AUGMENTED TIME-FREQUENCY MASK ESTIMATION IN CLUSTER-BASED SOURCE SEPARATION ALGORITHMS

Time-frequency mask estimation with various clustering approaches has proven effective in solving the audio source separation problem. In this framework, the time-frequency bins of the mixture spectrogram are represented in a high-dimensional embedding space, where various methods can be applied to group the embedded points to calculate either hard or soft source assignments and subsequently the time-frequency masks. However, the mismatch between the assignment algorithm during the training and inference phases in majority of the current approaches leads to a suboptimal solution, because the assignment objective that is used during the training (e.g. ideal binary mask) is not the same as the one used during the inference phase (e.g. k-means clustering). We propose a method to reduce the mismatch between these two conditions where the source embedding is trained such that the source assignment during training and inference phases results in similar outcomes. Our results show that matching the source assignment during training-and inference-phase results in more accurate and consistent mask estimation in the inference phase which significantly improves the source separation accuracy for various hard and soft clustering methods.