Senyuan Fan will Exploring Implicit Neural Audio Representation
Date:
Fri, 05/24/2024 - 10:30am - 12:00pm
Location:
CCRMA Seminar Room
Event Type:
Hearing Seminar Abstract
Audio coding can be implemented through concise neural codes employing end-to-end neural networks. While this method has shown promise in achieving high compression ratios, the reconstructed audio quality frequently suffers. Implicit neural representations (INRs) have demonstrated remarkable capability in modeling various complex signals, spanning from radiance fields and 3D shapes to images, videos, and audio. In contrast to end-to-end neural audio codecs, INRs do not necessitate extensive training data and show notably faster decoding speeds. However, existing research focus on low-sample-rate audio and has sacrificed quality over coding efficiency. In this presentation we demonstrate methods to train fully connected multilayer perceptron networks (MLPs) with periodic activation functions to show that INRs can proficiently model full-bandwidth audio signals.
FREE
Open to the Public