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Datasets:
b111111
/
Silver-Audio-Dataset

Tasks:
Feature Extraction
Modalities:
Audio
Languages:
Korean
Size:
100M<n<1B
Tags:
audio
homecam
numpy
Dataset card Files
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b111111 SeoyeonPark1223 commited on
Commit
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Duplicate from SilverAvocado/Silver-Audio-Dataset

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Co-authored-by: Seoyeon Park <SeoyeonPark1223@users.noreply.huggingface.co>

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+ *.arrow filter=lfs diff=lfs merge=lfs -text
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+ *.ckpt filter=lfs diff=lfs merge=lfs -text
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+ *.gz filter=lfs diff=lfs merge=lfs -text
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+ *.h5 filter=lfs diff=lfs merge=lfs -text
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+ *.joblib filter=lfs diff=lfs merge=lfs -text
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+ *.lfs.* filter=lfs diff=lfs merge=lfs -text
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+ *.lz4 filter=lfs diff=lfs merge=lfs -text
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+ *.mds filter=lfs diff=lfs merge=lfs -text
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+ *.mlmodel filter=lfs diff=lfs merge=lfs -text
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+ *.pickle filter=lfs diff=lfs merge=lfs -text
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+ *.pkl filter=lfs diff=lfs merge=lfs -text
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+ *.pt filter=lfs diff=lfs merge=lfs -text
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+ *.pth filter=lfs diff=lfs merge=lfs -text
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+ *.rar filter=lfs diff=lfs merge=lfs -text
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+ *.safetensors filter=lfs diff=lfs merge=lfs -text
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+ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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+ *.tar.* filter=lfs diff=lfs merge=lfs -text
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+ *.tar filter=lfs diff=lfs merge=lfs -text
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+ *.tflite filter=lfs diff=lfs merge=lfs -text
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+ *.zst filter=lfs diff=lfs merge=lfs -text
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+ *tfevents* filter=lfs diff=lfs merge=lfs -text
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+ # Audio files - uncompressed
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+ *.pcm filter=lfs diff=lfs merge=lfs -text
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+ *.sam filter=lfs diff=lfs merge=lfs -text
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+ *.raw filter=lfs diff=lfs merge=lfs -text
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+ # Audio files - compressed
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+ # Image files - compressed
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+ *.jpeg filter=lfs diff=lfs merge=lfs -text
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+ *.webp filter=lfs diff=lfs merge=lfs -text
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+ # Video files - compressed
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+ *.mp4 filter=lfs diff=lfs merge=lfs -text
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+ *.webm filter=lfs diff=lfs merge=lfs -text
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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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+ - numpy
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+ viewer: false
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+ size_categories:
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+ - 100M<n<1B
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+ ---
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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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+
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+ ## Dataset Descriptions
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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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+ - `violent_crime`: Violence / Criminal activities (폭력/범죄)
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+ - `fall`: Fall down (낙상)
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+ - `help_request`: Cries for help (도움 요청)
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+ - `daily-1`, `daily-2`: Normal indoor sounds (일상)
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+
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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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+ ![MFCC Output](./pics/mfcc-output.png)
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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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+
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+ - Adaptation in `SilverAssistant` project: [HuggingFace SilverAudio Model](https://huggingface.co/SilverAvocado/Silver-Audio)
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+
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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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