arXiv:2608. 08349v1 Announce Type: cross Abstract: Audio dramas weave dialogue, sound effects, and music into immersive stories.
arXiv:2607. 09973v1 Announce Type: cross Abstract: Industrial sound design requires audio generation systems that not only produce realistic audio, but also preserve the perceptual identity of a reference, support controllable variation, and remain efficient for practical workflows.
arXiv:2606. 00629v1 Announce Type: cross Abstract: Sound design workflows frequently oscillate between time-consuming library searches and the complexity of procedural synthesis, with practitioners typically relying on disconnected tools to address each challenge separately.
arXiv:2609.14344v1 Announce Type: cross Abstract: Instruction-guided music editors typically process each request independently, limiting their ability to support workflows in which users progressive...
The paper introduces MuseCPEval, the first comprehensive framework for assessing Music Context Preservation (MuseCP) in music editing systems. It defines four categories of music facets and provides fine‑grained metrics to detect subtle changes during editing tasks such as timbre transfer, instrument substitution, and genre transformation. The authors validate the metrics objectively and through a human study, and demonstrate their practical use in evaluating diverse editing systems, offering insights into each system’s strengths and limitations.
AVENUE is a new benchmark and evaluation framework for audio‑video editing that includes 1,291 source clips and 7,957 editing instructions covering audio‑targeted, video‑targeted, and coupled edits. It introduces a sample‑specific, modality‑aware evaluation that specifies the intended change and the content that must remain unchanged. The study applies this framework to joint, sequential, and separate editing models, revealing that existing models often alter unintended modalities, highlighting a key challenge in controllable AV editing.
arXiv:2609.08275v1 Announce Type: new Abstract: Recent multi-shot audio-video generators can produce increasingly coherent and cinematic outputs, but coherence does not imply the ability to execute e...
arXiv:2610.00097v1 Announce Type: new Abstract: Recent diffusion and autoregressive models have substantially improved text-to-video generation, yet producing coherent long-form story videos with con...
arXiv:2607. 24873v1 Announce Type: new Abstract: Recent advances in AI music generation have enabled users to create complete musical pieces from natural-language prompts.
arXiv:2606. 01703v1 Announce Type: cross Abstract: We address the challenge of generating high-fidelity, long-form soundtracks that remain coherent across scene transitions.
MUSE is a story‑generation engine that applies Robert McKee’s narrative theory to guide decisions about plot, character, and language throughout planning, drafting, and revision. It structures story knowledge into rule atoms, semantic consolidations, and mechanisms, and uses intermediate deliverables to preserve decisions across creative stages. Experiments show MUSE improves benchmark scores over zero‑shot generation and maintains low consistency errors across multiple models.
arXiv:2607. 11124v1 Announce Type: cross Abstract: Music creation is fundamentally a process of revision.
arXiv:2608. 03742v1 Announce Type: cross Abstract: Sound effects play a crucial role in conveying actions, events, and environmental cues across digital applications, often requiring a high degree of variation and contextual adaptability.