Sripathi Sridhar, Yujin Kim, Giovana Morais, Liang-Yuan Wu, Mehmet Atilay Kucukoglu, Prem Seetharaman, Lloyd May, Mark Cartwright 等 9 位作者
Audio captioning benchmarks typically evaluate a single unstructured free-text description of an audio scene, providing limited insight into how a model fails to understand polyphonic audio. A plausible caption may omit a source, or conflate multiple sources into one, or assign an attribute of one source to another, and conventional captioning evaluation cannot locate these errors. We introduce MixCardBench, a benchmark for structured captioning of polyphonic audio, which evaluates models in two stages: first, identifying and temporally localizing the sources in a scene, and second, generating a structured MixCard describing the semantic, temporal, and acoustic properties of each source. The benchmark contains 4884 ecologically plausible synthetic soundscapes with up to nine overlapping speech, music and sound effects sources built from high-quality recordings and source-level annotations, where each source has a reference MixCard. We evaluate six state-of-the-art large audio language models and find substantial limitations in current models, which struggle considerably with speech and sound effects timing and detection, with the strongest model reaching only around 60% mean card-field score. Performance varies significantly across attributes, exposing capability differences that are hidden by aggregate caption scores. We assess the quality and ecological validity of MixCardBench through human validation, distribution matching and a listening study, and show that human corrections have little effect on model rankings, and our synthesized soundscapes are closer to in-the-wild YouTube videos than source guided but otherwise random synthesis. The benchmark dataset and code are publicly available, and demo examples are at https://mixcards-anon.github.io/mixcards-demo/
William Chen, Prem Seetharaman, Ke Chen, Oriol Nieto, Kevin Duarte, Siddharth Srinivasan Iyer, Mamshad Nayeem Rizve, Zhiwen Cao 等 13 位作者
Multimedia editing requires generative models to interpret complex, compositional instructions and modify only the desired elements across modalities, a capability largely beyond existing systems. Current approaches are typically limited to simple, single-attribute edits within one modality, since diverse, high-quality editing pairs are hard to obtain, especially for cross-modal tasks where aligned audiovisual (AV) data is scarce. We present CrossEdit, a unified omni-modal editing model for images, audio, and video that performs AV movie scene edits zero-shot through cross-modal transfer. Our key observation is that complex instruction-following, once learned in any modality, generalizes to others. We exploit the additive nature of acoustic signals to procedurally generate large-scale audio editing pairs with compositional instructions, and show that fine-tuning on this synthetic data and self-supervised AV masked reconstruction, alongside a targeted set of curated cross-modal tasks, induces instruction-following that transfers zero-shot to unseen modality and instruction combinations. To evaluate this capability, we release CrossEditBench, a human-annotated benchmark of AV edits on movie scenes, and propose AV-FES, a metric that jointly scores instruction following and consistency. We show that our proposed techniques improve performance on zero-shot AV movie scene editing, while maintaining or improving performance on lip-synced speech editing and standard image, video, and audio editing benchmarks. Demos are available at https://wanchichen.github.io/crossedit/.
Swapnil Bhosale, Prem Seetharaman, Ke Chen, Haonan Chen, Justin Salamon, Zeyu Jin
Real-world audio applications require precise, queryable representations grounded in physical time. Yet modern Large Audio-Language Models (LALMs) increasingly favor conversational generation, producing free-form descriptions that conflate acoustic perception with higher-level reasoning and lack precise temporal grounding. We introduce TAC2, a foundation model that casts timestamped audio captioning as composable functions to construct granular, speaker-attributed acoustic timelines directly from in-the-wild audio. To avoid prohibitively expensive manual annotation, we programmatically fuse complementary predictions from specialized weak teachers through time-registered IoU matching. We model the resulting multi-granularity schema of timestamped events, speaker turns, and fine-grained speech attributes using an Instruction-Input-Output (IIO) completion format, in which each task is a function of the audio, an instruction, and an optional input. A single call decodes the full description autonomously, while composing calls lets earlier outputs or external priors, such as a reference transcript, condition later ones. We further extend TAC2 with expressive speech attributes to obtain TAC2-S, using parameter-efficient specialists to elicit latent paralinguistic capabilities from the pre-trained backbone and distill them into a unified model. We validate the resulting representation in two complementary settings. Paired with frozen text-only LLMs, it yields strong spoken audio understanding across diverse reasoning backends, demonstrating the utility of decoupling fine-grained acoustic perception from downstream reasoning. Used as paired supervision for text-to-audio generation, it supports semantically and temporally grounded reconstruction, together with explicit zero-shot control over event timing, speaker attributes, and word-level emphasis.