# Semi-Supervised Audio Classification with Partially Labeled Data

arXiv: https://arxiv.org/abs/2111.12761 · HTML: https://mlsanity.com/paper/2111.12761/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Nov 2021.

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

- [Symptom Identification for Interpretable Detection of Multiple Mental Disorders](https://mlsanity.com/paper/2205.11308/) (2022)
- [Music Instrument Classification Reprogrammed](https://mlsanity.com/paper/2211.08379/) (2022)
- [Face: Fast, Accurate and Context-Aware Audio Annotation and Classification](https://mlsanity.com/paper/2303.03666/) (2023)
- [Representation Learning for the Automatic Indexing of Sound Effects Libraries](https://mlsanity.com/paper/2208.09096/) (2022)
- [ECHO: Environmental Sound Classification with Hierarchical Ontology-guided Semi-Supervised Learning](https://mlsanity.com/paper/2409.14043/) (2024)

## Abstract (arXiv)

Audio classification has seen great progress with the increasing availability of large-scale datasets. These large datasets, however, are often only partially labeled as collecting full annotations is a tedious and expensive process. This paper presents two semi-supervised methods capable of learning with missing labels and evaluates them on two publicly available, partially labeled datasets. The first method relies on label enhancement by a two-stage teacher-student learning process, while the second method utilizes the mean teacher semi-supervised learning algorithm. Our results demonstrate the impact of improperly handling missing labels and compare the benefits of using different strategies leveraging data with few labels. Methods capable of learning with partially labeled data have the potential to improve models for audio classification by utilizing even larger amounts of data without the need for complete annotations.