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I'm trying to use eval_mus_track function of the museval package to evaluate my audio source separation model. The model I'm evaluating was trained to predict vocals and the results are similar to the actual vocals but the evaluation metrics such as SDR are negative.

Below is my function for generating the metrics:

def estimate_and_evaluate(track):

    #track.audio is stereo therefore we predict each channel separately
    vocals_predicted_channel_1, acompaniment_predicted_channel_1, _ = model_5.predict(np.squeeze(track.audio[:, 0]))
    vocals_predicted_channel_2, acompaniment_predicted_channel_2, _  = model_5.predict(np.squeeze(track.audio[:, 1])                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            )


    vocals = np.squeeze(np.array([vocals_predicted_channel_1.wav_file, vocals_predicted_channel_2.wav_file])).T
    accompaniment = np.squeeze(np.array([acompaniment_predicted_channel_1.wav_file, acompaniment_predicted_channel_2.wav_file])).T
    estimates = {
        'vocals': vocals,
        'accompaniment': accompaniment
    }

    scores = museval.eval_mus_track(track, estimates)
    print(scores)

The metric values I get are:

vocals          ==> SDR:  -3.776  SIR:   4.621  ISR:  -0.005  SAR: -30.538  
accompaniment   ==> SDR:  -0.590  SIR:   1.704  ISR:  -0.006  SAR: -16.613 

The above result doesn't make sense because first of all, accompaniment prediction is pure noise as this model was trained for vocals but it gets a higher SDR. The second reason is the predicted vocals have a very similar graph to the actual ones but still gets a negative SDR value! In the following graphs, the top one is the actual sound and the bottom one is the predicted source:

Channel 1: actual vocals channel 1 predicted vocals channel 1

Channel 2: actual vocals channel 2 predicted vocals channel 2 I tried to shift the predicted vocals as mentioned here but the result got worse.

Any idea what's causing this issue?

Thanks for your time

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