MultiSoundGen: Multi-Event V2A Generation

• MultiSoundGen is a video-to-audio generation framework for multi-event scenarios that precisely aligns semantic content and dynamic changes.
• It leverages a SlowFast contrastive audio-visual pretraining (SF-CAVP) module to capture both coarse semantics and rapid temporal dynamics.
• The AVP-RPO stage applies direct preference optimization on top-layer network parameters to enhance audio quality, semantic alignment, and temporal synchronization.

Searching arXiv for the primary paper and closely related work on multi-event V2A generation, AVP, and unified audio generation. MultiSoundGen is a video-to-audio (V2A) generation framework for multi-event scenarios, defined as video scenarios involving multiple sound sources, sound events, or transitions. It is motivated by two stated limitations of existing V2A methods: difficulty in precisely aligning intricate semantic information together with rapid dynamic features, and the lack of quantitative preference optimization for semantic-temporal alignment and audio quality in foundational training. The framework combines a base V2A generator, a SlowFast Contrastive Audio-Visual Pretraining module (SF-CAVP), and an AVP-Ranked Preference Optimization procedure (AVP-RPO) that introduces direct preference optimization into V2A. Reported experiments describe state-of-the-art performance in multi-event scenarios across distribution matching, audio quality, semantic alignment, and temporal synchronization (Yang et al., 24 Sep 2025).

1. Problem formulation and design objective

The central task addressed by MultiSoundGen is the generation of high-quality, temporally synchronized, semantically aligned audio for videos containing multiple overlapping events, multiple sound sources, and dynamic transitions. The paper characterizes such inputs as especially difficult because the generator must jointly model core semantic content and rapid dynamic changes, rather than relying only on coarse scene-level correspondence. It further argues that foundational training procedures for V2A had not incorporated quantitative preference optimization targeted at integrated generation quality in cluttered multi-event scenes (Yang et al., 24 Sep 2025).

Within this formulation, MultiSoundGen does not treat audio generation as a purely reconstructive or purely semantic matching problem. Instead, it frames multi-event V2A as a compound alignment problem in which distribution matching, audio quality, semantic alignment, and temporal synchronization must be improved simultaneously. A plausible implication is that the method is designed to correct failure modes that arise when one of these dimensions improves at the expense of the others, particularly in scenes with rapid event transitions.

The framework therefore introduces two linked innovations. The first is SF-CAVP, a unified dual-stream audio-visual pretraining model that explicitly aligns core semantic representations and rapid dynamic features of audio-visual data. The second is AVP-RPO, which uses SF-CAVP as a reward model to quantify and prioritize semantic-temporal matches while enhancing audio quality through direct preference optimization (Yang et al., 24 Sep 2025).

2. System architecture and processing pipeline

The operational pipeline is built around three components: a base generator, an audio-visual pretraining model, and a preference-optimization stage. The base V2A model is MMAudio, described as a multimodal diffusion transformer (MM-DiT) trained with Conditional Flow Matching (CFM). SF-CAVP is trained separately to produce robust audio-video representations. AVP-RPO then iteratively fine-tunes the base model by ranking generated outputs with SF-CAVP and applying a DPO-based objective (Yang et al., 24 Sep 2025).

Component	Mechanism	Function
Base V2A generator	MMAudio (MM-DiT) with CFM	Initial video-to-audio generation
AVP module	SF-CAVP	Audio-video representation learning and reward scoring
Optimization stage	AVP-RPO	Preference-based fine-tuning for alignment and quality

This architecture is explicitly modular. The generator remains a diffusion-based V2A model, while the pretraining module supplies an external ranking signal and the optimization stage converts that signal into parameter updates. The paper states that AVP-RPO is iterative: for each video, the current model generates multiple candidate audios, SF-CAVP ranks them, and the model is refined using preference pairs built from those rankings (Yang et al., 24 Sep 2025).

The design also includes a constrained fine-tuning strategy. Rather than updating the entire generator, the method optimizes only the top layers—specifically the final single-modal transformer layer, adaLN layers, and 1D convs—while the lower layers are frozen. The stated motivation is to avoid disturbing the model’s global generative structure and denoising dynamics. This suggests that MultiSoundGen treats alignment repair as a high-level adaptation problem rather than a full re-estimation of the diffusion backbone.

