# Face: Fast, Accurate and Context-Aware Audio Annotation and Classification

arXiv: https://arxiv.org/abs/2303.03666 · HTML: https://mlsanity.com/paper/2303.03666/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 27 references that are in the map.
- First posted on arXiv 7 Mar 2023.

## Builds on (most cited first, 6 of 6)

- [Addressing Missing Labels in Large-Scale Sound Event Recognition Using a Teacher-Student Framework With Loss Masking](https://mlsanity.com/paper/2005.00878/) (2020)
- [An Ontology-Aware Framework for Audio Event Classification](https://mlsanity.com/paper/2001.10048/) (2020)
- [Semi-Supervised Audio Classification with Partially Labeled Data](https://mlsanity.com/paper/2111.12761/) (2021)
- [Context Aware Machine Learning](https://mlsanity.com/paper/1901.03415/) (2019)
- [Semi-Supervised NMF-CNN For Sound Event Detection](https://mlsanity.com/paper/2007.00908/) (2020)
- [Cross-domain Semi-Supervised Audio Event Classification Using Contrastive Regularization](https://mlsanity.com/paper/2109.14508/) (2021)

## Built on by (most cited first, 1 of 1)

- [Automated data processing and feature engineering for deep learning and big data applications: a survey](https://mlsanity.com/paper/2403.11395/) (2024)

## Abstract (arXiv)

This paper presents a context-aware framework for feature selection and classification procedures to realize a fast and accurate audio event annotation and classification. The context-aware design starts with exploring feature extraction techniques to find an appropriate combination to select a set resulting in remarkable classification accuracy with minimal computational effort. The exploration for feature selection also embraces an investigation of audio Tempo representation, an advantageous feature extraction method missed by previous works in the environmental audio classification research scope. The proposed annotation method considers outlier, inlier, and hard-to-predict data samples to realize context-aware Active Learning, leading to the average accuracy of 90% when only 15% of data possess initial annotation. Our proposed algorithm for sound classification obtained average prediction accuracy of 98.05% on the UrbanSound8K dataset. The notebooks containing our source codes and implementation results are available at https://github.com/gitmehrdad/FACE.