arXiv:2608. 14916v1 Announce Type: cross Abstract: AI-generated music detectors are commonly evaluated against original songs, but real-world uploads are often remixed, re-encoded, pitch-shifted, or otherwise edited.
PRISM‑Bench is an audio‑centric diagnostic benchmark for text‑to‑audio‑video generation, built from 900 human‑verified samples. It evaluates audio along two axes—audio type (speech, music, sound) and sound‑source visibility (on‑screen vs. off‑screen)—across four perceptual dimensions (audio‑visual coherence, audio quality, audio expressiveness, and prompt following) using 35 fine‑grained criteria. The benchmark employs an enhanced MLLM‑as‑a‑Judge protocol that aligns strongly with human raters, revealing a performance gap between frontier and open‑source T2AV models and highlighting overfitting to perceptual fidelity while struggling with complex grounding and control tasks, especially for music and synchronized on‑screen audio.
arXiv:2608. 14016v1 Announce Type: cross Abstract: Live game commentary is scarce: it exists for professional esports broadcasts and almost nowhere else.
arXiv:2510. 02916v2 Announce Type: replace-cross Abstract: We propose SALSA-V, a multimodal video-to-audio generation model capable of synthesizing highly synchronized, high-fidelity long-form audio from silent video content.
StreamAV-Bench is the first comprehensive benchmark designed for streaming audio‑video generation, addressing the limitations of existing benchmarks that focus on completed sequences. It introduces a unified evaluation framework with a progressive track for instruction adherence and long‑horizon stability, and an interactive track for responsive interaction and state retention. The benchmark includes 32 fine‑grained, expert‑verified evaluation cases and evaluates 13 representative systems, revealing temporal drift in progressive generation and responsiveness bottlenecks in interactive control.
TUTTI is a new pre‑training framework for audio‑to‑score transcription that uses a large, fully synthetic multi‑instrument dataset generated by a symbolic music model. The approach trains a standard Transformer encoder‑decoder on these synthetic audio‑score pairs, producing a stronger foundational representation than single‑instrument training. When fine‑tuned on real datasets, TUTTI surpasses prior methods, achieving state‑of‑the‑art results and demonstrating strong cross‑instrument transferability.
arXiv:2606. 01703v1 Announce Type: cross Abstract: We address the challenge of generating high-fidelity, long-form soundtracks that remain coherent across scene transitions.
The paper introduces Cue2Narrate, a two‑stage pipeline that jointly predicts what visual events to narrate and when to insert the narration in long, untrimmed movie clips. It uses a dual‑head audio‑visual localizer to identify visual cue and narration windows, followed by a LoRA‑adapted vision‑language model that generates concise audio descriptions, trained with a Description Ranking Loss. The authors also present the LongLSMDC benchmark, comprising up to 8‑minute clips, and show that Cue2Narrate outperforms video‑only and audio‑only baselines by 5–12 points in average mAP and improves AD generation over fine‑tuned base VLMs.
Audio Description (AD) provides spoken narration of visual events during dialogue gaps, making movies accessible to visually impaired audiences. The problem requires determining both what (which visua...
Generalizable Audio-to-Score (A2S) transcription is fundamentally constrained by the severe scarcity of high-quality, real-world paired data. Relying solely on existing human-annotated datasets often...
arXiv:2608. 15690v1 Announce Type: cross Abstract: Text-to-audio-video (T2AV) generation models produce a video and its soundtrack from a textual description, but offer no control over whose voice speaks in the output.
The paper evaluates how robust three text‑to‑audio models—MusicGen‑small, MusicGen‑large, and Stable Audio 2.5—are to small changes in prompts that could affect adaptive game soundtracks. Using metrics such as log‑Mel distance, MFCC/chroma‑DTW, and CLAP similarity, the study finds that Stable Audio 2.5 consistently yields the lowest acoustic distances and highest CLAP similarity when prompts are structurally rephrased, while MusicGen‑large performs best under lexical substitutions and intensity shifts. The authors also observe that Stable Audio 2.5 shows the greatest variation in prompt‑to‑audio alignment across different random seeds, highlighting the need for multi‑seed robustness testing in game audio applications.