  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.
