标题： FCPE：一种快速基于上下文的音高估计模型 显示英文标题

标题： FCPE: A Fast Context-based Pitch Estimation Model

摘要： 音高估计（PE）在单音音频中对于MIDI转录和歌唱语音转换（SVC）至关重要，但现有方法在噪声环境下性能显著下降。 在本文中，我们提出了FCPE，一种基于上下文的快速音高估计模型，该模型采用Lynx-Net架构结合深度可分离卷积，以有效捕捉梅尔频谱图特征，同时保持低计算成本和强大的抗噪能力。 实验表明，我们的方法在MIR-1K数据集上实现了96.79%的原始音高准确率（RPA），与最先进的方法相当。 实时因子（RTF）在单个RTX 4090 GPU上为0.0062，这在效率上显著优于现有算法。 代码可在https://github.com/CNChTu/FCPE获取。 显示英文摘要

摘要： Pitch estimation (PE) in monophonic audio is crucial for MIDI transcription and singing voice conversion (SVC), but existing methods suffer significant performance degradation under noise. In this paper, we propose FCPE, a fast context-based pitch estimation model that employs a Lynx-Net architecture with depth-wise separable convolutions to effectively capture mel spectrogram features while maintaining low computational cost and robust noise tolerance. Experiments show that our method achieves 96.79\% Raw Pitch Accuracy (RPA) on the MIR-1K dataset, on par with the state-of-the-art methods. The Real-Time Factor (RTF) is 0.0062 on a single RTX 4090 GPU, which significantly outperforms existing algorithms in efficiency. Code is available at https://github.com/CNChTu/FCPE.