Audio Commonality Captioning (ACC)

• ACC is an audio-to-language task that produces a single caption by extracting the shared semantic content from multiple audio clips.
• It is designed to align multi-audio representations by avoiding the semantic shifts seen in difference-based supervision methods.
• ACC leverages multimodal large language models with specialized training regimes to preserve original task capabilities while enhancing audio-text alignment.

Searching arXiv for papers on Audio Commonality Captioning and closely related audio captioning work. Audio Commonality Captioning (ACC) is an audio-to-language task in which multiple audio clips are jointly processed to produce a single caption describing the semantics they share, rather than the semantics that distinguish them. In current literature, ACC is presented as a multi-audio captioning objective for multimodal LLMs (MLLMs), motivated by the need to strengthen audio-text alignment without inducing the semantic shift associated with difference-focused supervision. Within the broader lineage of automated audio captioning, ACC can also be viewed as an explicit operationalization of “commonality” across clips: instead of captioning one clip holistically or contrasting two clips by their deltas, it asks for the invariant semantic core that remains stable across inputs (Jia et al., 3 Aug 2025).

1. Conceptual position within audio captioning

Automated Audio Captioning (AAC) has been defined as “a cross-modal translation task of generating a natural language description for an audio clip,” typically formulated as conditional language modeling over an audio representation and a token prefix (Liu et al., 2021). In MLLMs, standard Audio Captioning (AC) is an Audio-to-Language task in which a model maps an input audio clip to a long-form caption that reflects the content and style of the audio, and it functions simultaneously as a pretraining objective, a finetuning objective, and a benchmark for audio-text cross-modal understanding (Jia et al., 3 Aug 2025).

ACC emerged in direct contrast to Audio Difference Captioning (ADC). ADC was introduced to address a common AC failure mode: acoustically similar clips often receive nearly identical captions even when they differ in subtle but semantically important ways. ADC therefore changes both the input and the target: the input is multiple audios, most commonly a pair, and the output is a short text describing only their differences. In the audio-editing construction used in the ACC literature, ADC captions are edit instructions such as “add a burst of bird song,” derived from synthetic add, delete, and replace operations over base and event clips (Jia et al., 3 Aug 2025).

The central critique of ADC is not that it lacks discriminative power, but that its output semantics diverge from the semantics of AC-style pretraining. AC pretraining encourages models to describe all salient content in an audio, whereas ADC finetuning asks them to suppress shared content and verbalize only a small delta. The ACC formulation was introduced precisely to mitigate this mismatch. Instead of asking what changed, ACC asks what remains common, and therefore retains the multi-audio reasoning difficulty of ADC while staying much closer to the descriptive style of conventional AC (Jia et al., 3 Aug 2025).

A compact formal comparison is:

• AC: learn $p(y_1 \mid a_1)$ and $p(y_2 \mid a_2)$
• ADC: learn $p(\Delta(y_1,y_2)\mid a_1,a_2)$
• ACC: learn $p(\mathcal{C}(y_1,y_2)\mid a_1,a_2)$

Here, $\Delta$ denotes a difference description, while $\mathcal{C}$ denotes a commonality extraction procedure over the captions paired with the two audios (Jia et al., 3 Aug 2025).

2. Formal definition and construction of commonality captions

ACC is defined over multiple audio clips; in the published instantiation, the inputs are pairs of pre-edit and post-edit audios. The target is a caption describing the shared semantics between them: the sound events present in both clips, rather than the events that were added, removed, or replaced (Jia et al., 3 Aug 2025).

The training data is constructed from the same audio-editing pairs used for ADC. A base audio segment $A$ is drawn from AudioCaps, and single-event audios $B$ and $C$ are drawn from AuditEval, which contains 100 event types and 400 clips across 8 categories. Mixing these clips yields add, delete, and replace transformations, producing 148,500 audio editing pairs (Jia et al., 3 Aug 2025).

