An acoustic activity recognition based on deep reinforcement learning
Ming Liu, Huang Jifeng, GAO Hai · DOAJ (DOAJ: Directory of Open Access Journals) · 2020
Most of previous research normally relied on specific data and manual filtering of outliers for better performance.In this paper,a new strategy of activity recognition was proposed which was entirely free from the constraint of user data and guaranteed the generalization ability of model by usage of combined Mel spectrogram and embedding features extracted from video sound clips of Google AudioSet dataset.11 general domestic-related activities were recognized and evaluated based on deep reinforcement learning method,which dynamically controlled the distribution of data and resolved the data imbalance problem.The experimental test produced 87.5% overall accuracy and more than 83% accuracy for the 11 activities respectively.