Abstract: Polyphonic Acoustic Event Detection (AED) is a challenging task as the sounds are mixed with the signals from diffe-rent events,and the overall features extracted from the mixture can not represent each event well,leading to suboptimal AED performance especially when the number of sound events increases or environment changes.Existing methods do not consider the impact of environmental changes on detection performance.Therefore,an Environment-Assisted Multi-Task learning (EAMT) method for AED was proposed.EAMT model mainly consists of two core parts:environment classifier and sound event detector,where the environment classifier is used to learn environment context features.As additional information of event detection,the environment context features are fused with sound event features to assist sound event detection by muli-task learning,so as to improve the robustness of EAMT model to environmental changes and the performance of polyphonic event detection.Based on Freesound dataset,one of the mainstream open data set in the field of AED,and general performance evaluation metrics F1 score,three sets of comparative experiments were set up to compare the proposed method with DNN(baseline) and CRNN,which is one of the most popular methods.The experimental results show that:compared with the single task model,EAMT model improves the performance of environment classification and event detection,and the introduction of environment context features further improves the performance of acoustic event detection.EAMT model has stronger robustness than DNN and CRNN as the F1 score of EAMT is 2% to 5% higher than other models when environment changes.When the number of target events increases,EAMT model still performs prominently,and compared with other models,EAMT model achieves an improvement of about 2% to 10% in F1 score.

Key words: Acoustic event detection, Environmental robustness, Environment-assisted, Features fusion, Multi-task learning