A method of permutation invariant training of a neural network model for separating audio source signals from a mixed signal comprising: generating , by the model, output layers from the mixed signal, the output layers being estimates of the audio source signals; determining an assignment order of labels to be assigned to the output layers, individual labels being associated with individual audio source signals and the assignment order being based, at least in part, on a minimum total deviation score, wherein the determining of the assignment order of labels to be assigned to the output layers comprises: calculating a set of pairwise deviations between the individual output layers and the audio source signals; calculating total deviation scores for possible assignment orders, wherein a total deviation score for a possible assignment order includes a summation of pairwise deviations between respective pairs of the individual output layers and the individual audio source signals to which the individual output layers correspond according to a possible assignment order; and selecting, from the possible assignment orders, the assignment order based at least in part on a total deviation score associated with the assignment order being a minimum total deviation score among the total deviation scores; optimizing model parameters of the model to minimize the minimum total deviation score of the determined assignment order. generating , by the model, output layers from the mixed signal, the output layers being estimates of the audio source signals; determining an assignment order of labels to be assigned to the output layers, individual labels being associated with individual audio source signals and the assignment order being based, at least in part, on a minimum total deviation score, wherein the determining of the assignment order of labels to be assigned to the output layers comprises: calculating a set of pairwise deviations between the individual output layers and the audio source signals; calculating total deviation scores for possible assignment orders, wherein a total deviation score for a possible assignment order includes a summation of pairwise deviations between respective pairs of the individual output layers and the individual audio source signals to which the individual output layers correspond according to a possible assignment order; and selecting, from the possible assignment orders, the assignment order based at least in part on a total deviation score associated with the assignment order being a minimum total deviation score among the total deviation scores; calculating a set of pairwise deviations between the individual output layers and the audio source signals; calculating total deviation scores for possible assignment orders, wherein a total deviation score for a possible assignment order includes a summation of pairwise deviations between respective pairs of the individual output layers and the individual audio source signals to which the individual output layers correspond according to a possible assignment order; and selecting, from the possible assignment orders, the assignment order based at least in part on a total deviation score associated with the assignment order being a minimum total deviation score among the total deviation scores; optimizing model parameters of the model to minimize the minimum total deviation score of the determined assignment order. <EOS>