Siamese SIREN: Audio Compression with Implicit Neural Representations
Luca A. Lanzendörfer Roger Wattenhofer
Implicit Neural Representations (INRs) have emerged as a promising method for
representing diverse data modalities, including 3D shapes, images, and audio.
While recent research has demonstrated successful applications of INRs in image
and 3D shape compression, their potential for audio compression remains largely
unexplored. Motivated by this, we present a preliminary investigation into the
use of INRs for audio compression. Our study introduces Siamese SIREN, a novel
approach based on the popular SIREN architecture. Our experimental results
indicate that Siamese SIREN achieves superior audio reconstruction fidelity
while utilizing fewer network parameters compared to previous INR
architectures.