让短时傅里叶变换参数可学习，自动优化时频表示。
Learnable Adaptive Time-Frequency Representation via Differentiable Short-Time Fourier Transform
- 将STFT构建为可微形式，支持梯度优化参数
- 在模拟与真实数据上提升时频表示质量
- 可嵌入神经网络联合训练，适合信号处理任务
短时傅里叶变换（STFT）广泛用于非平稳信号分析，但其性能高度依赖参数设置，传统手动或启发式调参常导致次优结果。为此，我们提出一种统一的可微分STFT框架，实现参数的梯度优化，克服了传统方法依赖计算量大的离散搜索的局限。该方法可根据任意目标准则精细调整时频表示（TFR）。此外，该框架能无缝集成至神经网络，实现STFT参数与网络权重的联合优化。实验在模拟与真实数据上验证了所提可微STFT在增强TFR及提升下游任务性能方面的有效性。
原文摘要 · Abstract (English)
The short-time Fourier transform (STFT) is widely used for analyzing non-stationary signals. However, its performance is highly sensitive to its parameters, and manual or heuristic tuning often yields suboptimal results. To overcome this limitation, we propose a unified differentiable formulation of the STFT that enables gradient-based optimization of its parameters. This approach addresses the limitations of traditional STFT parameter tuning methods, which often rely on computationally intensive discrete searches. It enables fine-tuning of the time-frequency representation (TFR) based on any desired criterion. Moreover, our approach integrates seamlessly with neural networks, allowing joint optimization of the STFT parameters and network weights. The efficacy of the proposed differentiable STFT in enhancing TFRs and improving performance in downstream tasks is demonstrated through experiments on both simulated and real-world data.
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