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Multichannel Long-Term Streaming Neural Speech Enhancement for Static and Moving Speakers
핵심 개념
The author proposes an online SpatialNet for long-term streaming speech enhancement, utilizing variants like masked SA, Retention, and Mamba. A short-signal training plus long-signal fine-tuning strategy is introduced to improve length extrapolation ability.
초록
The content introduces the online SpatialNet for speech enhancement in both static and moving speaker scenarios. Three variants are developed using different networks like masked SA, Retention, and Mamba. The proposed short-signal training plus long-signal fine-tuning strategy enhances the network's performance for processing very long audio streams efficiently.
The article discusses the challenges of processing long signals in speech enhancement applications and presents a novel approach to address these issues. By extending the offline SpatialNet to an online network with modified convolutional layers and cross-band blocks, the authors achieve outstanding performance for both static and moving speakers. The study also compares different training strategies and models to highlight the effectiveness of the proposed method.
Key points include:
- Introduction of online SpatialNet for speech enhancement.
- Development of three variants: masked SA, Retention, and Mamba.
- Proposal of a short-signal training plus long-signal fine-tuning strategy.
- Comparison with baseline methods to showcase superior performance.
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소스 방문
arxiv.org
Multichannel Long-Term Streaming Neural Speech Enhancement for Static and Moving Speakers
통계
"64-s signals are tested in our experiments."
"For moving speakers, the start location and moving direction are randomly sampled."
"The sampling rate is 8 kHz."
"STFT coefficients of target speech are estimated from h[f, t, :] by an output linear layer."
인용구
"No matter how long the training utterances are, the same amount of training utterances and the same learning rate schedule are used."
"The proposed ST+LF training strategy first learns short-term speech enhancement knowledge and then extends it to long-term processing."
"Mamba performs better for both static and moving speaker cases than MSA and Retention."
더 깊은 질문
How does the proposed online SpatialNet compare to other state-of-the-art speech enhancement methods
The proposed online SpatialNet outperforms other state-of-the-art speech enhancement methods in several aspects. Firstly, it extends the capabilities of the previously proposed offline SpatialNet by introducing online networks with linear inference complexity concerning signal length. This advancement allows for efficient processing of very long audio streams, a crucial feature for real-time applications. Additionally, the three variants of oSpatialNet - masked SA, Retention, and Mamba - offer different approaches to learning temporal-spatial information and have shown outstanding performance in both static and moving speaker scenarios. The comparison with baseline methods like EaBNet and McNet demonstrates superior speech enhancement results across various metrics such as SI-SDR, NB-PESQ, ESTOI, and SDR.
What implications could the length extrapolation ability have on real-world applications of speech enhancement technology
The length extrapolation ability of speech enhancement technology has significant implications for real-world applications. In scenarios where continuous or long audio streams need to be processed in real-time (e.g., video conferencing, live broadcasts), having models that can effectively handle signals longer than those used during training is crucial. The ability to maintain high-quality denoising and dereverberation over extended periods ensures consistent performance regardless of signal duration. Without robust length extrapolation capabilities, there could be degradation in speech quality or even system failure when dealing with lengthy input signals.
How might advancements in neural networks impact future developments in multichannel speech denoising
Advancements in neural networks are poised to drive future developments in multichannel speech denoising by offering more sophisticated modeling techniques and improved performance outcomes. With innovations like self-attention mechanisms (as seen in MSA and Retention variants) and structured state space sequence models (like Mamba), researchers can leverage these advanced architectures to enhance spatial information processing further. These advancements enable better discrimination between target speech and interferences while maintaining computational efficiency for streaming applications.
Additionally, the introduction of strategies like short-signal training plus long-signal fine-tuning provides a practical approach to improving length extrapolation abilities without significantly increasing training complexity or time requirements.
Overall, as neural network technologies continue to evolve, we can expect more effective solutions for multichannel speech denoising that deliver superior results across diverse use cases within the field of audio processing.
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