Audio Dialogues: Dialogues dataset for audio and music understanding

Introducing Audio Dialogues: A Dataset for Audio Understanding via Interactive Dialogue

Overview of the Paper

This paper presents the introduction of Audio Dialogues, a notably expansive dataset designed to facilitate audio understanding through interactive dialogue. Audio Dialogues encompasses 163.8k samples, which span across general sounds and music, marking a significant advancement in audio dataset development. Unlike existing datasets, which predominantly focus on single-turn interactions such as audio captioning and question answering, Audio Dialogues is tailored to foster a deeper engagement with audio content via multi-turn dialogues and comparative questions about multiple audio inputs. The dataset is generated using a Large Language Model (LLM) leveraging existing caption annotations, applied in a prompting-based approach to produce rich and informative dialogues. This paper assesses the performance of current audio-augmented LLMs using this dataset, showcasing its potential in pushing forward the capabilities of such models in understanding and interacting based on audio content.

Data Generation Pipeline

The data generation pipeline detailed in the paper is a comprehensive procedure that begins with extracting descriptions from the strongly labeled AudioSet and MusicCaps datasets. These descriptions are then enriched with auditory features and used as prompts for GPT-4 to generate multi-turn dialogues. Additionally, the dataset includes a subset of interactions focused on comparing multiple audios together, employing CLAP embeddings to cluster similar or dissimilar audio samples for richer comparison-based questions and answers.

To ensure the quality of generated dialogues, the authors have implemented a data filtration mechanism. This filtering process eliminates responses with indications of uncertainty or low relevance, utilizing the correspondence between text and audio embeddings to gauge the accuracy and relevance of the dialogue content related to the original audio samples.

Contributions and Evaluation

The primary contributions of this paper include:

• The curation of a novel dataset, Audio Dialogues, poised to redefine benchmarks for audio understanding models in terms of engaging with multi-turn dialogues and complex audio interactions.
• A detailed data generation and filtration pipeline that serves as a blueprint for future endeavors in dataset creation for other modalities or applications.
• A thorough evaluation of existing audio-augmented LLMs using this new dataset, illustrating the nuanced challenges and potential advancements in model performance that Audio Dialogues will facilitate.

Evaluation of audio-augmented LLMs, including LTU, Qwen-Audio, and Audio Flamingo, on the Audio Dialogues dataset indicates notable improvements in model interaction capabilities post fine-tuning. The metrics used for evaluation underscore the complexity and applicability of the dataset in enhancing audio understanding models' performance.

Theoretical and Practical Implications

From a theoretical perspective, Audio Dialogues sets a new standard for interactive audio understanding, pushing the envelope on how dialogue systems can engage with auditory content. The dataset's structure invites nuanced exploration into context retention, reasoning, and engagement strategies over multiple turns of conversation, aspects digital assistants must negotiate effectively.

On a practical level, the broad application range for Audio Dialogues spans improving accessibility through enhanced auditory digital assistants for those with visual impairments, boosting the sophistication of audio content management systems, and refining interactive educational tools where audio plays a pivotal role.

Future Directions

Looking forward, the refinement and expansion of the Audio Dialogues dataset could include temporal grounding of dialogues to specific audio events and the integration of unsupervised learning approaches to scale data generation. Additionally, exploring human-in-the-loop feedback mechanisms during data generation and filtration could further enhance dataset quality, offering a promising direction for subsequent research efforts.

Conclusion

In summary, Audio Dialogues emerges as a valuable resource for researchers and practitioners aiming to advance audio understanding through LLMs and interactive audio models. By bridging the gap between current datasets and the intricate requirements of audio-based dialogue systems, this work paves the way for groundbreaking advancements in auditory information processing and interactive AI systems.