Aiming at solving the problems of insufficient feature information extraction and low accuracy in conventional anomalous sound detection methods, this paper presents a new method for detecting anomalous sound based on STgram-MFN optimization. By fusing multiple attention mechanisms for feature recalibration, it can selectively emphasize features with high informative content and suppress less useful features, thereby improving the accuracy of anomalous sound detection. Experiments on the DCASE 2020 Challenge Task two dataset show that compared with the original STgram-MFN, Its AUC has reached 94.20%, 74.29%, 88.82%, 92.86%, 99.29%, 98.06% (ToyCar, Toycar, Fan, Pump, Slider, Valve). Respectively, increased by 1.56%, 1.37%, 4.05%, 2.87%, 0.04% and 2.91%. In addition, the average AUC of our proposed method is improved by 2.13%.
When dealing with complex tasks, such as robots imitating human actions and autonomous vehicles driving in urban environments, it can be difficult to determine the reward function of the Markov decision-making process. In contrast to reinforcement learning, Inverse Reinforcement Learning (IRL) can infer the reward function through the finite state space and the linear combination of reward features, given the optimal strategy or expert trajectory. At present, IRL has many challenges, such as ambiguity, large computation and generalization. As part of this paper, we discuss existing research related to these issues, describe the existing traditional IRL methods, implement the model, and then propose future direction for further research.
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