Audio Difference Captioning (ADC)

• Audio Difference Captioning (ADC) is a task where models compare two similar audio clips and generate natural language instructions describing subtle changes such as added events or volume variations.
• ADC methods employ techniques like cross-attention masking, latent subtraction, and prefix tuning to build comparison-aware representations of paired audio inputs.
• Empirical findings show that explicit comparative mechanisms, such as late similarity-discrepancy disentanglement, significantly enhance performance over naive captioning approaches.

Audio Difference Captioning (ADC) is an audio-language task in which a model receives two audio clips and generates natural-language text describing their semantic difference rather than independently summarizing each clip. In its original formulation, the task targets pairs of similar but slightly different clips and often expresses the output as an instruction such as “Make the rain louder,” that is, a description of how to modify the first clip to obtain the second (Takeuchi et al., 2023). Related work broadens the output space from concise difference captions to multi-tier comparative explanations of audio events, acoustic scenes, signal characteristics, and listener emotion, but preserves the same core structure of pairwise comparative generation (Deshmukh et al., 6 Feb 2025). A distinct but closely related line of work uses latent difference representations and weak supervision from synthetic audio mixing to make captioning models difference-aware without collecting human-written difference captions, which is relevant to ADC but is not itself a fully supervised ADC formulation for arbitrary paired recordings (Komatsu et al., 2023).

1. Task definition and formalization

ADC was explicitly introduced as “a new extension task of audio captioning for describing the semantic differences between input pairs of similar but slightly different audio clips” (Takeuchi et al., 2023). This definition departs from conventional audio captioning in three ways: the input is a pair $(\boldsymbol{x}, \boldsymbol{y})$ rather than a single clip, the target text is a difference description rather than a scene summary, and the model must detect and verbalize subtle event-level or attribute-level changes such as addition or removal of a sound event or a change in loudness (Takeuchi et al., 2023).

In the formal setup of the original ADC paper, the system estimates a word sequence $\boldsymbol{w}$ from two audio clips $\boldsymbol{x}$ and $\boldsymbol{y}$ through three stages: audio embedding, audio difference encoding, and text decoding (Takeuchi et al., 2023). The mapping is written as

$$(\boldsymbol{x}, \boldsymbol{y}) \mapsto \boldsymbol{w}.$$

The difference encoding stage is treated as the distinctive component of ADC, because it must convert two clip representations into a comparison-oriented latent representation that foregrounds what changed.

A broader comparative formulation appears in “ADIFF: Explaining audio difference using natural language,” which defines dataset instances as $\{a_i, a_j, e_{ij}\}$ or, in training notation, $\{x_1^i, x_2^i, t^i, c^i\}$, where the output is a natural-language explanation of the difference between the two recordings (Deshmukh et al., 6 Feb 2025). In that work, the task is still pairwise caption generation, but the target can be concise, brief, or detailed, depending on the requested granularity. This suggests that ADC can be understood narrowly as concise difference captioning or more broadly as comparative audio explanation.

A recurrent motivation across the literature is that standard single-audio captioning often generates similar captions for similar clips, making the actual difference difficult to recover from two independently produced captions (Takeuchi et al., 2023). ADC therefore addresses change description directly rather than treating it as an implicit by-product of ordinary captioning.

2. Relation to conventional audio captioning and adjacent tasks

ADC is distinct from standard audio captioning, where one audio clip is mapped to one caption summarizing the clip’s main semantic content (Takeuchi et al., 2023). In ADC, the output no longer attempts to cover the full content of either clip; instead it verbalizes a discrepancy or edit relation between them. In the original task framing, the output is typically directional and instruction-like, expressing what should change in the first clip to obtain the second (Takeuchi et al., 2023).

This directional structure has methodological consequences. The original ADC paper identifies differences of several kinds, including event-level changes such as addition or removal of a dog bark and attribute-level changes such as louder background rain (Takeuchi et al., 2023). The ADIFF formulation enlarges this scope to include audio events, sound objects or sources, acoustic scenes, signal characteristics including frequency, amplitude, temporal patterns, pitch, timbre, loudness, dynamic range, attack and decay, spectro-temporal qualities, temporal order, semantics, and listener emotion (Deshmukh et al., 6 Feb 2025). A plausible implication is that later ADC-style work moves from caption-sized descriptions toward comparative reasoning over acoustics, scene structure, and human perception.

ADC is also not equivalent to retrieval, classification, audio question answering, or simply determining whether two clips are the same or different. The ADIFF paper explicitly positions its task as a generation problem requiring joint encoding of two audio inputs, explicit comparison, and free-form comparative text generation (Deshmukh et al., 6 Feb 2025). The original ADC paper likewise argues that the key challenge is not recognizing content in isolation but extracting and verbalizing what is different between highly similar clips (Takeuchi et al., 2023).