The ACC target caption is then derived by operation type:

• Add: $A \to A+B$. The commonality caption is the caption of the original audio $p(y_2 \mid a_2)$0.
• Delete: $p(y_2 \mid a_2)$1. The commonality caption is the caption of the post-edit audio.
• Replace: $p(y_2 \mid a_2)$2. The commonality caption is obtained by word-level alignment between the two captions and extraction of the longest overlapping phrase or content segment.

Using the notation in the source formulation, if $p(y_2 \mid a_2)$3 is a pair of audios with captions $p(y_2 \mid a_2)$4 and $p(y_2 \mid a_2)$5, then the commonality function $p(y_2 \mid a_2)$6 selects $p(y_2 \mid a_2)$7 for Add, $p(y_2 \mid a_2)$8 for Delete, and the longest common subsequence phrase for Replace. Training maximizes

$p(y_2 \mid a_2)$9

using standard autoregressive generation loss over textual tokens (Jia et al., 3 Aug 2025).

This construction makes ACC “comparably challenging but gentler” than ADC for two reasons. It remains challenging because the model must jointly process multiple audios, compare them, and infer structured shared semantics. It is gentler because the target caption is usually richer, longer, and stylistically closer to ordinary AC captions than short edit instructions. For Add and Delete operations in particular, the ACC caption is effectively a standard AC caption for the shared scene, not an instruction about change (Jia et al., 3 Aug 2025).

3. MLLM implementation and training regimes

The published ACC experiments use Qwen2-Audio-7B-Instruct as the underlying MLLM. In that architecture, a high-capacity audio encoder maps raw audio into continuous embeddings, Qwen-7B serves as the LLM, and audio embeddings are passed to the decoder with instruction-style prompts. The model natively supports multi-audio inputs by treating audio embeddings as special tokens that can be interleaved with textual prompts (Jia et al., 3 Aug 2025).

Finetuning is performed with LoRA-based parameter-efficient adaptation. The reported configuration uses LoRA rank $p(\Delta(y_1,y_2)\mid a_1,a_2)$0, alpha $p(\Delta(y_1,y_2)\mid a_1,a_2)$1, dropout $p(\Delta(y_1,y_2)\mid a_1,a_2)$2, AdamW with learning rate $p(\Delta(y_1,y_2)\mid a_1,a_2)$3, weight decay $p(\Delta(y_1,y_2)\mid a_1,a_2)$4, cosine schedule, batch size $p(\Delta(y_1,y_2)\mid a_1,a_2)$5 with gradient accumulation of $p(\Delta(y_1,y_2)\mid a_1,a_2)$6, maximum context length $p(\Delta(y_1,y_2)\mid a_1,a_2)$7, and the ms-swift framework. The loss is the standard instruction-tuning cross-entropy over token sequences conditioned on audio and text prompt. No additional contrastive or specialized ACC loss is introduced; when multiple tasks are used together, they are combined through data mixture rather than an explicitly weighted multitask loss (Jia et al., 3 Aug 2025).

Two training regimes are reported. In single-task finetuning, the model is trained on exactly one task among AC, ADC, and ACC. In sequential multitask finetuning, all models are first finetuned on AudioCaps with AC and then further finetuned on either ADC data or ACC data, yielding AC+ADC and AC+ACC variants (Jia et al., 3 Aug 2025).

The grouping logic for ACC is entirely defined by the editing construction. Each instance is a pair of clips—pre-edit and post-edit—derived from Add, Delete, or Replace operations. Because both clips share the base component $p(\Delta(y_1,y_2)\mid a_1,a_2)$8, the commonality is non-trivial but well-defined: the pair is guaranteed to share a large portion of background content by construction (Jia et al., 3 Aug 2025).

4. Common representations and contrastive precursors

Although ACC itself is trained in the cited MLLM work with standard instruction-tuning loss, the broader AAC literature contains earlier mechanisms for learning representations that encode cross-modal commonality. An important precursor is CL4AC, an encoder-decoder AAC framework designed to improve latent representation quality and audio-text alignment under limited data (Liu et al., 2021).

