This topic covers AI-driven music and audio generation tools that combine creative synthesis with built-in provenance and copyright features—watermarks, metadata tags, or detectable signatures—to make synthetic tracks and voices traceable and easier to manage. Interest in these capabilities has grown as generative models (from research-grade systems to consumer studios) move into production workflows and platforms seek ways to balance creative use with rights protection and content safety. Key categories include AI Music Creation Tools (text- or voice-conditioned song generators and mastering chains) and Voice Synthesis & Transcription (voice cloning, dubbing, transcription and spoken-word production). Representative tools: Musci — an AI music studio for turning ideas into multi-track songs; ACE‑Step — a fast, open-source diffusion + transformer approach for coherent, full-song generation; Podcastle — an all‑in‑one spoken-word studio for recording, editing, cloning voices and automatic transcripts; and MasteringBOX — an automated AI mastering service for finishing tracks. At the model level, large systems such as DeepMind’s Lyria 3 and Google’s Gemini Music (and other large music models) exemplify the high-fidelity generation side that publishers and platforms are pairing with watermarking solutions. Watermarking schemes (e.g., SynthID‑style detectable signatures or embedded metadata) are now a central consideration: they help content owners and platforms identify synthetic audio, support licensing workflows, and reduce misuse, but they bring trade-offs in robustness, interoperability and audio quality. Evaluations for producers and platforms should therefore consider generation quality, watermark detectability and persistence after editing, metadata standards, and integration with rights and moderation systems.