用机器学习区分致幻音乐与普通音乐，准确率达93%。
DDMD: AI-Powered Digital Drug Music Detector
- 基于MFCC、音高、频谱等特征，用随机森林分类
- 在3176段音频上实现93%分类准确率
- 提供网页工具，供公众检测致幻音乐
我们提出首个数字致幻音乐检测器DDMD，一种二分类模型，用于区分数字致幻音乐与普通音乐。现有研究多关注致幻音乐的心理、神经或社会影响，但尚未在音乐信息检索（MIR）领域系统探讨其分类。本研究首次构建了包含3,176段音频的数据集，分为1,676段致幻音乐和1,500段非致幻音乐。提取了包括梅尔频率倒谱系数（MFCC）、音高向量（chroma）、频谱对比度及频率均值与标准差在内的多种特征，采用随机森林分类器，达到93%的分类准确率。最终开发了网页应用部署该模型，支持用户实时检测数字致幻音乐。
原文摘要 · Abstract (English)
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.
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