@article {2004Classification,
	title = {Classification of bioacoustic time series based on the combination of global and local decisions},
	journal = {Pattern Recognition},
	volume = {37},
	number = {12},
	year = {2004},
	month = {December},
	pages = {2293{\textendash}2305},
	abstract = {Automated classification of cricket songs from Thailand and Ecuador is the topic of this study. For this, the locations of pulses are determined and different features in the time and the frequency domain are extracted automatically from the time series. For the categorization of the sound patterns these features are combined through data fusion, temporal fusion and decision fusion. Local features and global features are distinguished. For the classification a fuzzy- k -nearest-neighbour classifier was used. Classification results for a data set containing songs of 28 different species are presented.},
	keywords = {asr, bioacoustics, classification, crickets, databases, orthoptera, sound\_recognition},
	issn = {00313203},
	doi = {10.1016/j.patcog.2004.04.004},
	url = {http://dx.doi.org/10.1016/j.patcog.2004.04.004}
}
@article {Chesmore2004Automated,
	title = {Automated identification of field-recorded songs of four British grasshoppers using bioacoustic signal recognition},
	journal = {Bulletin of Entomological Research},
	volume = {94},
	number = {04},
	year = {2004},
	pages = {319{\textendash}330},
	abstract = {Recognition of Orthoptera species by means of their song is widely used in field work but requires expertise. It is now possible to develop computer-based systems to achieve the same task with a number of advantages including continuous long term unattended operation and automatic species logging. The system described here achieves automated discrimination between different species by utilizing a novel time domain signal coding technique and an artificial neural network. The system has previously been shown to recognize 25 species of British Orthoptera with 99\% accuracy for good quality sounds. This paper tests the system on field recordings of four species of grasshopper in northern England in 2002 and shows that it is capable of not only correctly recognizing the target species under a range of acoustic conditions but also of recognizing other sounds such as birds and man-made sounds. Recognition accuracies for the four species of typically 70\&\#8211;100\% are obtained for field recordings with varying sound intensities and background signals.},
	keywords = {acoustics, asr, bioacoustics, grasshoppers, identification, orthoptera, stridulation},
	doi = {10.1079/BER2004306},
	url = {http://dx.doi.org/10.1079/BER2004306},
	author = {Chesmore, E. D. and Ohya, E.}
}
@proceedings {Ferguson2004Extracting,
	title = {Extracting tactical information from acoustic signals},
	year = {2004},
	month = {December},
	pages = {149{\textendash}153},
	abstract = {Smart acoustic sensor systems can be deployed for the automatic detection, localization, classification and tracking of military activities, which are inherently noisy. Acoustic sensors are appealing because they are passive, affordable, robust. and compact. Also, the propagation of sound energy is not limited by obstacles, which block or obscure the clear line of sight that is required for the effective operation of electromagnetic systems. Methods, with examples, for extracting tactical information from acoustic signals emitted by moving sources (air and ground vehicles) are provided for both single sensor and multiple sensor configurations. The methods are based on processing either the narrowband or broadband spectral components of the source{\textquoteright}s acoustic signature. For example, the tactical information extracted by narrowband processing of single sensor data for a moving source is the source speed, slant range and time at the closest point of approach, as well as the source (or rest) frequency of the narrowband signal. For an array of sensors, the tactical information consists of estimates of a complete set of target motion parameters, which indicate the speed and trajectory of the source. Weapon firings generate acoustic impulses and supersonic projectiles generate acoustic shocks, which can be localized and classified using spatially distributed acoustic sensors. The methods developed for land-based acoustic surveillance using microphone data are also applied to hydrophone data for passive acoustic surveillance of the underwater environment.},
	keywords = {acoustics, asr, monitoring, sensors},
	doi = {10.1109/ISSNIP.2004.1417453},
	url = {http://dx.doi.org/10.1109/ISSNIP.2004.1417453},
	author = {Ferguson, B. G. and Lo, K. W.}
}
