craffel
SIR is always INFINITY from the "mir_eval.separation.bss_eval_sources" function
Hi there, For the mir_eval.separation.bss_eval_sources(reference, estimate) function, SDR and SAR are always the same and SIR is always infinity...
I wonder, 1> besides reference, estimate, do I need to offer the noise file to the function? 2> the input matrices are not bounded to [-1,+1]. Is that Okay?
1> besides reference, estimate, do I need to offer the noisy to the function?
what do you mean by "the noisy"?
2> the input matrices are not bounded to [-1,+1]. Is that Okay?
I don't think this would cause a problem since the metric is mostly based on cross correlations.
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!
I don't know what your application is, but bss_eval is not perfectly suitable to evaluate denoising applications.
If you still want to do it, make sure to pass 2 signals to both reference and estimates, where true sources are [clean vocals, interfering noise] and estimates are [predicted vocals, predicted noise]
also see #256
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 thought it's equivalent to the bss toolbox, http://bass-db.gforge.inria.fr/bss_eval/, but with Python. Am I wrong??
yes, it is a reimplementation of these tools. So do you get different result with mir_eval than with matlab? If not, I guess your problem is related to the usage of bss_eval for your application.
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 4 parameters. [SDR, SIR, SNR, SAR] = bss crit(s target, e interf, (e noise), e artif)
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 function to get SDR, SAR, SIR then?
From the handbook, it takes 3 or 4 parameters. [SDR, SIR, SNR, SAR] = bss crit(s target, e interf, (e noise), e artif)
you need to compare with [SDR,SIR,SAR,perm]=bss_eval_sources(se,s)
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 function to get SDR, SAR, SIR then?
as mentioned here: you need 4 sources. The mixture is not needed because bss_eval is designed for linear mixtures only.
In your example you only have 3 sources. To make it work you need to provided the estimated noise as well. If your algorithm doesn't provide it, you can use mix-denoised.
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 is designed to evaluate source separation, right? Thanks.
Do we have the counterpart for bss_crit()?
see https://github.com/craffel/mir_eval/blob/master/mir_eval/separation.py#L803
What's the difference between these two? bss_eval_sources() and bss_crit()?
Please read here
And why you say that mir-eval is not perfect for denoising application? BSS toolbox is designed to evaluate source separation, right?
sure you can use it, just make sure you provide all sources
“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?