Implementation of D3Net about dnn-based_source_separation HOT 11 OPEN

@lyghter
I'm sorry I couldn't help you. Now, I'm sharing the scripts and results in egs/musdb18/d3net.

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I'm not sure of # channels before frequency concatenation.
The # of channels depends on the growth rate and # of D2 blocks.
I added bottleneck convolution so that both frequency bands have the same channels.

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What needs to be fixed

• multi dilated convolution
  • timing of batch normalization
• # of output channels of D2 block
• order of D3 block and downsampling layer in Down D3 block
• upsampling layer

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Now, I updated D3Net architecture.

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Hello, @tky823. I participate in the Music Demixing Challenge (4th place on leaderboard A). I suggest you write a training script for D3Net and join a team with me.

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Hi, @lyghter. I'm now writing the training code. I am not sure if it will be available soon, but I plan to add it.

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The challenge will end on July 31st. If you write the training code this month, I will try to train the model and use it in my solution. Sony's nnabla implementation has too slow inference on CPU. It cannot be used in the challenge.

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I invite you to join my team and suggest you keep the new code private until the end of the challenge.

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I am currently 4th on Leaderboard A and 5th on Leaderboard B. Top-3 from A and top-3 from B will receive prizes.

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I'm not sure how my implementation of D3Net will work, so I don't know if I'll be able to participate anytime soon. If I can help, I will join your team. I work on other tasks for about a week. Maybe I will be able to join after that.

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Hello @tky823
Take a look at this. It looks like this repo contains pytorch implementation of D3Net and training code. I just found it and haven't tried it yet.

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