CL4AC uses a sequence-to-sequence architecture with a CNN-10 PANNs audio encoder and a Transformer decoder. The AAC task is formulated over training data

$p(\Delta(y_1,y_2)\mid a_1,a_2)$9

where $p(\mathcal{C}(y_1,y_2)\mid a_1,a_2)$0 is the log mel-spectrogram and $p(\mathcal{C}(y_1,y_2)\mid a_1,a_2)$1 is the caption token sequence. The decoder models

$p(\mathcal{C}(y_1,y_2)\mid a_1,a_2)$2

with $p(\mathcal{C}(y_1,y_2)\mid a_1,a_2)$3, and captioning alone is trained by cross-entropy: $p(\mathcal{C}(y_1,y_2)\mid a_1,a_2)$4

Its distinctive component is a self-supervised auxiliary task built from mismatched audio-caption pairs. The decoder outputs a joint audio-text representation

$p(\mathcal{C}(y_1,y_2)\mid a_1,a_2)$5

and the final state $p(\mathcal{C}(y_1,y_2)\mid a_1,a_2)$6, described as able to attend to the context of all input tokens and the audio feature, is passed to a binary classifier that predicts whether the audio-caption pair is matched or unpaired. The contrastive loss is

$p(\mathcal{C}(y_1,y_2)\mid a_1,a_2)$7

and the training objective is

$p(\mathcal{C}(y_1,y_2)\mid a_1,a_2)$8

For positive pairs ($p(\mathcal{C}(y_1,y_2)\mid a_1,a_2)$9), both captioning and contrastive supervision are applied; for negative pairs ($\Delta$0), only the contrastive term is used because the captioning gradient is meaningless (Liu et al., 2021).

Within this framework, the notion of commonality appears implicitly in two places: the shared cross-modal representation $\Delta$1, whose states depend jointly on audio and text, and the contrastive discrimination over $\Delta$2, which encourages consistent semantic alignment and pushes mismatched pairs apart. The source description states that embeddings of different audio clips that share similar semantics and captions will converge in the shared latent space. This suggests a direct representational bridge to ACC: a latent space that clusters semantically aligned audio-caption pairs can serve as a substrate for identifying what is shared across multiple clips (Liu et al., 2021).

CL4AC reports improvements over a comparable baseline on the Clotho evaluation split, with BLEU$\Delta$3 improving from $\Delta$4 to $\Delta$5, CIDEr from $\Delta$6 to $\Delta$7, and SPIDEr from $\Delta$8 to $\Delta$9. The gains increase with higher-order BLEU scores, while SPICE remains unchanged and METEOR is slightly worse. In the paper’s interpretation, this pattern is consistent with better semantic alignment in the latent space under limited data (Liu et al., 2021).

5. Empirical behavior of ACC

ACC was evaluated against AC and ADC on both captioning benchmarks and downstream classification tasks. The captioning experiments use AudioCaps as both finetuning source and evaluation benchmark, with Clotho used only for evaluation and explicitly noted as having no overlap with training (Jia et al., 3 Aug 2025).

In single-task finetuning on AudioCaps, ACC outperformed AC on nearly all reported captioning metrics. BLEU-1 increased from $\mathcal{C}$0 to $\mathcal{C}$1, BLEU-4 from $\mathcal{C}$2 to $\mathcal{C}$3, FENSE from $\mathcal{C}$4 to $\mathcal{C}$5, SPICE from $\mathcal{C}$6 to $\mathcal{C}$7, SPIDEr from $\mathcal{C}$8 to $\mathcal{C}$9, CIDEr-D from $A$0 to $A$1, METEOR from $A$2 to $A$3, and ROUGE-L from $A$4 to $A$5. By contrast, ADC sharply reduced most metrics, yielding BLEU-4 $A$6, FENSE $A$7, SPIDEr $A$8, and CIDEr-D $A$9, although its SPICE score was $B$0 (Jia et al., 3 Aug 2025).

