Transferring Voice Knowledge for Acoustic Event Detection: An Empirical Study

• URL: http://arxiv.org/abs/2110.03174v1
• Date: Thu, 7 Oct 2021 04:03:21 GMT
• Title: Transferring Voice Knowledge for Acoustic Event Detection: An Empirical Study
• Authors: Dawei Liang, Yangyang Shi, Yun Wang, Nayan Singhal, Alex Xiao, Jonathan Shaw, Edison Thomaz, Ozlem Kalinli, Mike Seltzer
• Abstract summary: This paper investigates the potential of transferring high-level voice representations extracted from a public speaker dataset to enrich an acoustic event detection pipeline. We develop a dual-branch neural network architecture for the joint learning of voice and acoustic features during an AED process.
• Score: 11.825240267691209
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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