To expand the spatial and temporal scales of passive acoustic monitoring of animals, automatically detecting target sounds among noises with similar acoustic properties is essential but challenging. In particular, the classification of tonal vocalisations and tonal noise remains a universal problem in bioacoustics research. The vocalisations of dugong, which is an endangered marine mammal that inhabits coastal seas, need to be monitored to enhance our understanding of its habitat use. However, detecting dugong tonal vocalisations is difficult due to the presence of tonal noise in the same frequency band. In this study, a classification method was developed for these signals to handle large acoustic data by reducing the labour required for manual inspection. Mel-frequency cepstral coefficients (MFCC) were extracted to characterise background sounds along with a few parameters of the signal contour, and a support vector machine was trained for binary classification. The classifier achieved an 84.4% recall and a 93.5% precision on the testing dataset even in a noisy shallow marine environment. This methodology enables the effective classification of dugong calls and similar tonal noises by combining contour and MFCC features and can extend the spatial and temporal scale of acoustic monitoring of the endangered dugong. This technique is potentially applicable to the monitoring of other endangered marine mammals that produce tonal vocalisations.

为了扩大动物被动声学监测的空间和时间尺度，自动检测具有相似声学特性的噪声中的目标声音是必不可少的，但是具有挑战性。特别地，音调发声和音调噪声的分类仍然是生物声学研究中的普遍问题。儒艮是生活在沿海海洋中的一种濒临灭绝的海洋哺乳动物，因此需要对其进行监测，以增进我们对其栖息地使用的了解。然而，由于在相同频带中存在音调噪声，因此难以检测儒艮音调发声。在这项研究中，针对这些信号开发了一种分类方法，通过减少人工检查所需的劳动来处理大量声学数据。提取梅尔频率倒谱系数（MFCC）来表征背景声音以及信号轮廓的一些参数，并训练了支持向量机进行二进制分类。即使在嘈杂的浅海环境中，该分类器在测试数据集上也实现了84.4％的召回率和93.5％的精度。通过结合轮廓和MFCC特征，该方法可以对儒艮呼叫和类似的音调噪声进行有效分类，并且可以扩展对濒临灭绝的儒艮的声音监测的时空范围。该技术可能适用于监测产生声调发声的其他濒临灭绝的海洋哺乳动物。即使在嘈杂的浅海环境中，测试数据集的召回率为4％，准确度为93.5％。通过结合轮廓和MFCC特征，该方法可以对儒艮呼叫和类似的音调噪声进行有效分类，并且可以扩展对濒临灭绝的儒艮的声音监测的时空范围。该技术可能适用于监测产生声调发声的其他濒临灭绝的海洋哺乳动物。即使在嘈杂的浅海环境中，测试数据集的召回率为4％，准确度为93.5％。通过结合轮廓和MFCC特征，该方法可以对儒艮呼叫和类似的音调噪声进行有效分类，并且可以扩展对濒临灭绝的儒艮的声音监测的时空范围。该技术可能适用于监测产生声调发声的其他濒临灭绝的海洋哺乳动物。