arXiv:2412.04746v1 Announce Type: cross
Abstract: Modern music retrieval systems often rely on fixed representations of user preferences, limiting their ability to capture users’ diverse and uncertain retrieval needs. To address this limitation, we introduce Diff4Steer, a novel generative retrieval framework that employs lightweight diffusion models to synthesize diverse seed embeddings from user queries that represent potential directions for music exploration. Unlike deterministic methods that map user query to a single point in embedding space, Diff4Steer provides a statistical prior on the target modality (audio) for retrieval, effectively capturing the uncertainty and multi-faceted nature of user preferences. Furthermore, Diff4Steer can be steered by image or text inputs, enabling more flexible and controllable music discovery combined with nearest neighbor search. Our framework outperforms deterministic regression methods and LLM-based generative retrieval baseline in terms of retrieval and ranking metrics, demonstrating its effectiveness in capturing user preferences, leading to more diverse and relevant recommendations. Listening examples are available at tinyurl.com/diff4steer.
Diff4Steer: A Novel Generative Retrieval Framework for Music Exploration
Modern music retrieval systems often struggle to capture the diverse and uncertain retrieval needs of users. This limitation is due to their reliance on fixed representations of user preferences. To overcome this challenge, a team of researchers has introduced Diff4Steer, a highly innovative generative retrieval framework that aims to synthesize diverse seed embeddings from user queries, representing potential directions for music exploration.
Unlike deterministic methods that map user queries to a single point in embedding space, Diff4Steer employs lightweight diffusion models to provide a statistical prior on the target modality, which in this case is audio. This approach effectively captures the uncertainty and multi-faceted nature of user preferences, allowing for a more nuanced understanding of their musical tastes.
One of the standout features of Diff4Steer is its ability to be steered by image or text inputs, in addition to traditional audio queries. This unique functionality enables a more flexible and controllable music discovery experience, combined with advanced nearest neighbor search techniques. By incorporating different modalities, the framework allows users to explore music based on visual cues or textual descriptions, bridging the gap between different sensory experiences.
The use of diffusion models in Diff4Steer holds promise for the wider field of multimedia information systems. The concept of using statistical priors to capture uncertainty and leverage diverse data sources is not only relevant to music retrieval but can also be applied to other domains where unstructured multimedia data is prevalent. By expanding the scope of this framework beyond music, researchers and practitioners can explore its potential in analyzing and retrieving multimedia content such as images, videos, and text.
Furthermore, Diff4Steer’s integration of artificial reality, augmented reality, and virtual realities can enhance the music exploration experience. By incorporating these technologies, users can visualize and interact with music in immersive environments, adding a new layer of engagement and sensory stimulation. This multidisciplinary approach opens up avenues for cross-pollination between the fields of multimedia information systems and virtual reality, leading to the development of more immersive and interactive music retrieval systems.
In terms of performance, Diff4Steer demonstrates its effectiveness in capturing user preferences and generating more diverse and relevant recommendations. It outperforms deterministic regression methods and a generative retrieval baseline, showcasing the superiority of its statistical approach. By providing a wider range of music options to users, Diff4Steer has the potential to enhance music discovery and foster a deeper connection between listeners and their preferred genres.
In conclusion, Diff4Steer offers a groundbreaking solution to the limitations of traditional music retrieval systems. By incorporating lightweight diffusion models and the ability to be steered by different modalities, it provides a more comprehensive understanding of user preferences and enables a more flexible and controllable music exploration experience. Its implications extend beyond the field of music, opening up new possibilities in multimedia information systems, artificial reality, augmented reality, and virtual realities.