ImmersiveFlow: Stereo-to-7.1.4 spatial audio generation with flow matching
Abstract
Immersive spatial audio has become increasingly critical for applications ranging from AR/VR to home entertainment and automotive sound systems. However, existing generative methods remain constrained to low-dimensional formats such as binaural audio and First-Order Ambisonics (FOA). Binaural rendering is inherently limited to headphone playback, while FOA suffers from spatial aliasing and insufficient resolution for high-frequency. To overcome these limitations, we introduce ImmersiveFlow, the first end-to-end generative framework that directly synthesizes discrete 7.1.4 format spatial audio from stereo input. ImmersiveFlow leverages Flow Matching to learn trajectories from stereo inputs to multichannel spatial features within a pretrained VAE latent space. At inference, the Flow Matching model predicted latent features are decoded by the VAE and converted into the final 7.1.4 waveform. Comprehensive objective and subjective evaluations demonstrate that our method produces perceptually rich sound fields and enhanced externalization, significantly outperforming traditional upmixing techniques. Code implementations and audio samples are provided at: https://github.com/violet-audio/ImmersiveFlow.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The paper presents ImmersiveFlow, a pioneering framework for stereo-to-7.1.4 spatial audio generation, significantly advancing the field of immersive audio processing. The innovative use of flow matching within a VAE context demonstrates a strong potential for improving audio quality in various applications, marking a substantial contribution to the audio machine learning landscape.
Methodology
The proposed ImmersiveFlow framework introduces a novel approach to generating high-channel spatial audio directly from stereo input using Conditional Flow Matching (CFM) and a pretrained Variational Autoencoder (VAE). This end-to-end generative model effectively addresses the limitations of traditional upmixing techniques by leveraging flow-based generative modeling to learn the mapping from stereo audio to immersive 7.1.4 audio. The methodology is well-structured, employing a transformer-based architecture for the velocity field, which enhances the model's ability to capture complex spatial audio distributions.
Results & evaluation
The paper presents a comprehensive evaluation of ImmersiveFlow through both objective and subjective metrics. The dataset, while internally constructed, is robust, comprising 100 professionally mixed tracks, and the evaluation metrics are appropriate for assessing generative audio quality. The results indicate that ImmersiveFlow performs comparably to commercial upmixing tools while demonstrating superior generative capabilities, particularly in capturing spatial statistical structures.
Reproducibility
The paper provides sufficient details regarding the implementation, including the architecture of the VAE and the flow matching model, as well as the training setup. The availability of code and audio samples on GitHub enhances reproducibility, allowing other researchers to validate and build upon the work.
Limitations
One limitation noted is the potential loss of high-frequency detail during the VAE encoding process, which may affect the overall quality of the generated audio. Additionally, the internal dataset, while extensive, may not encompass the full diversity of audio content found in real-world scenarios, potentially limiting the generalizability of the model.
Broader impact
The development of ImmersiveFlow has significant implications for various applications in immersive media, including AR/VR, home entertainment, and automotive sound systems. By enabling high-quality spatial audio generation from stereo sources, this work could enhance user experiences in these domains, paving the way for more realistic and engaging audio environments. The paper presents ImmersiveFlow, a pioneering framework for stereo-to-7.1.4 spatial audio generation, significantly advancing the field of immersive audio processing. The innovative use of flow matching within a VAE context demonstrates a strong potential for improving audio quality in various applications, marking a substantial contribution to the audio machine learning landscape.
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