wav2shape：聆听鼓机的形状
wav2shape: Hearing the Shape of a Drum Machine
摘要：
从几个波形例子中解开和恢复物理属性，如形状和材料，是音频信号处理中具有挑战性的逆问题，在音乐声学和结构工程中有许多应用。我们建议通过结合时频分析和监督机器学习来解决这个问题。我们首先使用函数变换方法合成声音数据集。然后，我们表示每个谐振声音的时不变散射变换系数，并制定了一个深卷积神经网络的多维回归的谐振器的参数估计。我们插值散射系数在鼓的表面作为潜在的缺失数据的替代品，并研究了神经网络的响应插值样本。最后，我们重新合成鼓的声音从散射系数，因此铺平了道路，对鼓的声音，其潜变量是物理上可解释的深层生成模型。
英文摘要：
Disentangling and recovering physical attributes, such as shape and material, from a few waveform examples is a challenging inverse problem in audio signal processing, with numerous applications in musical acoustics as well as structural engineering. We propose to address this problem via a combination of time--frequency analysis and supervised machine learning. We start by synthesizing a dataset of sounds using the functional transformation method. Then, we represent each percussive sound in terms of its time-invariant scattering transform coefficients and formulate the parametric estimation of the resonator as multidimensional regression with a deep convolutional neural network. We interpolate scattering coefficients over the surface of the drum as a surrogate for potentially missing data, and study the response of the neural network to interpolated samples. Lastly, we resynthesize drum sounds from scattering coefficients, therefore paving the way towards a deep generative model of drum sounds whose latent variables are physically interpretable.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
