Acoustic fish species identification using deep learning and machine learning algorithms: A systematic review

In fishery acoustics, surveys using sensor systems such as sonars and echosounders have been widely considered to be accurate tools for acquiring fish species data, fish species biomass, and abundance estimations. During acoustic surveys, research vessels are equipped with echosounders that produce sound waves and then record all echoes coming from objects and targets in the water column. The preprocessing and scrutinizing of acoustic fish species data have always been manually conducted and have been considered time-consuming. Meanwhile, deep learning and machine learning-based approaches have also been adopted to automate or partially automate the acoustic echo scrutinizing process and build an objective process with which the species echo classification uncertainty is expected to be lower than the uncertainty of scrutinizing experts. A review of the state-of-the-art of different deep learning and machine learning applications in acoustic fish species echo classification has been highly requested. Therefore, the present paper is conceived to identify and scan the studies conducted on acoustic fish echo identification using deep learning and machine learning approaches. This document can be extended to include other marine organisms rather than just fish species. To search for related papers, we used a systematic approach to search the most known electronic databases over the last five years. We were able to identify 13 related works, which have been processed to give a summary of multiple deep and machine learning approaches used in acoustic fish species identification, and then compare their architectures, performances, and the challenges encountered in their applications.