On Clotho, ACC and AC were much closer, but ADC again degraded performance. AC achieved BLEU-1 $B$1, BLEU-4 $B$2, FENSE $B$3, SPICE $B$4, SPIDEr $B$5, CIDEr-D $B$6, METEOR $B$7, and ROUGE-L $B$8. ACC yielded BLEU-1 $B$9, BLEU-4 $C$0, FENSE $C$1, SPICE $C$2, SPIDEr $C$3, CIDEr-D $C$4, METEOR $C$5, and ROUGE-L $C$6. The reported interpretation is that ACC generalizes out of domain at least as well as AC, whereas ADC produces severe degradation (Jia et al., 3 Aug 2025).

The sequential finetuning results accentuate the contrast. On AudioCaps, AC+ACC exceeded AC+ADC on every reported metric: BLEU-1 $C$7 vs $C$8, BLEU-4 $C$9 vs $A \to A+B$0, FENSE $A \to A+B$1 vs $A \to A+B$2, SPICE $A \to A+B$3 vs $A \to A+B$4, SPIDEr $A \to A+B$5 vs $A \to A+B$6, CIDEr-D $A \to A+B$7 vs $A \to A+B$8, METEOR $A \to A+B$9 vs $p(y_2 \mid a_2)$00, and ROUGE-L $p(y_2 \mid a_2)$01 vs $p(y_2 \mid a_2)$02. On Clotho, AC+ACC also exceeded AC+ADC, though by smaller margins: for example, BLEU-1 $p(y_2 \mid a_2)$03 vs $p(y_2 \mid a_2)$04, CIDEr-D $p(y_2 \mid a_2)$05 vs $p(y_2 \mid a_2)$06, and SPIDEr $p(y_2 \mid a_2)$07 vs $p(y_2 \mid a_2)$08 (Jia et al., 3 Aug 2025).

ACC also preserved more of the base model’s downstream capabilities than ADC. On VocalSound (VSC), IEMOCAP (SER), NSynth (MIC), and GTZAN (MGC), the original model scored $p(y_2 \mid a_2)$09, $p(y_2 \mid a_2)$10, $p(y_2 \mid a_2)$11, and $p(y_2 \mid a_2)$12. After AC finetuning, the scores were $p(y_2 \mid a_2)$13, $p(y_2 \mid a_2)$14, $p(y_2 \mid a_2)$15, and $p(y_2 \mid a_2)$16. After ADC finetuning, they dropped to $p(y_2 \mid a_2)$17, $p(y_2 \mid a_2)$18, $p(y_2 \mid a_2)$19, and $p(y_2 \mid a_2)$20. After ACC finetuning, they were $p(y_2 \mid a_2)$21, $p(y_2 \mid a_2)$22, $p(y_2 \mid a_2)$23, and $p(y_2 \mid a_2)$24. The reported conclusion is that ACC better preserves the model’s original capabilities across voice, speech, and music tasks, while ADC is consistently harmful across all four downstream tasks (Jia et al., 3 Aug 2025).

Qualitative case analysis follows the same pattern. AC retains holistic captioning ability but misses subtle nuances. ADC often produces vague or detached outputs such as “The sound of the scratch.” or “The phone vibrates.” ACC, by contrast, remains rich and multi-event while also capturing fine-grained details, indicating that it maintains the global scene semantics associated with AC while benefiting from multi-audio comparison (Jia et al., 3 Aug 2025).

6. Related resources and architectural templates

ACC research sits alongside two adjacent developments: large-scale fine-grained caption corpora for representation learning and token-prefix multimodal captioning architectures.

ACAVCaps is a large-scale, automatically generated audio captioning dataset derived from ACAV100M. It provides 13,000 hours of audio, 4.7M audio-caption samples, three captions per identified scene or event, domain coverage labeled as Extended Multi-Domain, and 76.7k unique tokens under the Qwen3 tokenizer. Its pipeline combines multi-expert annotation with LLM-CoT synthesis: CED-Base routes clips into speech, music, or sound-event analysis; specialized experts extract content-related and content-unrelated acoustic attributes; a baseline large audio-LLM contributes a direct description; and Deepseek-R1 synthesizes semantically consistent yet stylistically varied captions and QA pairs (Niu et al., 25 Mar 2026).

