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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
- 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.
- MFCC Extraction:
- 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.
- The extracted MFCC features are saved as
- Saved Dataset:
- The processed
13 x n
MFCC arrays are stored as.npy
files, which serve as the direct input to the model.
- The processed
- Audio Collection:
Adaptation in
SilverAssistant
project: HuggingFace SilverAudio Model
Data Source
- Source: AI Hub 위급상황 음성/음향
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