Acoustic scene classification (ASC) and sound event detection (SED) are major topics in environmental sound analysis. Considering that acoustic scenes and sound events are closely related to each other, the joint analysis of acoustic scenes and sound events using multitask learning (MTL)-based neural networks was proposed in some previous works. Conventional methods train MTL-based models using a linear combination of ASC and SED loss functions with constant weights. However, the performance of conventional MTL-based methods depends strongly on the weights of the ASC and SED losses, and it is difficult to determine the appropriate balance between the constant weights of the losses of MTL of ASC and SED. In this paper, we thus propose dynamic weight adaptation methods for MTL of ASC and SED based on dynamic weight average and multi--focal loss to adjust the learning weights automatically. Evaluation experiments using parts of the TUT Acoustic Scenes 2016/2017 and TUT Sound Events 2016/2017 are conducted, and we show that the proposed methods improve the scene classification and event detection performance characteristics compared with the conventional MTL-based method. We then investigate how the learning weights of ASC and SED tasks dynamically adapt as the model training progresses.
翻译:声学场景分类(ASC)和声学事件探测(SED)是环境无害分析的主要专题。考虑到声学场景和声学事件彼此密切相关,在以前的一些著作中曾提议使用多任务学习(MTL)神经网络对声学场景和声学事件进行联合分析。常规方法利用ASC和SED损失函数的线性组合进行基于MTL的模型培训,同时使用不变重量来自动调整学习重量。然而,传统的MTL方法的性能在很大程度上取决于ASC和SED损失的重量,因此难以确定ASC和SEDML损失的常数重量之间的适当平衡。因此,我们在此文件中根据动态重量平均值和多系数损失来建议对ASC和SEDM的ML进行动态重量调整,以便自动调整学习重量。我们随后利用TUT的声学SECDS(2016/2017年)和TUT声音事件部分进行了评估实验,我们展示了拟议的方法如何改进现场分类和事件探测性工作特征特征,与常规MTL(S-DA)的调整方法相比,我们随后研究如何学习了S-DS-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-