3. SF-CAVP: SlowFast contrastive audio-visual pretraining

SF-CAVP is presented as a pioneering AVP model with a unified dual-stream architecture. It adopts SlowFast encoders for both modalities, using a shared design principle across audio and video. The Slow Stream uses lower temporal sampling and higher channel capacity to capture high-level semantic content, whereas the Fast Stream uses higher temporal sampling and lower channel capacity to capture rapid dynamics and fine-grained temporal changes. Lateral Connections transfer features between the streams across stages so that slow and fast representations are fused rather than isolated (Yang et al., 24 Sep 2025).

Both encoders employ a ResNet-50 backbone tailored for audio or video. Video is processed as temporal frame sequences, and audio is processed as log-mel-spectrogram sequences. After global pooling and concatenation of the slow and fast streams, each modality yields a 2304-dimensional feature vector. The architecture is therefore symmetrical at the representation level even though the raw inputs differ (Yang et al., 24 Sep 2025).

Training is performed with segment-level contrastive audio-visual pretraining. Video and audio are split into $S$ equal-duration temporal segments. A positive pair consists of audio and video features from the same temporal segment and video, while negative pairs consist of mismatched segments or samples from different videos. The objective is a bidirectional InfoNCE loss:

$$L_{av} = - \frac{1}{B S} \sum_{i=1}^B \sum_{j=1}^S \log \frac{\exp(a_{ij} \cdot v_{ij} / t)}{\sum_k \exp(a_{ik} \cdot v_{ik} / t)}$$

with learnable temperature $t$, and total loss

$L = \frac{L_{av} + L_{va}}{2}.$

The stated result of this design is a temporally aligned, semantically rich shared audio-video embedding space that is sensitive to both event semantics and dynamic changes. In the context of multi-event V2A, SF-CAVP functions not only as a pretraining mechanism but also as the ranking model later used by AVP-RPO (Yang et al., 24 Sep 2025).

4. AVP-RPO and direct preference optimization for V2A

The second major component, AVP-RPO, extends Direct Preference Optimization (DPO) to the V2A setting. Its procedure is explicitly iterative. For each input video, the current generator produces $N_a$ candidate audios. SF-CAVP then computes segment-wise cosine similarities between video and audio features for each candidate. Instead of using a simple mean across segments, the ranking score is defined as the mean of the lowest quartile of segment similarities:

$$S_{fs} = \mathrm{mean}(S_{\mathrm{sim}}^{(1)}, \ldots, S_{\mathrm{sim}}^{(\lceil S/4 \rceil)}).$$

The paper states that this construction increases sensitivity to weakly aligned segments, which is particularly relevant in cluttered multi-event scenes where failures may be localized rather than global (Yang et al., 24 Sep 2025).

Preference pairs are then constructed in a specific way. The winner is always the ground-truth audio, because the task requires strict audio-video fidelity. The loser is the generated audio with the lowest SF-CAVP similarity. This differs from self-ranking strategies that choose the best generated sample as the preferred output. The paper reports that using ground-truth audio as the winner outperforms using the highest-scoring generated audio (Yang et al., 24 Sep 2025).

The DPO objective is adapted to flow-matching diffusion models. The reported DPO-FM loss is

$$L_{\mathrm{DPO-FM}} = -\mathbb{E} \left[ \log \sigma \left( -\left\| v_\theta(t, C, x^\star) - u^\star \right\|^2 + \left\| v_\theta(t, C, x^-) - u^- \right\|^2 - \left\| v_{\theta_r}(t,C,x^\star)-u^\star \right\|^2 + \left\| v_{\theta_r}(t,C,x^-)-u^- \right\|^2 \right) \right].$$

To stabilize training, the method adds a normalized flow-matching anchor based on the winner sample:

$$L_{\mathrm{AVP-RPO}} = \mathcal{N}(L_{\mathrm{DPO-FM}}) + \mathcal{N}(L_{\mathrm{FM-win}}),$$

where $\mathcal{N}(\cdot)$ denotes normalization to $[0,1]$. The paper reports that summing normalized DPO and FM-win losses improves both fidelity and alignment, and that fine-tuning only the model’s top layers prevents severe performance degradation and noisy outputs (Yang et al., 24 Sep 2025).

