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Browse files- README.md +43 -0
- pics/mfcc-output.png +3 -0
README.md
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---
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task_categories:
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- feature-extraction
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language:
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- ko
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tags:
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- audio
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- homecam
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- npy
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---
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## Dataset Overview
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- The dataset is a curated collection of `.npy` files containing MFCC features extracted from raw audio recordings.
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- It has been specifically designed for training and evaluating machine learning models in the context of real-world emergency sound detection and classification tasks.
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- The dataset captures diverse audio scenarios, making it a robust resource for developing safety-focused AI systems, such as the `SilverAssistant` project.
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## Dataset Description
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- 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:
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- Criminal activities
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- Violence
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- Falls
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- Cries for help
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- Normal indoor sounds
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- Feature Extraction Process
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1. Audio Collection:
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- Audio samples were sourced from datasets, such as AI Hub, to ensure coverage of diverse scenarios.
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- These include emergency and non-emergency sounds to train the model for accurate classification.
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2. MFCC Extraction:
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- The raw audio signals were processed to extract Mel-Frequency Cepstral Coefficients (MFCC).
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- The MFCC features effectively capture the frequency characteristics of the audio, making them suitable for sound classification tasks.
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3. Output Format:
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- The extracted MFCC features are saved as `13 x n` numpy arrays, where:
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- 13: Represents the number of MFCC coefficients (features).
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- n: Corresponds to the number of frames in the audio segment.
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4. Saved Dataset:
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- The processed `13 x n` MFCC arrays are stored as `.npy` files, which serve as the direct input to the model.
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- Adaptation in `SilverAssistant` project: [HuggingFace SilverAudio Model](https://huggingface.co/SilverAvocado/Silver-Audio)
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## Data Source
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- Source: [AI Hub 위급상황 음성/음향](https://www.aihub.or.kr/aihubdata/data/view.do?currMenu=&topMenu=&aihubDataSe=data&dataSetSn=170)
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pics/mfcc-output.png
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Git LFS Details
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