IntroductionThe 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.MethodsIn 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.ResultsExperimental 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%.DiscussionThe 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.

中文翻译：

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基于低维声学特征的大口黑鲈进食声识别


简介大口黑鲈（Micropterus salmoides）的进食声音主要分为吞咽声音和咀嚼声音，两种声音的强度与鱼的密度和摄食欲望密切相关。因此，准确识别这两种声音对于研究鱼类摄食行为具有重要意义。方法在本研究中，我们提出了一种基于低维声学特征的鱼类吞咽和咀嚼声音的识别方法。首先，利用同步视听手段采集大口黑鲈的摄食声音信号和图像信号。通过分析声音信号的时频域特征，我们识别了四个类别的 15 个关键声学特征，包括短时平均能量、平均梅尔频率倒谱系数、功率谱峰值和中心频率。随后，采用九种降维算法，从 15 维声学特征中选择 Top-6 特征，并使用四种机器学习模型比较它们在识别吞咽和咀嚼声音方面的精度。大口黑鲈摄食特征识别的准确性提取的声学特征展示了全局相关性和线性特征。在考虑特征维数和分类性能时，基于随机森林模型的特征降维与识别模型相结合表现出最好的性能，识别准确率达到98.63%。讨论该方法以较低的计算复杂度对吞咽和咀嚼声音进行评估，从而提高了吞咽和咀嚼声音的评估精度，从而为养鱼精准投喂技术的研究提供了有效的技术支撑。