Although ACC is not evaluated in that work, ACAVCaps is highly relevant because ACC depends on representations that capture shared structure across clips at high semantic and acoustic resolution. The dataset is explicitly designed to be fine-grained and multi-faceted: captions may encode speaker properties, speaking style, emotion, tempo, mood, instrumentation, environment, reverberation, noise, and recording quality. The reported results indicate that pretraining on ACAVCaps yields a DATE score of $p(y_2 \mid a_2)$25 on MECAT-Caption versus $p(y_2 \mid a_2)$26–$p(y_2 \mid a_2)$27 for several other large captioning datasets, and it improves multiple downstream tasks relative to a four-dataset Combined baseline, including AISHELL-2, LibriSpeech, Common Voice French, NSynth, and IEMOCAP, though VGGSound is lower (Niu et al., 25 Mar 2026).

AVCap provides a complementary architectural template. It treats audio-visual features as text tokens in a Transformer decoder. Audio and video are first encoded by modality-specific ViT-style encoders, fused by a shallow joint encoder into $p(y_2 \mid a_2)$28, projected into the decoder’s text-embedding space,

$p(y_2 \mid a_2)$29

and prepended to the text token sequence with an attention mask that lets multimodal prefix tokens attend bidirectionally while text tokens attend causally to one another and fully to the prefix. The formulation is explicitly described as extensible to ACC by replacing a single audio-video pair with multiple audio inputs and learning a commonality representation to be used as prefix tokens in the same way (Kim et al., 2024).

The practical significance of these two lines is different but complementary. ACAVCaps supplies scale, lexical diversity, and attribute-rich supervision for learning representations that expose intersections among clips. AVCap supplies a concrete decoder interface for injecting such intersections into a LLM as pseudo-tokens. Together they situate ACC within a broader shift from single-clip captioning toward multi-input, representation-rich audio-language modeling (Niu et al., 25 Mar 2026, Kim et al., 2024).

7. Limitations and open directions

The ACC literature identifies several limitations that are structural rather than merely implementation-specific. First, the current dataset construction depends heavily on the Audit/AuditEval mixing strategy, on base AudioCaps segments, and on automatically produced pre-edit and post-edit captions. As a result, ACC supervision is tied to synthetic editing pairs, and its quality depends on the realism of the mixes and the reliability of the caption-generation pipeline (Jia et al., 3 Aug 2025).

Second, the operational definition of commonality is restricted. For Add and Delete, commonality is inherited directly from one of the full captions. For Replace, it is defined as the longest overlapping phrase or content segment under word-level alignment. This is heuristic: it may miss subtler shared semantics, and in some cases it may collapse to generic overlap such as “a person speaking” (Jia et al., 3 Aug 2025).

Third, current ACC experiments consider only audio pairs. More complex settings involving larger sets of clips, longer sequences, or temporal commonality are not studied. The model is also tuned within a relatively narrow instruction-tuning regime centered on AC, ADC, and ACC, leaving its interaction with broader pretraining objectives unresolved (Jia et al., 3 Aug 2025).

Related work points to several technically plausible extensions. CL4AC suggests that shared latent spaces built through binary matched-versus-unpaired discrimination can improve representation quality under data scarcity, and its discussion explicitly mentions MoCo and SimCLR as future directions for contrastive learning in AAC (Liu et al., 2021). ACAVCaps suggests a second direction: use multi-expert, multi-faceted caption generation to create richer supervision for shared-attribute reasoning across clips. The ACC paper itself suggests broader use of ACC as a general training objective in MLLMs for audio, exploration of richer multi-audio or multi-step scenarios, and combination with other pretraining objectives to improve robustness (Jia et al., 3 Aug 2025).

More generally, ACC introduces a specific design principle for multimodal generative systems: fine-grained supervision should remain close to the semantic and stylistic manifold of the model’s pretraining objective. In the reported evidence, difference-focused supervision conflicts with AC-style generative semantics and induces catastrophic forgetting, whereas commonality-focused supervision preserves the descriptive structure of captioning while still requiring multi-audio comparison. That principle is likely to remain central as ACC moves from pairwise synthetic construction toward larger-scale and more natural multi-audio reasoning settings (Jia et al., 3 Aug 2025).