For the data accessed from clean-360 datasetd, did you simply add up these audios together according to the value of k?

No, for the time-domain signals there were a few important steps involved to sample the data:
We chose random choice of k speakers from the dataset. Each speaker has 5 utterances. These were trimmed so that silence in the beginning and the end of the utterances is removed using a voice activity detection method (I used this one) The utterances were appended in random order. The concatenated utterances were padded with zeros at the end so that all speakers have the same recording length. Finally the utterances were mixed to get the final output

Example:
    A..C = Speaker Id
    1..3 = Utterance Id

    Before padding:
        track1: |---A3---||--A2--||-----A1-----|
        track2: |---B2---||-B1-||--B3--|
        track3: |-------C1------||-C3-||C2|

    After padding:
        track1: |---A3---||--A2--||--A1|
        track2: |---B2---||-B1-||--B3--|
        track3: |-------C1------||-C3-||

  frame count:  |333333333333333333333333
        k: 3

Before the transformation to time-frequency matirx, what else did you do to form the wav files for training?

the mixing was applied by normalizing each track to have the same SNR to each other and the final mix was peak normalized

I noticed you mentioned peak normalization in the article. I'm new to the audio-processing field and I wonder how that works. What value is set to be the maximum value so that peaks above it are normalized?

yes out /= np.max(out, axis=0) does it ;-)

Uh oh!

Is the training data available? #6

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