A common misconception is that difference captioning can be approximated by separately captioning each clip and informally comparing the outputs. The motivating examples in ADC research are introduced precisely because this strategy fails when both captions collapse to near-identical generic descriptions such as “heavy rain,” even when one clip contains louder rain or an additional event (Takeuchi et al., 2023). The task is therefore defined as comparison-conditioned generation rather than post hoc caption differencing.

3. Supervision regimes and benchmark datasets

The first dedicated ADC benchmark is AudioDiffCaps, introduced together with the formal task definition (Takeuchi et al., 2023). AudioDiffCaps consists of pairs of similar but slightly different audio clips with human-written difference captions. The paired clips are synthetically generated using Scaper, with source sounds drawn from FSD50K for background sounds and ESC-50 for foreground event sounds (Takeuchi et al., 2023). The background classes are rain and car_passing_by, and the foreground event classes are dog, chirping_bird, thunder, footsteps, car_horn, and church_bells (Takeuchi et al., 2023). Each generated clip is 10 seconds long, can contain up to two foreground events, and allows 0–100% overlap (Takeuchi et al., 2023). The development set contains 5,996 audio clip pairs and the evaluation set contains 1,720 audio clip pairs; annotation was done by five naïve workers remotely supervised by an experienced annotator, and captions are written as instructional descriptions such as “Make the rain louder” (Takeuchi et al., 2023).

A broader benchmark regime appears in ADIFF through two new datasets derived from existing caption corpora: ACD, from AudioCaps, and CLD, from ClothoV21 (Deshmukh et al., 6 Feb 2025). These datasets use LLM-generated difference explanations from pairs of human captions, with human verification applied only to the test set (Deshmukh et al., 6 Feb 2025). Each pair is annotated at three explanation tiers. For CLD, the per-tier split sizes are Train 19,195, Val 5,225, and Test 5,225; for ACD, the per-tier split sizes are Train 48,660, Val 2,456, and Test 4,680 (Deshmukh et al., 6 Feb 2025). Median explanation lengths increase substantially by tier: for CLD, 27, 51, and 155 tokens; for ACD, 29, 52–53, and 154–155 (Deshmukh et al., 6 Feb 2025). The paper also reports an LLM-based information density score of 2.91 for Tier 1, 3.64 for Tier 2, and 4.17 for Tier 3 (Deshmukh et al., 6 Feb 2025).

A separate but important supervision regime is presented in “Audio Difference Learning for Audio Captioning,” which does not provide human-authored difference captions for arbitrary paired recordings (Komatsu et al., 2023). Instead, it synthesizes a weakly supervised difference-captioning signal by mixing a captioned audio clip $X_{input}$ with a reference sound $X_{ref}$ to form

$X_{input}^{+} = X_{input} + X_{ref},$

then treating the pair $\boldsymbol{w}$0 as a differential example whose target caption is simply the original caption $\boldsymbol{w}$1 of $\boldsymbol{w}$2 (Komatsu et al., 2023). The resulting objective is

$\boldsymbol{w}$3

This construction uses Clotho as the captioning dataset and ESC-50 as the source of additional reference sounds; the Clotho split sizes are 4,981 clips total, with 2,893 train, 1,045 validation, and 1,043 test, and each clip is 15–30 seconds long (Komatsu et al., 2023). The method is therefore best described as weakly supervised residual caption learning rather than supervised ADC with human difference labels.

Dataset or regime	Supervision type	Key properties
AudioDiffCaps	Human-annotated difference captions	5,996 development pairs; 1,720 evaluation pairs; instruction-form captions (Takeuchi et al., 2023)
ACD / CLD	LLM-generated difference explanations; human-verified test set	Three explanation tiers; derived from AudioCaps and ClothoV21 (Deshmukh et al., 6 Feb 2025)
Audio difference learning	Weak supervision from synthetic mixtures	Uses original captions as targets for latent residuals (Komatsu et al., 2023)

These three regimes correspond to three distinct notions of ADC supervision: direct pairwise difference captioning, synthetic comparative explanation, and indirect difference-aware training.

4. Model architectures and representation strategies

The original ADC model combines a pretrained audio feature extractor, a difference encoder, a text decoder, and a similarity-discrepancy disentanglement auxiliary objective (Takeuchi et al., 2023). Audio features are extracted with BYOL-A and fine-tuned end-to-end, yielding frame-level representations

$\boldsymbol{w}$4

The model then prepends special tokens $\boldsymbol{w}$5 and $\boldsymbol{w}$6, concatenates the two streams into

$\boldsymbol{w}$7

applies positional encoding, and passes the sequence through a cross-attention-concentrated (CAC) transformer encoder (Takeuchi et al., 2023).

