# Acoustic scene classification using teacher-student learning with soft-labels

arXiv: https://arxiv.org/abs/1904.10135 · HTML: https://mlsanity.com/paper/1904.10135/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Apr 2019.

## Built on by (most cited first, 4 of 4)

- [Multi-View Attention Transfer for Efficient Speech Enhancement](https://mlsanity.com/paper/2208.10367/) (2022)
- [Attentive max feature map and joint training for acoustic scene classification](https://mlsanity.com/paper/2104.07213/) (2021)
- [Robust, General, and Low Complexity Acoustic Scene Classification Systems and An Effective Visualization for Presenting a Sound Scene Context](https://mlsanity.com/paper/2210.08610/) (2022)
- [Creating a Good Teacher for Knowledge Distillation in Acoustic Scene Classification](https://mlsanity.com/paper/2503.11363/) (2025)

## Abstract (arXiv)

Acoustic scene classification identifies an input segment into one of the pre-defined classes using spectral information. The spectral information of acoustic scenes may not be mutually exclusive due to common acoustic properties across different classes, such as babble noises included in both airports and shopping malls. However, conventional training procedure based on one-hot labels does not consider the similarities between different acoustic scenes. We exploit teacher-student learning with the purpose to derive soft-labels that consider common acoustic properties among different acoustic scenes. In teacher-student learning, the teacher network produces soft-labels, based on which the student network is trained. We investigate various methods to extract soft-labels that better represent similarities across different scenes. Such attempts include extracting soft-labels from multiple audio segments that are defined as an identical acoustic scene. Experimental results demonstrate the potential of our approach, showing a classification accuracy of 77.36 % on the DCASE 2018 task 1 validation set.