5. Experimental protocol and reported performance

The experimental program separates pretraining and fine-tuning. SF-CAVP is trained on VGGSound with approximately 173K train videos and 2K val. AVP-RPO fine-tunes on VGGSound Source (VGG-SS) with 4.4K train, 120 val, and 500 test samples. Evaluation uses VGG-SS split into single-event and multi-event sets, and also includes an out-of-distribution test on Kling-Audio-Eval benchmark with 21K annotated samples (Yang et al., 24 Sep 2025).

The reported metrics cover four dimensions. Distribution matching is evaluated by Fréchet Distance (FD) using PANNs and VGGish, together with KL-Divergence (PANNs). Audio quality is measured with Inception Score (IS, PANNs). Semantic alignment is measured by average cosine similarity using ImageBind. Temporal synchronization is measured by DeSync score (Synchformer), where lower values indicate better synchronization (Yang et al., 24 Sep 2025).

Evaluation aspect	Metric(s)
Distribution matching	FD, KL-Divergence
Audio quality	IS
Semantic alignment	ImageBind cosine similarity
Temporal synchronization	DeSync

The paper states that MultiSoundGen achieves state-of-the-art performance in multi-event scenarios. In the summary table, it is described as having the lowest distribution-matching score, the highest or top 2 audio-quality score, high semantic-alignment performance within the top 2, and the lowest DeSync score, with a model size of 157M parameters. On multi-event videos, the method improves over the base model by up to 10.3% in distribution matching and 5.3% in temporal synchronization. The improvements are also reported as robust on single-event and out-of-distribution test sets (Yang et al., 24 Sep 2025).

The ablation studies reported in the paper are methodologically important. Replacing SF-CAVP with Segment AVCLIP as the AVP reward model leads to performance degradation across iterations. Using the ground-truth audio as the winner in preference pairs outperforms using the highest-scoring generated audio. Full-model tuning leads to severe degradation and noisy outputs, whereas top-layer tuning is stable. These findings collectively indicate that the gains are attributed not merely to additional fine-tuning, but to the specific combination of SF-CAVP ranking, ground-truth preference anchoring, and constrained parameter updates (Yang et al., 24 Sep 2025).

6. Relation to adjacent research directions

MultiSoundGen occupies a specific position within recent audio generation research. In sound-source-aware V2A, SSV2A addresses generation at the sound-source level by locally perceiving multimodal sound sources, learning a Cross-Modal Sound Source (CMSS) Manifold, and attentively mixing source semantics into a rich audio representation before generation (Guo et al., 2024). MultiSoundGen, by contrast, focuses on multi-event scenario alignment through dual-stream AVP and DPO-based optimization rather than explicit source-level object decomposition (Yang et al., 24 Sep 2025).

In long-form or structured audiovisual generation, ReelWave proposes a multi-agent framework for movie sound generation with a Sound Director, Foley Artist, Composer, and Voice Actor, while Foley-Omni jointly models speech, sound effects, and music within a shared latent generation process for complete video soundtrack generation (Wang et al., 10 Mar 2025). MultiSoundGen is narrower in task scope than these full-soundtrack systems, but more specialized in optimizing semantic-temporal correspondence for V2A under dense multi-event conditions (Tao et al., 2 Jun 2026).

At a broader systems level, unified audio models such as UniFlow-Audio support both time-aligned and non-time-aligned tasks across speech, music, and sound effects with omni-modal inputs and a non-autoregressive flow-matching backbone (Xu et al., 29 Sep 2025). A plausible implication is that MultiSoundGen’s SF-CAVP and AVP-RPO could be viewed as specialized components for a particular failure regime—multi-event V2A alignment—within the larger trajectory toward general audio generation systems. Its principal contribution is therefore not generic multimodal generation, but a targeted method for improving semantic-temporal alignment and audio quality where multiple events and transitions are present (Yang et al., 24 Sep 2025).