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阶段预测问题:转置卷积&stft(短时间傅里叶变换)

Hinash88.

螺纹启动器

Hinash88.

加入了2020年7月29日
1
"Human perception is also highly sensitive to discontinuities and irregularities in periodic waveforms. Figure 1 shows that when the stride of the frames does not exactly equal a waveform’s periodicity, the alignment (phase) of the two precesses over time. This condition is assured as at any time there are typically many different frequencies in a given signal. This is a challenge for a synthesis network, as it must learn all the appropriate frequency and phase combinations and activate them in just the right combination to produce a coherent waveform. This phase precession is exactly the same phenomena observed with a short-time Fourier transform (STFT), which is composed of strided filterbanks just like convolutional networks. Phase precession also occurs in situations where filterbanks overlap (window or kernel size < stride)."

有人可以提供任何理论,了解STFT和卷积中发现的这种现象吗?

附件

S.

豆子

加入了2020年8月17日
51.
短时间傅里叶变化(STFT)是一种突触的相关变化,用于决定符号附近区域的正弦复发和阶段物质随着时间的变化而变化。[1]实际上讲,用于图案的方法是将更加抽出的时间信号分配成等效长度的较短部分,然后在每个较短的部分上独立地改变傅里叶改变。这揭示了每个较短部分的傅里叶范围。在那个点时,作为规则将改变的光谱作为时间的分量绘制,称为频谱图或级联图。
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