cocosci

Hi Kevin, I saw that tutorial... But I didn't see any specific part of the code that deals with parallelization... In my case, without any gpu specification, all 8 gpus...

Say, for d in ['/gpu:5','/gpu:6']: with tf.device(d): a = tf.Variable(tf.truncated_normal([200, 500], dtype=tf.float32)) b = tf.Variable(tf.truncated_normal([500, 300], dtype=tf.float32)) b2 = tf.Variable(tf.truncated_normal( [300, 400], dtype=tf.float32)) b3 = tf.Variable(tf.truncated_normal( [400, 1000], dtype=tf.float32)) b4...

The background noise that is added to the clean signal. For each signal, I added 10 types of noise.

According to the document, reference_sources : matrix containing true sources estimated_sources : matrix containing estimated sources so how will the toolbox find the noise I added and calculate SIR? Thanks!

Yes, I am trying to evaluate my source separation/speech enhancement/ or denoising algorithm. I thought it's equivalent to the bss toolbox, http://bass-db.gforge.inria.fr/bss_eval/, but with Python. Am I wrong??

I haven't tried the matlab version, but I did download it. For that Matlab function, it takes more parameters than the counterpart here. From the handbook, it takes 3 or...

In my case, say I have a clean file, "clean.wav"; noise, "noise.wav", (and the mixed.wav) and the denoised file, "denoised.wav". They are with the same length. How to use your...

Do we have the counterpart for bss_crit()? What's the difference between these two? bss_eval_sources() and bss_crit()? And why you say that mir-eval is not perfect for denoising application? BSS toolbox...

Thanks for your in-time and patient explanation!

“If your algorithm doesn't provide it, you can use mix-denoised.” What do you mean by this? Can I just use mixed_data - predicted_source to get predicted_noise?