Automated Audio Captioning: A Multi-Agent Approach to Enhance Performance
Introduction
The Automated Audio Captioning (AAC) task centers around generating natural language descriptions from audio inputs. Given the distinct modalities between the input (audio) and the output (text), AAC systems typically rely on an audio encoder to extract relevant information from the sound, represented as feature vectors, which a decoder then uses to generate text descriptions.
The Approach
Our approach employs multiple audio encoders, specifically BEATs and ConvNeXt, to generate complementary audio representations. This fusion enables the decoder to attend to a wider pool of feature sets, leading to more accurate and detailed captions.
Multi-Encoder Fusion
We employed two pre-trained audio encoders (BEATs and ConvNeXt) to generate complementary audio representations. This fusion enables the decoder to attend to a wider pool of feature sets, leading to more accurate and detailed captions.
Multi-Layer Aggregation
Different layers of the encoders capture varying aspects of the input audio, and by aggregating outputs across all layers, we further enriched the information fed into the decoder.
Generative Caption Modeling
To optimize the generation of natural language descriptions, we applied a large language model (LLM)-based summarization process, similar to techniques used in RobustGER. This step consolidates multiple candidate captions into a single, fluent output, using LLMs to ensure both grammatical coherence and a human-like feel to the descriptions.
Multi-Agent Collaboration
Our approach also involves a new multi-agent collaboration inference pipeline, inspired by recent research showing the benefits of nucleus sampling in AAC tasks. This pipeline consists of three stages:
CLAP-based Caption Filtering
We generate multiple candidate captions and filter out less relevant ones using a Contrastive Language-Audio Pretraining (CLAP) model, reducing the number of candidates by half.
Hybrid Reranking
The remaining captions are then ranked using our hybrid reranking method to select the top k-best captions.
LLM Summarization
Finally, we use a task-activated LLM to summarize the k-best captions into a single, coherent caption, ensuring the final output captures all critical aspects of the audio.
Impact and Performance
Our multi-encoder system achieved a Fluency Enhanced Sentence-BERT Evaluation (FENSE) score of 0.5442, outperforming the baseline score of 0.5040. By incorporating multi-agent systems, we have opened new avenues for further improving AAC tasks.
Future Work
We will explore integrating more advanced fusion techniques and examining how further collaboration between specialized agents can enhance both the granularity and quality of the generated captions.
Conclusion
Our contributions demonstrate the potential of multi-agent AI systems in advancing general-purpose understanding. We hope that our work inspires continued exploration in this area and encourages other teams to adopt similar strategies for fusing diverse models to handle complex multimodal tasks like AAC.
FAQs
Q: What is the main goal of Automated Audio Captioning (AAC)?
A: The main goal of AAC is to generate natural language descriptions from audio inputs.
Q: What are the key innovations in your approach?
A: Our approach employs multiple audio encoders, multi-layer aggregation, and generative caption modeling, as well as a new multi-agent collaboration inference pipeline.
Q: What is the significance of the multi-agent collaboration in your approach?
A: The multi-agent collaboration enables the system to leverage the strengths of each agent, resulting in more accurate and detailed captions.
Q: What is the potential impact of your work?
A: Our work has the potential to advance general-purpose understanding and inspire further exploration in multi-agent AI systems for complex multimodal tasks like AAC.
Q: How did you use NVIDIA technology in your work?
A: We used advanced NVIDIA computer technology, including the Taipei-1 supercomputer cluster and the NVIDIA DGX and OVX platforms, to accelerate our research and development.
