Attentive Max Feature Map for Acoustic Scene Classification with Joint Learning considering the Abstraction of Classes

Abstract

The attention mechanism has been widely adopted in acoustic scene classification. However, we find that during the process of attention exclusively emphasizing information, it tends to excessively discard information although improving the performance. We propose a mechanism referred to as the attentive max feature map which combines two effective techniques, attention and max feature map, to further elaborate the attention mechanism and mitigate the abovementioned phenomenon. Furthermore, we explore various joint learning methods that utilize additional labels originally generated for subtask B (3-classes) on top of existing labels for subtask A (10-classes) of the DCASE2020 challenge. We expect that using two kinds of labels simultaneously would be helpful because the labels of the two subtasks differ in their degree of abstraction. Applying two proposed techniques, our proposed system achieves state-of-the-art performance among single systems on subtask A. In addition, because the model has a complexity comparable to subtask B's requirement, it shows the possibility of developing a system that fulfills the requirements of both subtasks; generalization on multiple devices and low-complexity.

Interactive Concept Bottleneck Models

“…Ma et al (2018) and Zannone et al (2019) uses a partial variational autoencoder to predict the rest of features given the acquired ones to model the feature importance and uncertainity and combine it with acquisition policy to maximize information gain. Shim, Hwang, and Yang (2018) treats this as a joint learning problem trains both the classifier and RL agent together to learn when and which feature to acquire to increase classification accuracy while maintaining cost-efficiency. Li and Oliva (2021) reformulate Markov Decision Process (MDP) and learn a generative surrogate model to capture inter feature dependencies to aid RL agent with intermediate rewards and auxiliary information.…”

“…This is an oracular skyline since it uses the test label for optimization, and is an approximate ceiling on the performance achievable under any interactive policy. 4 Active Feature Acquisition (AFA): We also compare against the Active Feature Acquisition policy based on work by Shim, Hwang, and Yang (2018). We use image embeddings from ResNet18 pre-trained on ImageNet as auxiliary information, and ground truth concepts as features that can be acquired; we train an RL policy to actively acquire features/concepts as described in Shim, Hwang, and Yang (2018).…”

“…4 Active Feature Acquisition (AFA): We also compare against the Active Feature Acquisition policy based on work by Shim, Hwang, and Yang (2018). We use image embeddings from ResNet18 pre-trained on ImageNet as auxiliary information, and ground truth concepts as features that can be acquired; we train an RL policy to actively acquire features/concepts as described in Shim, Hwang, and Yang (2018). This algorithm is relatively sensitive to the value of a hyperparameter r cost that determines the tradeoff between model performance and acquisition cost.…”

“…• We compare our dynamic instance-based query policy against static policies that determine a fixed order of querying attributes for all examples (Koh et al 2020), as well as SOTA active feature acquisition strategies (Shim, Hwang, and Yang 2018) that apply to more general settings of acquiring features beyond human-interpretable concepts.…”