DDMD: AI-Powered Digital Drug Music Detector

TL;DR

This paper introduces DDMD, a machine learning-based tool that accurately detects digital drug music from regular music, with potential applications in music analysis and safety.

Contribution

The study presents the first classifier specifically designed to identify digital drug music, utilizing a novel dataset and achieving high accuracy with a Random Forest model.

Findings

Achieved 93% accuracy in classifying digital drug music.

Developed a web application for practical deployment.

Created a dataset of over 3,000 audio files for this task.

Abstract

We present the first version of DDMD (Digital Drug Music Detector), a binary classifier that distinguishes digital drug music from normal music. In the literature, digital drug music is primarily explored regarding its psychological, neurological, or social impact. However, despite numerous studies on using machine learning in Music Information Retrieval (MIR), including music genre classification, digital drug music has not been considered in this field. In this study, we initially collected a dataset of 3,176 audio files divided into two classes (1,676 digital drugs and 1,500 non-digital drugs). We extracted machine learning features, including MFCCs, chroma, spectral contrast, and frequency analysis metrics (mean and standard deviation of detected frequencies). Using a Random Forest classifier, we achieved an accuracy of 93%. Finally, we developed a web application to deploy the model, enabling end users to detect digital drug music.