轻量级神经网络有效抑制户外录音风噪，适合移动设备实时处理。
Low-Complexity Neural Wind Noise Reduction for Audio Recordings
- 基于风噪频谱特性设计轻量级单通道神经网络
- 仅249K参数，计算量约73MHz，性能媲美顶尖模型
- 适合嵌入式与移动端音频应用，兼顾效果与效率
风噪严重降低户外音频录制质量，但在资源受限设备上实现实时抑制仍具挑战。本文提出一种低复杂度单通道深度神经网络，利用风噪的频谱特征进行降噪。实验结果表明，该方法性能可与当前最先进的低复杂度ULCNet模型相媲美。所提模型仅含249K参数，计算量约为73MHz，适用于嵌入式及移动音频应用场景。
原文摘要 · Abstract (English)
Wind noise significantly degrades the quality of outdoor audio recordings, yet remains difficult to suppress in real-time on resource-constrained devices. In this work, we propose a low-complexity single-channel deep neural network that leverages the spectral characteristics of wind noise. Experimental results show that our method achieves performance comparable to the state-of-the-art low-complexity ULCNet model. The proposed model, with only 249K parameters and roughly 73 MHz of computational power, is suitable for embedded and mobile audio applications.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。