1. An Empirical Evaluation of Machine Learning Approaches for Species Identification through Bioacoustics
- Author
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Hao Ji, Sashi Thapaliya, Jonathan Louie, Ibraheem Saleh, Jose Figueroa-Hernandez, and Liang Zhang
- Subjects
Artificial neural network ,Computer science ,business.industry ,Bioacoustics ,Deep learning ,05 social sciences ,06 humanities and the arts ,Machine learning ,computer.software_genre ,050105 experimental psychology ,060404 music ,Background noise ,Set (abstract data type) ,Identification (information) ,User assistance ,Data quality ,0501 psychology and cognitive sciences ,Artificial intelligence ,business ,computer ,0604 arts - Abstract
In this paper, we investigate a complete system for identifying species from audio files. Audio data from both high quality and low quality sound files with varying degrees of background noise are collected and preprocessed for enhancing the learning capability of machine learning models. Then, fine-scale features are extracted to quantify acoustic properties of audio streams. Based on the proposed system, we evaluate a set of popular machine learning approaches on audio data from the cat and dog families. The experimental results show that the use of appropriate quality data and machine learning models yield compelling identification accuracy of species with limited user assistance.
- Published
- 2017