Visual Active Learning for Labeling: A Case for Soundscape Ecology Data

Abstract

Labeling of samples is a recurrent and time-consuming task in data analysis and machine learning and yet generally overlooked in terms of visual analytics approaches to improve the process. As the number of tailored applications of learning models increases, it is crucial that more effective approaches to labeling are developed. In this paper, we report the development of a methodology and a framework to support labeling, with an application case as background. The methodology performs visual active learning and label propagation with 2D embeddings as layouts to achieve faster and interactive labeling of samples. The framework is realized through SoundscapeX, a tool to support labeling in soundscape ecology data. We have applied the framework to a set of audio recordings collected for a Long Term Ecological Research Project in the Cantareira-Mantiqueira Corridor (LTER CCM), localized in the transition between northeastern São Paulo state and southern Minas Gerais state in Brazil. We employed a pre-label data set of groups of animals to test the efficacy of the approach. The results showed the best accuracy at 94.58% in the prediction of labeling for birds and insects; and 91.09% for the prediction of the sound event as frogs and insects.

Soundscape Characterization Using Autoencoders and Unsupervised Learning

“…Supervised methodologies in acoustic analysis often yield high-accuracy results but face challenges due to their reliance on labeled data for model training and testing. These challenges include the need for expert analysis to identify patterns in audio [ 15 , 16 ], assumptions about data structure [ 10 ] such as the set of possible animal vocalizations [ 17 ], time-consuming sample labeling with practical issues like lacking specific timestamps and frequency bands, and handling overlapping acoustic events in time and frequency [ 15 , 16 ]. Moreover, feature extraction methods are sensitive to noisy data [ 7 ].…”