SilverAvocado/Silver-Audio-Dataset

该数据集是一个包含从原始音频记录中提取的MFCC特征的`.npy`文件的精选集合，专门设计用于训练和评估机器学习模型，特别是在现实世界中的紧急声音检测和分类任务中。数据集捕捉了多样化的音频场景，使其成为开发以安全为重点的AI系统（如`SilverAssistant`项目）的强大资源。数据集包括多种现实场景，如暴力/犯罪活动、跌倒、求助请求和日常室内声音。音频样本来自AI Hub等数据集，确保覆盖多样化的场景。MFCC特征提取过程包括音频收集、MFCC提取和输出格式处理，最终保存为`.npy`文件。

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. The dataset includes various real-world scenarios, such as violent/criminal activities, falls, cries for help, and normal indoor sounds. The feature extraction process involves audio collection, MFCC extraction, output formatting, and saving the processed MFCC arrays as `.npy` files.