README.md

metadata

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:

  • 폭력/범죄: Violence / Criminal activities
  • 낙상: Fall down
  • 도움 요청: Cries for help
  • 일상: Normal indoor sounds

• 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

• Source: AI Hub 위급상황 음성/음향