The defining mechanism of the CAC encoder is a masked multi-head attention layer in which each token may attend only across clips, not within its own clip (Takeuchi et al., 2023). Tokens associated with the first clip are blocked from attending to the first clip, and tokens associated with the second clip are blocked from attending to the second clip. This forces the encoder to build comparison-oriented representations rather than model each clip independently.

The similarity-discrepancy disentanglement (SDD) objective further divides the hidden representation into similar and discrepant halves (Takeuchi et al., 2023). For early disentanglement or late disentanglement, the latent representation is split along the hidden dimension, and the auxiliary loss is

$\boldsymbol{w}$8

with

$\boldsymbol{w}$9

$\boldsymbol{x}$0

Here $\boldsymbol{x}$1 and $\boldsymbol{x}$2 are embedding networks made of a bidirectional LSTM followed by average pooling (Takeuchi et al., 2023). Training optimizes

$\boldsymbol{x}$3

The latent-difference approach of Komatsu et al. is architecturally simpler (Komatsu et al., 2023). It remains within an encoder-decoder audio captioning pipeline but introduces a second audio input $\boldsymbol{x}$4 encoded by the same shared audio encoder. The key representation is

$\boldsymbol{x}$5

with simple subtraction used in the reported experiments (Komatsu et al., 2023). A BART decoder then generates text from the differential representation. The architecture is built on the DCASE 2023 Task 6 baseline, uses a 64-dimensional mel-spectrogram computed from audio sampled at 44.1 kHz, with 40 ms window length and 20 ms hop size, a pre-trained CNN layer with 12 layers followed by an adapter layer consisting of linear layers, and latent feature dimension 768 (Komatsu et al., 2023).

ADIFF adopts a different design centered on prefix tuning and relational latent transformation (Deshmukh et al., 6 Feb 2025). Each audio is encoded with HTSAT pretrained on AudioSet, projected into GPT-2 latent space, and combined with a separator token and prompt embedding. The combined prefix is

$\boldsymbol{x}$6

then transformed by a cross-projection module,

$\boldsymbol{x}$7

before autoregressive decoding with GPT-2 under

$\boldsymbol{x}$8

ADIFF supplements this with position captioning, in which the model is sometimes asked to caption the first or second audio specifically, thereby improving attribution of content to input position (Deshmukh et al., 6 Feb 2025).

Taken together, these architectures illustrate three major comparison strategies in ADC-related work: forced cross-reference through attention masking, direct latent subtraction, and explicit relational fusion in prompt space.

5. Evaluation protocols and empirical findings

ADC studies generally use captioning metrics, including BLEU, METEOR, ROUGE-L, CIDEr, SPICE, and SPIDEr (Takeuchi et al., 2023, Deshmukh et al., 6 Feb 2025). The original ADC experiments compare a baseline transformer, the CAC transformer, CAC plus early SDD, and CAC plus late SDD on AudioDiffCaps (Takeuchi et al., 2023). The baseline obtains BLEU-1 67.1, BLEU-4 31.7, METEOR 24.3, ROUGE-L 56.9, CIDEr 82.7, SPICE 19.5, and SPIDEr 51.1; the CAC transformer improves to BLEU-4 33.4, METEOR 25.2, ROUGE-L 59.5, CIDEr 90.2, and SPIDEr 54.9; the best-performing late SDD model with $\boldsymbol{x}$9 reaches BLEU-1 69.9, BLEU-4 39.5, METEOR 26.3, ROUGE-L 61.3, CIDEr 97.1, SPICE 22.6, and SPIDEr 59.9 (Takeuchi et al., 2023). The paper states that late disentanglement achieves the best results in all evaluation metrics overall.

The weakly supervised latent-difference method is evaluated primarily as standard audio captioning rather than direct ADC (Komatsu et al., 2023). On Clotho, the baseline reports SPIDEr 0.272 and CIDEr 0.420, AL-MixGen reports SPIDEr 0.275 and CIDEr 0.425, and the proposed method reports SPIDEr 0.291 and CIDEr 0.454 (Komatsu et al., 2023). The headline result is an improvement in SPIDEr from 0.272 to 0.291, about a 7% relative gain (Komatsu et al., 2023). Because the reference audio is used during training only and “was not used during the evaluation,” these results demonstrate improvement in conventional captioning benchmarks rather than quantitative evaluation on a supervised ADC benchmark (Komatsu et al., 2023).

ADIFF reports results separately by explanation tier on ACD and CLD (Deshmukh et al., 6 Feb 2025). On ACD, SPIDEr rises from 0.220 to 0.303 for Tier 1, from 0.225 to 0.345 for Tier 2, and from 0.123 to 0.183 for Tier 3 when moving from the naive baseline to ADIFF (Deshmukh et al., 6 Feb 2025). On CLD, the corresponding SPIDEr values move from 0.212 to 0.652 for Tier 1, from 0.641 to 0.692 for Tier 2, and from 0.166 to 0.417 for Tier 3 (Deshmukh et al., 6 Feb 2025). Human evaluation by five professionals scores explanations for correctness, granularity, and readability on a 1–5 scale; ADIFF obtains 3.47, 3.53, and 3.57 respectively, exceeding the baseline and Qwen-Audio variants on all three dimensions (Deshmukh et al., 6 Feb 2025).

