Gunshot Audio Spectrogram Dataset for Binary Classification Using FFT, LogMel, and MFCC Features

This dataset comprises 15,962 labeled audio samples organized into two classes: Gunshot (5,614 instances) and Non-Gunshot (10,348 instances). The data were collected from multiple public repositories, including UrbanSound8k, ESC-50, Gunshot/Gunfire Audio Dataset, Gunshot Audio Dataset, and Gunshot Audio Forensics Dataset. All audio files were pre-processed using the Librosa library: audio durations were standardized to 5 seconds through trimming or zero-padding, and a pre-emphasis filter was applied to enhance high-frequency components. Feature extraction was performed using three established techniques—Fast Fourier Transform (FFT), Mel-Frequency Cepstral Coefficients (MFCC), and Log-Mel Spectrograms—resulting in a spectrogram-based dataset suitable for training and evaluating machine learning models for gunshot detection tasks.