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

Data Source