline on the evaluation data are as follows: |
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| Name | Official rank | Rank score | Split 5% | Split 10% | Split 25% | Split 50% | Split 100% | |
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| Werning_UPBNT | 8 | 54.35 | 49.21 % | 52.51 % | 55.49 % | 56.20 % | 58.34 % | |
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| Baseline | 17 | 50.73 | 44.00 % | 46.95 % | 51.47 % | 54.40 % | 56.84 % | |
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## Usage |
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The example notebook shows how to predict the acoustic scene for a given audio file using the models. |
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The model code is adapted from the baseline repository: https://github.com/CPJKU/dcase2024_task1_baseline |
