Tilt loss: a perceptual loss function to improve music packet loss concealment

Recent advancements in deep learning have paved the way for novel approaches to the problem of Packet Loss Concealment (PLC) in networked music performance systems. However, deep neural networks may have large inference times and, therefore, violate the strict temporal requirements of PLC methods for such systems. A promising avenue in this space lies in the exploration of the loss function used to train the network. Indeed, loss functions have a direct impact on the latent representation learned by the model during the training process without any additional cost at inference time. In this paper, we present the Tilt Loss, a perceptual loss function, i.e., a loss function that allows the model trained with it to have performances that correlate with the human evaluation. The proposed method was able to outperform the current state-of-the-art in PLC methods according to human evaluation, albeit the model exhibited unsatisfactory performance with unpitched instruments. Furthermore, our study pinpoints the need for novel objective metrics specifically tailored for the PLC case.