Transferring Voice Knowledge for Acoustic Event Detection: An Empirical Study
This work addresses acoustic event detection for human-centered contexts, but it is incremental as it builds on prior knowledge transfer methods.
The paper tackled acoustic event detection by transferring voice knowledge from a speaker dataset to enrich the pipeline, resulting in improved mean average precision from 0.134 to 0.292 for a CNN baseline and from 0.351 to 0.361 for a TALNet baseline.
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.