SVD features processing about ismir2018-revisiting-svd HOT 7 OPEN

Hi :)
So the idea behind this singing voice detection system is to determine whether there is a singing voice or not for each input segment (which is 1.6 seconds of audio). This means that if we want to analyze a longer audio file, we need to divide it into 1.6-second long segments. Step 3 is where I do this : just looping through the time dimension of the melspectrogram and cutting them into 1.6-second segments. At the end, there are 13475 segments of size (80, 115).

After running the prediction, you will be able to identify at which frames the prediction is 0 or 1. From here, you need to convert frames to seconds using the function like frames_to_time.

I hope this helps. Let me know if there are more questions!

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@simonefrancia Sure, but I think 100ms is way too short to determine if the input contains singing voice or not. The big characteristic to detect is vibrato and 100ms doesn't seem long enough to detect vibrato. Typically the input is around 1 second and it makes sense in human's perspective as well. Feel free to try :)

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Hi @kyungyunlee, thanks for your response.
So is assigned one label for every 1.6 seconds?
Because what I don't understand is why if I have an audio with duration 194 seconds ( that is the duration of the example above) , as result of the preprocessing I have 13475 segments of 1.6 second for each one.
Thanks

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@simonefrancia Hi, yes it's single binary label for 1.6 seconds, but there is overlap during training so there will be more than 194/1.6 segments, for instance.

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Ok. But is it possible to be more precise, for example if I want to have a prediction for every 100ms ?
is it possible to do this, only changing some config of training?
Thank you

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@kyungyunlee according to you, what is the smallest duration of segment we can use for training?
Thanks

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I am not sure, since I haven’t tried using shorter input. I think at this point you have to define it in terms of your task goal

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