Relational Teacher Student Learning with Neural Label Embedding for Device Adaptation in Acoustic Scene Classification
Table 1: Evaluation results on DCASE 2018 Task1b.
['Model', 'Dev A acc.(%)', 'Dev B acc.(%)', 'Dev C acc.(%)']
The official baseline system [dcase2018], and the AlexNet-L (All Devs), listed in the two first rows, are trained using data from all the three devices, namely A, B, and C. The amount of Device A training material is much larger than that available for Devices B & C (24 hours against 2 hours); therefore, when the baseline systems are skewed toward Device A, and the degradation in the classification accuracy when moving from Device A test data to device B & C test data is significant. A data (this is, Device B & C data is missing). This experiment is conducted to clarify any doubt about the severity of the device mismatch issue. In fact, the accuracy drops from 69.6% on device A down to 13.4% and 16.2% on Devices B and C, respectively. This adaptation approach can be put forth because Device B & C data are paired with some of Device A data. The teacher is AlexNet-L (Dev A). The student is obtained by copying the teacher architecture and then adapting it using target device data and soft labels generated by the teacher model fed with the Device A paired data. In soft labels adaptation, the teacher parameters remain unchanged. That may be due to the the mismatch between source and target domain is large. Finally, we turn our attention to the key architecture proposed for device adaptation. (NLE-RTSL adaptation) are reported. First, we can observe that both NLE adaptation approaches attain superior performance compared to one-hot adaptation. For Device B, classification accuracy goes from 57.0% to 58.7%, and from 60.8% to 62.0% for Device C. A significant boost in the classification results can further be obtained by imposing structural constrains during knowledge distillation. Indeed, NLE-RTSL adaptation allows us to attain top performance of 59.2% for Device B, and 64.2% on Device C, respectively.