# Relational Teacher Student Learning with Neural Label Embedding for Device Adaptation in Acoustic Scene Classification

arXiv: https://arxiv.org/abs/2008.00110 · HTML: https://mlsanity.com/paper/2008.00110/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jul 2020.

## Built on by (most cited first, 5 of 5)

- [PATE-AAE: Incorporating Adversarial Autoencoder into Private Aggregation of Teacher Ensembles for Spoken Command Classification](https://mlsanity.com/paper/2104.01271/) (2021)
- [A Lottery Ticket Hypothesis Framework for Low-Complexity Device-Robust Neural Acoustic Scene Classification](https://mlsanity.com/paper/2107.01461/) (2021)
- [Neural Model Reprogramming with Similarity Based Mapping for Low-Resource Spoken Command Recognition](https://mlsanity.com/paper/2110.03894/) (2021)
- [A Variational Bayesian Approach to Learning Latent Variables for Acoustic Knowledge Transfer](https://mlsanity.com/paper/2110.08598/) (2021)
- [Adversarial Domain Adaptation with Paired Examples for Acoustic Scene Classification on Different Recording Devices](https://mlsanity.com/paper/2110.09598/) (2021)

## Abstract (arXiv)

In this paper, we propose a domain adaptation framework to address the device mismatch issue in acoustic scene classification leveraging upon neural label embedding (NLE) and relational teacher student learning (RTSL). Taking into account the structural relationships between acoustic scene classes, our proposed framework captures such relationships which are intrinsically device-independent. In the training stage, transferable knowledge is condensed in NLE from the source domain. Next in the adaptation stage, a novel RTSL strategy is adopted to learn adapted target models without using paired source-target data often required in conventional teacher student learning. The proposed framework is evaluated on the DCASE 2018 Task1b data set. Experimental results based on AlexNet-L deep classification models confirm the effectiveness of our proposed approach for mismatch situations. NLE-alone adaptation compares favourably with the conventional device adaptation and teacher student based adaptation techniques. NLE with RTSL further improves the classification accuracy.