## 1 Introduction  
Within the field of bioacoustics, there is a growing need for accurate automatic signal classification for tasks such as call-type classification, individual identification, and assessment of correlation between vocalization patterns and specific social or behavioral contexts. There are a number of well-established techniques for vocalization classification, both template-based and parameter-based. Of the template-based methods, many require significant hand-tuning and have difficulty with temporal and individual variability. In recent years, there has been work toward application of more advanced pattern recognition methods such as those used in human speech and speaker recognition, in particular Hidden Markov Models (HMMs). The work presented here focuses on the use of HMMs as a robust recognition framework for bioacoustic signal classification across a variety of species and classification tasks. Existing methods for bioacoustic signal classification include multivariate feature analysis, spectrogram cross-correlation, matched filtering, neural networks, dynamic time warping, and others. The most well-established of these is spectrogram cross-correlation (SPCC) [1,2], in which a spectrogram (time-sequence of short-time Fourier transforms) is directly correlated with test vocalizations. Spectrogram cross-correlation is simple to implement, requiring only a single vocalization example of each type of call to be recognized, and can be easily applied to either the isolated vocalization task, where recordings have been presegmented into separate files, or to continuous detection, where a sliding window is applied across a long recording, with correlation peaks indicating target detection. This SPCC method is well-suited to recognizing animal sounds consisting of tones and frequency sweeps [3]. However, the cross correlation approach is unable to adapt to changes in call duration and alignment, and is also significantly impacted by frequency variation such as shifts caused by vocal individuality across callers. Matched filtering (MF) is a time-series correlation method that uses synthetic waveforms [4] or synthetic spectrograms [3] instead of recording examples. This approach is also easy to implement, although requiring more effort to construct pattern templates, and has many of the same strengths and weaknesses as spectrogram cross-correlation. Artificial Neural Network (ANN) approaches can be used for classification in many different applications, depending on the specific choice of input features and network topologies. If spectrogram values are used as input features, a common configuration, then ANNs can adjust the emphasis given to various portions of the time-frequency template, giving improved classification for some tasks such as species detection [5-7]. However, in this configuration, ANNs are subject to some of the same temporal nonlinearity and frequency shifting weaknesses as SPCC and MF. Dynamic time warping (DTW) [8], commonly used in small-vocabulary isolated word recognition for human speech, is able to handle temporal nonlinearity through optimal frame-to-frame alignment of the reference template with test vocalizations. However, it is difficult to implement DTW in a continuous detection framework, which is one of the reasons it is now rarely used for large-scale speech recognition. HMMs, now used in nearly all state-of-the-art speech recognition systems, have begun to gain attention in bioacoustics as well [9-12]. An HMM is a statistical state machine model where each state represents a stationary spectral configuration, and transitions between states represent spectral changes over time. Benefits of HMMs include their ability to handle nonlinear temporal alignment, their statistical basis for classification, their extendibility to continuous recognition or detection, and the ease with which they can incorporate complex language or other recognition constraints. The performance of HMM-based systems has been compared to other classification methods for bioacoustic tasks.