Recognition of feeding sounds of large-mouth black bass based on low-dimensional acoustic features

Introduction The eating sounds of largemouth black bass ( Micropterus salmoides ) are primarily categorized into swallowing and chewing sounds, both intensities of which are closely correlated with fish density and feeding desire. Therefore, accurate recognition of these two sounds is of significant importance for studying fish feeding behavior. Methods In this study, we propose a method based on low-dimensional acoustic features for the recognition of swallowing and chewing sounds in fish. Initially, utilizing synchronous audio-visual means, we collect feeding sound signals and image signals of largemouth black bass. By analyzing the time-frequency domain features of the sound signals, we identify 15 key acoustic features across four categories including short-time average energy, average Mel-frequency cepstral coefficients, power spectral peak, and center frequency. Subsequently, employing nine dimensionality reduction algorithms, we select the Top-6 features from the 15-dimensional acoustic features and compare their precision in recognizing swallowing and chewing sounds using four machine learning models. Results Experimental results indicate that supervised feature pre-screening positively enhances the accuracy of largemouth black bass feeding feature recognition. Extracted acoustic features demonstrate global correlation and linear characteristics. When considering feature dimensionality and classification performance, the combination of feature dimensionality reduction and recognition model based on the random forest model exhibits the best performance, achieving an identification accuracy of 98.63%. Discussion The proposed method offers higher assessment accuracy of swallowing and chewing sounds with lower computational complexity, thus providing effective technical support for the research on precise feeding technology in fish farming.