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

TL;DR

This paper introduces a fast, accurate, and context-aware audio annotation and classification framework that leverages feature selection, tempo representation, and active learning to achieve high accuracy with minimal data annotation effort.

Contribution

It proposes a novel context-aware framework combining feature selection, tempo representation, and active learning for efficient audio classification.

Findings

Achieved 90% accuracy with only 15% annotated data

Obtained 98.05% accuracy on UrbanSound8K dataset

Introduced a new feature selection and context-aware active learning approach

Abstract

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.