Hidden Markov Models (HMMs) were developed and implemented for the discrimination of 11 available Whales (Cetaceans). The primarily aims of the experiments were to explore the impact frame size and step size, feature vector size, and number of states for feature extraction and acoustic models on classification accuracy. Through the experiments using Mel-Frequency Cepstral Coefficients (MFCCs) extracted from the vocalizations (7 ms frame size and 6 ms step size), HMMs containing 4 states with single underlying Gaussian Mixture Model (GMM) yielded high classification accuracies ranging from 82.72% (9 classes) to 100.00% (1–3 classes), including discrimination of 84.11% (11 classes). From the results, the framework could be applied to the analysis of other marine mammals for the automatic classification and detection of vocalizations and species.

隐马尔可夫模型（HMM）的开发和实现是为了区分11种可用的鲸鱼（鲸类）。实验的主要目的是探索影响帧的大小和步长，特征向量的大小以及特征提取和声学模型对分类精度的状态数。通过使用从发声中提取的梅尔频率倒谱系数（MFCC）（7 ms帧大小和6 ms步长）进行的实验，包含4种状态和单个基础高斯混合模型（GMM）的HMM产生了82.72％（ 9类）到100.00％（1-3类），包括84.11％的歧视（11类）。从结果来看，该框架可以应用于其他海洋哺乳动物的分析，以便对发声和物种进行自动分类和检测。