  This report provides a brief description of our proposed solution for the
Vocal Burst Type classification task of the ACII 2022 Affective Vocal Bursts
(A-VB) Competition. We experimented with two approaches as part of our solution
for the task at hand. The first of which is based on convolutional neural
networks trained on Mel Spectrograms, and the second is based on average
pooling of deep acoustic embeddings from a pretrained wav2vec2 model. Our best
performing model achieves an unweighted average recall (UAR) of 0.5190 for the
test partition, compared to the chance-level UAR of 0.1250 and a baseline of
0.4172. Thus, an improvement of around 20% over the challenge baseline. The
results reported in this document demonstrate the efficacy of our proposed
approaches to solve the AV-B Type Classification task.