A notable empirical pattern across these papers is that explicit comparison mechanisms improve performance more consistently than naive pair concatenation. In the original ADC model, cross-attention masking and late SDD both sharpen attention around the discrepant regions of the pair (Takeuchi et al., 2023). In ADIFF, cross-projection, position captioning, and final-stage fine-tuning all improve comparative generation, with particularly large gains on CLD Tier 3 after adding position captioning (Deshmukh et al., 6 Feb 2025). This suggests that attribution and pairwise interaction are central technical bottlenecks in ADC.

6. Limitations, controversies, and later developments

The literature repeatedly distinguishes between direct ADC and methods that are merely difference-aware. The most explicit case is “Audio Difference Learning for Audio Captioning,” whose main benchmark evaluation reverts to standard single-audio captioning by setting the reference to zero at inference, even though the model can also be used qualitatively for latent subtraction between two audios (Komatsu et al., 2023). It therefore provides a foundation for difference-aware representation learning, but it does not constitute a supervised ADC benchmark with human-authored difference captions for arbitrary real pairs.

The first direct ADC benchmark is also deliberately narrow. AudioDiffCaps restricts the space of differences to increase or decrease of background sounds, increase or decrease of sound events, and addition or removal of sound events, using only a small number of background and foreground classes and synthetic soundscape generation (Takeuchi et al., 2023). The paper explicitly notes that the dataset scope is limited, the data are synthetic, captions are instruction-form only, and future work should explore broader linguistic variety and stronger pretrained generative LLMs such as BART (Takeuchi et al., 2023). A plausible implication is that early ADC benchmarks prioritize task identifiability and controlled comparison over ecological diversity.

ADIFF broadens the output space substantially but introduces another limitation: most training labels are LLM-generated from human captions rather than written directly from raw listening, and only the test set is human-verified (Deshmukh et al., 6 Feb 2025). The paper is explicit that train and validation labels remain partially synthetic and that hallucinated events in generated explanations had to be corrected on the test set (Deshmukh et al., 6 Feb 2025). It also notes that Tier 3 remains hard, that both baseline and stronger models can hallucinate events, and that objective metrics may be misleading because they reward surface overlap in an open-ended comparative generation task (Deshmukh et al., 6 Feb 2025).

A later development reframes ADC not as a benchmark problem in itself but as a fine-grained training objective for multimodal LLMs, and argues that it may be poorly matched to caption-centric pretraining (Jia et al., 3 Aug 2025). That paper describes ADC as a task that “takes multiple audio inputs and encourages the model to describe their differences, thereby promoting fine-grained audio discrimination,” but criticizes the resulting “semantic gap between the input audios—often rich in diverse sound events—and the relatively brief, difference-focused output captions” (Jia et al., 3 Aug 2025). In experiments with Qwen2-Audio-7B-Instruct, fine-tuning with ADC yields markedly worse captioning metrics than ordinary audio captioning or Audio Commonality Captioning (ACC). On AudioCaps, ADC reports BLEU-1 0.0825, BLEU-4 0.0051, FENSE 0.3442, SPIDEr 0.0688, CIDEr-D 0.0927, METEOR 0.0422, and ROUGE-L 0.0949, compared with substantially higher values for AC and ACC (Jia et al., 3 Aug 2025). The same work reports degraded downstream performance on VSC, SER, MIC, and MGC, and interprets this as catastrophic forgetting caused by mismatch with AC-style pretraining (Jia et al., 3 Aug 2025).

This later critique does not deny the usefulness of difference-focused supervision for explicit change description. Rather, it argues that ADC and AC serve different optimization goals: ADC sharpens discriminative comparison, while caption-pretrained audio MLLMs may retain broader capabilities better under commonality-focused objectives (Jia et al., 3 Aug 2025). The controversy is therefore not whether difference description is a meaningful task, but where ADC fits in the design space of audio-language modeling.

Across these papers, several unresolved issues recur: asymmetry between $\boldsymbol{y}$0 and $\boldsymbol{y}$1, temporal alignment of paired recordings, multiple simultaneous changes, non-additive acoustic transformations, lexical grounding for words such as “added,” “removed,” or “remains,” and the difficulty of evaluating rich comparative explanations with standard captioning metrics (Komatsu et al., 2023, Deshmukh et al., 6 Feb 2025). This suggests that ADC is best understood not as a solved variant of audio captioning but as an active research area spanning task design, supervision strategy, pairwise representation learning, and evaluation methodology.