The iN-NMF framework can be extended to handle more complex audio representations by incorporating phase information and enforcing non-negative constraints on the learned functions. To include phase information, the implicit neural representations for the spectral templates and activations can be augmented to include phase components. This can be achieved by either concatenating the phase information with the magnitude information or by designing a separate neural network to model the phase. By integrating phase information, the iN-NMF model can capture the complete spectro-temporal characteristics of the audio signals, enabling more accurate reconstructions and separations.
In terms of non-negative constraints, the implicit neural representations can be modified to ensure that the learned functions output non-negative values. This can be achieved by incorporating appropriate activation functions in the neural networks, such as ReLU or softplus, which inherently enforce non-negativity. By imposing non-negative constraints, the iN-NMF framework can maintain the interpretability and sparsity of the learned representations, which is crucial for tasks like source separation and audio analysis.