SONICS: Synthetic Or Not Identifying Counterfeit Songs ICLR 2025 [Poster] 📌 Abstract The recent surge in AI generated songs presents exciting possibilities and challenges. These innovations necessitate the ability to distinguish between human composed and synthetic songs to safeguard artistic integrity and protect human musical artistry. Existing research and datasets in fake song detection only focus on singing voice deepfake detection (SVDD), where the vocals are AI generated but the instrumental music is sourced from real songs. However, these approaches are inadequate for detecting contemporary end to end artificial songs where all components (vocals, music, lyrics, and style) could be AI generated. Additionally, existing datasets lack music lyrics diversity, long duration songs, and open access fake songs. To address these gaps, we introduce SONICS, a novel dataset for end to end Synthetic Song Detection (SSD), comprising over 97k songs (4,751 hours) with over 49k synthetic songs from popular platforms like Suno and Udio. Furthermore, we highlight the importance of modeling long range temporal dependencies in songs for effective authenticity detection, an aspect entirely over…
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