Detecting and Mitigating Insertion Hallucination in Video-to-Audio Generation
HALCON method reduces insertion hallucination in video-to-audio generation by over 50%.
Key Findings
Methodology
The paper introduces HALCON, a method to reduce insertion hallucination through a three-stage process: initial audio generation to expose hallucinated segments, identification and masking of unreliable video features, and audio regeneration using corrected conditioning. This method does not require model retraining.
Key Results
- HALCON reduced hallucination occurrence by 62% and duration by 64% on the Kling-Audio-Eval dataset.
- On the VGGSound dataset, hallucination occurrence was reduced by about 52%, but duration reduction was smaller, indicating training bias impact.
- On the AVE dataset, HALCON reduced hallucination occurrence by 53.5% and duration by 40.7%.
Significance
This study is the first to systematically define, measure, and effectively mitigate insertion hallucination in video-to-audio generation. By introducing new evaluation metrics IH@vid and IH@dur, the research paves the way for more reliable V2A models, enhancing their credibility in practical applications.
Technical Contribution
The technical contribution lies in the HALCON method, which reduces hallucination by dynamically masking unreliable video features. Compared to existing methods, HALCON effectively reduces hallucination without degrading audio quality and temporal synchronization.
Novelty
This work is the first to define and systematically study the insertion hallucination phenomenon, proposing the HALCON method to address it. Unlike previous work, HALCON intervenes directly during inference without requiring model retraining.
Limitations
- HALCON's effectiveness in reducing hallucination is limited on training datasets, indicating potential overfitting to training biases.
- In some cases, important video features may be mistakenly masked, affecting audio generation quality.
Future Work
Future research could explore more refined feature masking strategies and generalization capabilities across more datasets. Additionally, the study could extend to other multimodal generation tasks.
AI Executive Summary
Video-to-audio generation technology has made significant strides in recent years, but existing models often generate sounds that do not match the visual content, known as insertion hallucination. This paper is the first to define and systematically study this phenomenon, proposing the HALCON method to reduce hallucination. HALCON uses a three-stage process: initial audio generation, identification and masking of unreliable video features, and audio regeneration, significantly reducing hallucination occurrence and duration. Experimental results show that HALCON effectively reduces hallucination across multiple datasets while maintaining audio quality and temporal synchronization. This research paves the way for more reliable V2A models, with significant academic and practical implications. Future research could explore more refined feature masking strategies and applications in other multimodal generation tasks.
Deep Analysis
Background
Video-to-audio generation aims to automatically synthesize sound for videos, traditionally relying on manual Foley production. Recently, models like MMAudio and ThinkSound have achieved significant progress by learning audio-video alignment from large-scale datasets. However, these models often exhibit insertion hallucination, generating sounds that do not correspond to the visual content.
Core Problem
Insertion hallucination refers to the generation of sound events with no corresponding visual source. This phenomenon is primarily driven by dataset biases, such as the prevalence of off-screen sounds. Existing evaluation metrics fail to detect this issue, leading to misleading assessments of model reliability.
Innovation
The HALCON method reduces insertion hallucination through a three-stage process. First, it generates initial audio to expose hallucinated segments, then identifies and masks unreliable video features, and finally regenerates audio using corrected conditioning. This method intervenes directly during inference without requiring model retraining.
Methodology
- �� Initial Generation: Generate initial audio from input video.
- �� Hallucination Detection: Use a multi-detector ensemble framework to identify hallucinated segments.
- �� Feature Correction: Mask unreliable video features and regenerate audio using corrected features.
Experiments
Experiments were conducted on Kling-Audio-Eval, VGGSound, and AVE datasets to evaluate the effectiveness of the HALCON method. IH@vid and IH@dur metrics were used to quantify hallucination occurrence and severity, compared to existing models.
Results
The HALCON method significantly reduced hallucination occurrence and duration across multiple datasets. On the Kling-Audio-Eval dataset, hallucination occurrence was reduced by 62% and duration by 64%. On the VGGSound dataset, occurrence was reduced by about 52%.
Applications
The HALCON method can be used to improve the reliability of video-to-audio generation models, applicable to automatic sound effect generation in movies, games, and animations.
Limitations & Outlook
HALCON's effectiveness in reducing hallucination is limited on training datasets, indicating potential overfitting to training biases. In some cases, important video features may be mistakenly masked, affecting audio generation quality.
Plain Language Accessible to non-experts
Imagine you're cooking in a kitchen. You have a recipe (video) and need to make a dish (audio) based on it. Sometimes, the recipe includes irrelevant steps, like adding music while making a salad. The HALCON method acts like a smart assistant, identifying these irrelevant steps and helping you focus on the necessary ones, resulting in a tastier dish.
ELI14 Explained like you're 14
Imagine you're playing a game where your task is to generate the right sound effects based on video content. Sometimes, the game gives you irrelevant hints, like playing music in a quiet scene. HALCON is like a super assistant that spots these irrelevant hints and helps you generate more fitting sound effects, making the game experience more realistic!
Glossary
Insertion Hallucination
Refers to the generation of sound events with no corresponding visual source, often caused by dataset biases.
In video-to-audio generation, models often generate sounds like music or speech that don't match the visual content.
HALCON
A method to reduce insertion hallucination by masking unreliable video features.
HALCON intervenes during inference without requiring model retraining.
IH@vid
A new metric to measure the occurrence of hallucination in videos.
Used to quantify the frequency of insertion hallucination in video-to-audio generation.
IH@dur
A new metric to measure the duration of hallucination.
Used to assess the severity of insertion hallucination in generated audio.
Multi-detector Ensemble Framework
A framework combining multiple audio event detectors to identify hallucinated segments.
Improves hallucination detection accuracy through majority voting.
Open Questions Unanswered questions from this research
- 1 How to further reduce hallucination without affecting audio quality? Current methods are limited on training datasets.
- 2 How to extend the HALCON method to other multimodal generation tasks?
- 3 How to improve hallucination detection accuracy without increasing computational costs?
Applications
Immediate Applications
Multimedia Content Production
HALCON can be used for automatic sound effect generation in movies, games, and animations, improving audio-video matching.
Long-term Vision
Multimodal Generation Tasks
In the future, the HALCON method could be extended to other multimodal generation tasks, enhancing the reliability and authenticity of generated content.
Abstract
Video-to-Audio generation has made remarkable strides in automatically synthesizing sound for video. However, existing evaluation metrics, which focus on semantic and temporal alignment, overlook a critical failure mode: models often generate acoustic events, particularly speech and music, that have no corresponding visual source. We term this phenomenon Insertion Hallucination and identify it as a systemic risk driven by dataset biases, such as the prevalence of off-screen sounds, that remains completely undetected by current metrics. To address this challenge, we first develop a systematic evaluation framework that employs a majority-voting ensemble of multiple audio event detectors. We also introduce two novel metrics to quantify the prevalence and severity of this issue: IH@vid (the fraction of videos with hallucinations) and IH@dur (the fraction of hallucinated duration). Building on this, we introduce HALCON to mitigate IH. HALCON follows a three-stage procedure: it first generates initial audio to expose hallucinated segments, then identifies and masks the corresponding unreliable video features, and finally regenerates the audio using the corrected conditioning. Experiments on several mainstream V2A benchmarks first reveal that state-of-the-art models suffer from severe IH. In contrast, our HALCON method reduces both the prevalence and duration of hallucinations by over 50\% on average, without degrading, and in some cases even improving, conventional metrics for audio quality and temporal synchronization. Our work is the first to formally define, systematically measure, and effectively mitigate Insertion Hallucination, paving the way for more reliable and faithful V2A models.