DDMD: AI-Powered Digital Drug Music Detector
Fuente:
arXiv
Enregistré dans:
| Auteur principal: | |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866914999284269056 |
|---|---|
| author | Gharzouli, Mohamed |
| author_facet | Gharzouli, Mohamed |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_23293 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DDMD: AI-Powered Digital Drug Music Detector Gharzouli, Mohamed Audio and Speech Processing Sound 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. |
| title | DDMD: AI-Powered Digital Drug Music Detector |
| topic | Audio and Speech Processing Sound |
| url | https://arxiv.org/abs/2410.23293 |