Comments (3)
Xiaobin-Rong
commented on June 11, 2024
1
你说得对，RNNoise原文把 FFT 和 IFFT 也算进运算量里了。这样我的对比确实存在不严谨的地方，感谢指出。但 RNNoise 只是 GTCRN 的对比对象之一，GTCRN 在 VCTK-DEMAND 数据集上的 PESQ=2.87 是客观成立的。
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Xiaobin-Rong
commented on June 11, 2024
RNNoise的 MACs，使用的是 DeepFilterNet2 的 Table 1 中提供的数据。
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jingxuan9862
commented on June 11, 2024
https://jmvalin.ca/papers/rnnoise_mmsp2018.pdf 原文中给出的数据，整体的算法复杂度为40Mflops，单网络部分是17.5Mflops , 换成MACs的话是8.75M MACs
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Related Issues (14)
- can you share the paper, I can not find it in the internet HOT 7
- could you share the paper link? Thanks HOT 12
- 请问您排查到onnxsim.simplify导出时的问题所在了吗 HOT 3
- 提问，关于SFE模块使用nn.unfold的目的。 HOT 3
- 请教一下Temporal Recurrent Attention的作用 HOT 2
- 请问，训练代码在之后会开源吗 HOT 3
- 请问，如果想扩展一下，用计算量换模型的性能，应该朝哪个方向调参？ HOT 4
- Is this paper available for download online? HOT 1
- LIcense? HOT 1
- 请问，模型低信噪比场景听感难受问题 HOT 4
- Loss gets nan HOT 14
- 48k_training HOT 1
- GTCRN的look-ahead是70ms吗 HOT 2
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