## 1 Introduction
[PERSON] _et al._[13] compared the performance of a matched filter and an HMM system for detecting bowhead whale (_Balena mysticetus_) call notes. The matched filter used a spectrogram template, while the HMM used the three largest spectral peaks as features for an 18-stage model. The HMM detected 97% of the notes, whereas the MF detected 84%. [PERSON] [14] compared an HMM approach to a DTW-based system using a dataset consisting of two different species of bird song. His conclusion was that although the DTW system worked better with a small amount of training data, the HMM system was more robust to noise and to vocalization variability. One of the issues that makes comparison across methods difficult is that of feature extraction. The selection of features is an essential component of any pattern recognition system, often having as much or more impact on the outcome as the choice of classification model. For example, SPCC by definition relies on short-time Fourier transform amplitudes as a feature, whereas MF uses a direct time-series correlation, and DTW can use any spectral distance measure between frames. HMMs, as statistical classification models, are able to use any frame-based feature vector giving meaningful discriminability across classes, most commonly cepstral coefficients. This use of cepstrum coefficients is somewhat different than the standard statistical analysis approaches common in the bioacoustics field, where typical features include whole-vocalization measures such as duration, energy, and maximum, minimum and average fundamental frequency values. Such measures work well with statistical test techniques such as the t-test, Chi-Squared test, MANOVA, and factor analysis. Frame-based features, however, are somewhat more difficult to use with traditional statistical approaches [8,15] because there are a variable number of features per vocalization. Another significant and often unavoidable problem in bioacoustic signal processing is the presence of background noise due to adverse recording environments, as well as convolutional noise due to microphone and sound propagation variability. Standard spectral features are highly sensitive to noise, which can decrease system accuracy and give misleading results. It is important, therefore, to incorporate signal enhancement and/or robust feature extraction techniques that enhance the vocalization while suppressing background noise. Common techniques to reduce noise artifacts in bioacoustic signals include basic bandpass filters and related frequency-based methods for spectrogram filtering and equalization, often incorporated directly into acquisition and analysis tools [16]. Other approaches in recent years have included spectral subtraction [17], minimum mean-squared error (MMSE) estimation [18,19], adaptive line enhancement [20], and perceptually motivated wavelet denoising [21]. In the study presented here, we examine the HMM recognition framework and look at its applicability to several different types of bioacoustic classification tasks. The framework is illustrated with three different datasets, each representing a different kind of task. While the same HMM framework and feature extraction methods are used across all tasks, front-end signal processing methods and implementation details are tailored to each application. Application tasks include call type classification of Asian elephant vocalizations, syllable-based song-type classification of ortolan hunting songs, and age-dependent stress stimulus classification of poultry vocalizations.