FiPA-SR - FiLM-Conditioned Perceptually Informed Audio Super-Resolution

Audio bandwidth extension aims to reconstruct missing high-frequency content from bandlimited signals. This paper proposes FiPA-SR, a GAN-based perceptual architecture capable of handling different input bandwidths within a single model. Building upon the previous \(\textrm{AEROMamba}_\textrm{P}\) framework, the proposed model incorporates FiLM layers to adapt the reconstruction process according to the respective bandwidth. Experiments on the MUSDB dataset show that FiPA-SR outperforms the state-of-the-art AudioSR model across 8, 20, and 32 kHz input sampling rates. Moreover, the proposed architecture uses approximately 3\(\times\) less GPU memory and performs inference more than 60\(\times\) faster than the diffusion-based baseline.