Diffusion Buffer for Online Generative Speech Enhancement
Abstract
Online Speech Enhancement was mainly reserved for predictive models. A key advantage of these models is that for an incoming signal frame from a stream of data, the model is called only once for enhancement. In contrast, generative Speech Enhancement models often require multiple calls, resulting in a computational complexity that is too high for many online speech enhancement applications. This work presents the Diffusion Buffer, a generative diffusion-based Speech Enhancement model which only requires one neural network call per incoming signal frame from a stream of data and performs enhancement in an online fashion on a consumer-grade GPU. The key idea of the Diffusion Buffer is to align physical time with Diffusion time-steps. The approach progressively denoises frames through physical time, where past frames have more noise removed. Consequently, an enhanced frame is output to the listener with a delay defined by the Diffusion Buffer, and the output frame has a corresponding look-ahead. In this work, we extend upon our previous work by carefully designing a 2D convolutional UNet architecture that specifically aligns with the Diffusion Buffer's look-ahead. We observe that the proposed UNet improves performance, particularly when the algorithmic latency is low. Moreover, we show that using a Data Prediction loss instead of Denoising Score Matching loss enables flexible control over the trade-off between algorithmic latency and quality during inference. The extended Diffusion Buffer equipped with a novel NN and loss function drastically reduces the algorithmic latency from 320 - 960 ms to 32 - 176 ms with an even increased performance. While it has been shown before that offline generative diffusion models outperform predictive approaches in unseen noisy speech data, we confirm that the online Diffusion Buffer also outperforms its predictive counterpart on unseen noisy speech data.
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 main contribution of this paper is the introduction of the Diffusion Buffer, a novel approach to online generative speech enhancement that effectively reduces algorithmic latency while improving performance on unseen noisy speech data. This work represents a significant advancement in the field of speech processing, combining innovative methodologies with practical applications.
Methodology
The paper introduces the Diffusion Buffer, a novel generative diffusion-based model for online speech enhancement that significantly reduces computational latency while maintaining performance. The methodology is well-structured, focusing on aligning physical time with diffusion time-steps, and employs a carefully designed 2D convolutional UNet architecture. The use of a Data Prediction loss function instead of the traditional Denoising Score Matching loss is a notable innovation, allowing for flexible control over latency and quality during inference. The proposed architecture and training strategy effectively address the challenges of real-time processing in speech enhancement.
Results & evaluation
The experiments are comprehensive, utilizing a well-defined dataset (EARS-WHAM-v2) and a robust evaluation framework. The results demonstrate that the proposed model outperforms traditional predictive models in various metrics, particularly under unseen noisy conditions. The paper provides a thorough analysis of the performance improvements achieved through the Diffusion Buffer, with clear comparisons to baseline models. However, the paper could benefit from additional ablation studies to further dissect the contributions of individual components.
Reproducibility
The authors mention that code will be released upon acceptance, which is a positive aspect for reproducibility. The methodology is described in sufficient detail, enabling other researchers to replicate the experiments. However, the lack of specific hyperparameter settings and training configurations in the main text may hinder complete reproducibility.
Limitations
One limitation is the reliance on a specific hardware configuration (consumer-grade GPUs), which may not generalize to all environments. Additionally, while the model shows promise in reducing latency, the trade-off between latency and quality could be further explored, particularly in real-world applications. The paper also does not address potential scalability issues when applied to larger datasets or more complex noise environments.
Broader impact
The proposed method has significant implications for real-time communication applications, such as video conferencing and VoIP, where clear audio is crucial. By improving speech enhancement capabilities on consumer-grade hardware, this research could enhance the user experience in various interactive platforms. The findings may also inspire further research into generative models for other audio processing tasks. The main contribution of this paper is the introduction of the Diffusion Buffer, a novel approach to online generative speech enhancement that effectively reduces algorithmic latency while improving performance on unseen noisy speech data. This work represents a significant advancement in the field of speech processing, combining innovative methodologies with practical applications.
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