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Outdoor Human Footsteps Event and Environment Joint Recognition

Human footsteps contain rich audio information. The analysis of human footsteps can identify the actor's activity event and activity environment at the same time, so that the two independent tasks of sound event detection (SED) and sound scene classification (ASC) can be identified together. In order to realize this multitask recognition including events and scenes, this paper designs a human running and walking data set in a complex and similar environment. Based on this, a cross double footsteps segmentation scheme is proposed to cross-segment the continuous footsteps sound signals. Features are extracted from the perspective of event and environment, and two fusion features are designed in this paper. Multiple recognition models are used to accurately identify tasks. The cross double footsteps segmentation algorithm and fusion features designed in this paper simplify and balance the tasks, so that the multitask design of outdoor human footsteps event and environment joint recognition can achieve accurate recognition without complex models.