  Detection of common events and scenes from audio is useful for extracting and
understanding human contexts in daily life. Prior studies have shown that
leveraging knowledge from a relevant domain is beneficial for a target acoustic
event detection (AED) process. Inspired by the observation that many
human-centered acoustic events in daily life involve voice elements, this paper
investigates the potential of transferring high-level voice representations
extracted from a public speaker dataset to enrich an AED pipeline. Towards this
end, we develop a dual-branch neural network architecture for the joint
learning of voice and acoustic features during an AED process and conduct
thorough empirical studies to examine the performance on the public AudioSet
[1] with different types of inputs. Our main observations are that: 1) Joint
learning of audio and voice inputs improves the AED performance (mean average
precision) for both a CNN baseline (0.292 vs 0.134 mAP) and a TALNet [2]
baseline (0.361 vs 0.351 mAP); 2) Augmenting the extra voice features is
critical to maximize the model performance with dual inputs.
