Authors: Bin Wu, Sakriani Sakti, Shinnosuke Takamichi, Satoshi Nakamura
Abstract: Marmoset, a highly vocalized primate, has become a popular animal model for
studying social-communicative behavior and its underlying mechanism. In the
study of vocal communication, it is vital to know the caller identities, call
contents, and vocal exchanges. Previous work of a CNN has achieved a joint
model for call segmentation, classification, and caller identification for
marmoset vocalizations. However, the CNN has limitations in modeling long-range
acoustic patterns; the Transformer architecture that has been shown to
outperform CNNs, utilizes the self-attention mechanism that efficiently
segregates information parallelly over long distances and captures the global
structure of marmoset vocalization. We propose using the Transformer to jointly
segment and classify the marmoset calls and identify the callers for each
vocalization.
Source: http://arxiv.org/abs/2410.23279v1
