| --- |
| task_categories: |
| - feature-extraction |
| language: |
| - ko |
| tags: |
| - audio |
| - homecam |
| - numpy |
| viewer: false |
| size_categories: |
| - 100M<n<1B |
| --- |
| |
| ## Dataset Overview |
| - The dataset is a curated collection of `.npy` files containing MFCC features extracted from raw audio recordings. |
| - It has been specifically designed for training and evaluating machine learning models in the context of real-world emergency sound detection and classification tasks. |
| - The dataset captures diverse audio scenarios, making it a robust resource for developing safety-focused AI systems, such as the `SilverAssistant` project. |
|
|
| ## Dataset Descriptions |
| - The dataset used for this audio model consists of `.npy` files containing MFCC features extracted from raw audio recordings. These recordings include various real-world scenarios, such as: |
| - `violent_crime`: Violence / Criminal activities (폭력/범죄) |
| - `fall`: Fall down (낙상) |
| - `help_request`: Cries for help (도움 요청) |
| - `daily-1`, `daily-2`: Normal indoor sounds (일상) |
|
|
| - Feature Extraction Process |
| 1. Audio Collection: |
| - Audio samples were sourced from datasets, such as AI Hub, to ensure coverage of diverse scenarios. |
| - These include emergency and non-emergency sounds to train the model for accurate classification. |
| 2. MFCC Extraction: |
| - The raw audio signals were processed to extract Mel-Frequency Cepstral Coefficients (MFCC). |
| - The MFCC features effectively capture the frequency characteristics of the audio, making them suitable for sound classification tasks. |
|  |
| 3. Output Format: |
| - The extracted MFCC features are saved as `13 x n` numpy arrays, where: |
| - 13: Represents the number of MFCC coefficients (features). |
| - n: Corresponds to the number of frames in the audio segment. |
| 4. Saved Dataset: |
| - The processed `13 x n` MFCC arrays are stored as `.npy` files, which serve as the direct input to the model. |
| |
| - Adaptation in `SilverAssistant` project: [HuggingFace SilverAudio Model](https://huggingface.co/SilverAvocado/Silver-Audio) |
|
|
| ## Data Source |
| - Source: [AI Hub 위급상황 음성/음향](https://www.aihub.or.kr/aihubdata/data/view.do?currMenu=&topMenu=&aihubDataSe=data&dataSetSn=170) |
