MeanFlow-TSE: One-Step Generative Target Speaker Extraction with Mean Flow
Abstract
Target speaker extraction (TSE) aims to isolate a desired speaker's voice from a multi-speaker mixture using auxiliary information such as a reference utterance. Although recent advances in diffusion and flow-matching models have improved TSE performance, these methods typically require multi-step sampling, which limits their practicality in low-latency settings. In this work, we propose MeanFlow-TSE, a one-step generative TSE framework trained with mean-flow objectives, enabling fast and high-quality generation without iterative refinement. Building on the AD-FlowTSE paradigm, our method defines a flow between the background and target source that is governed by the mixing ratio (MR). Experiments on the Libri2Mix corpus show that our approach outperforms existing diffusion- and flow-matching-based TSE models in separation quality and perceptual metrics while requiring only a single inference step. These results demonstrate that mean-flow-guided one-step generation offers an effective and efficient alternative for real-time target speaker extraction. Code is available at https://github.com/rikishimizu/MeanFlow-TSE.
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 MeanFlow-TSE, a novel one-step generative framework for target speaker extraction that significantly improves performance and efficiency over existing methods. The comprehensive evaluation of the methodology and results highlights its potential impact on real-time audio applications, marking a meaningful advancement in the field.
Methodology
The proposed MeanFlow-TSE framework introduces a novel one-step generative approach to target speaker extraction (TSE) by leveraging mean-flow objectives. This method stands out by eliminating the need for iterative refinement, which is common in existing models, thus addressing latency issues in real-time applications. The integration of mixing ratio-aware training and adaptive weight loss strategies demonstrates a thoughtful approach to improving model performance while maintaining computational efficiency. The use of curriculum learning for transitioning training objectives is a sophisticated technique that enhances stability and performance.
Results & evaluation
The experiments conducted on the Libri2Mix dataset are comprehensive, comparing MeanFlow-TSE against a variety of state-of-the-art models. The reported results indicate significant improvements in separation quality and perceptual metrics, showcasing the effectiveness of the proposed method. The use of multiple evaluation metrics (SI-SDR, PESQ, ESTOI, etc.) provides a well-rounded assessment of model performance, reinforcing the claims made by the authors. The thorough benchmarking against existing models adds credibility to the findings.
Reproducibility
The paper provides sufficient implementation details, including the architecture, training configurations, and optimization strategies, which are essential for reproducibility. The availability of the code on GitHub further enhances the potential for other researchers to replicate the results. However, the absence of a demo URL limits immediate accessibility to the model's capabilities.
Limitations
While the paper presents a robust framework, it does not address potential limitations in handling diverse acoustic environments or the scalability of the model to multi-channel scenarios. Additionally, the reliance on the Libri2Mix dataset may limit the generalizability of the findings to other real-world applications.
Broader impact
The advancements in TSE presented in this work have significant implications for applications in automatic speech recognition, hearing aids, and telecommunications, particularly in noisy environments. The efficiency of the MeanFlow-TSE framework could facilitate real-time applications, making it a valuable contribution to the field of audio processing. The paper presents MeanFlow-TSE, a novel one-step generative framework for target speaker extraction that significantly improves performance and efficiency over existing methods. The comprehensive evaluation of the methodology and results highlights its potential impact on real-time audio applications, marking a meaningful advancement in the field.
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