In this paper, we propose a differentiable version of the short-time Fourier transform (STFT) that allows for gradient-based optimization of the hop length or the frame temporal position by making these parameters continuous. Our approach provides improved control over the temporal positioning of frames, as the continuous nature of the hop length allows for a more finely-tuned optimization. Furthermore, our contribution enables the use of optimization methods such as gradient descent, which are more computationally efficient than conventional discrete optimization methods. Our differentiable STFT can also be easily integrated into existing algorithms and neural networks. We present a simulated illustration to demonstrate the efficacy of our approach and to garner interest from the research community.

翻译：本文提出了一种可微版本的短时傅里叶变换（STFT），通过将跳跃长度或帧时域位置设为连续参数，实现了对二者的梯度优化。该方法中跳跃长度的连续性使得帧时域定位更为精细可控，从而显著提升了优化的精准度。此外，我们的贡献使得梯度下降等优化方法得以应用，相比传统离散优化方法具有更高的计算效率。该可微STFT可轻松集成至现有算法与神经网络中。我们通过仿真实验验证了该方法的有效性，以期引起研究领域的关注。