@article {Swiston2009Comparison,
	title = {Comparison of manual and automated methods for identifying target sounds in audio recordings of Pileated, Pale-billed, and putative Ivory-billed woodpeckers},
	journal = {Journal of Field Ornithology},
	volume = {80},
	number = {1},
	year = {2009},
	month = {March},
	pages = {42{\textendash}50},
	publisher = {Blackwell Publishing},
	keywords = {asr, bioacoustics, biodiversity, birds, monitoring},
	issn = {02738570},
	doi = {10.1111/j.1557-9263.2009.00204.x},
	url = {http://dx.doi.org/10.1111/j.1557-9263.2009.00204.x},
	author = {Swiston, Kyle A. and Mennill, Daniel J.}
}
@article {Bardeli2009Detecting,
	title = {Detecting bird sounds in a complex acoustic environment and application to bioacoustic monitoring},
	journal = {Pattern Recognition Letters},
	year = {2009},
	month = {September},
	abstract = {Trends in bird population sizes are an important indicator in nature conservation but measuring such sizes is a very difficult, labour intensive process. Enormous progress in audio signal processing and pattern recognition in recent years makes it possible to incorporate automated methods into the detection of bird vocalisations. These methods can be employed to support the census of population sizes. We report about a study testing the feasibility of bird monitoring supported by automatic bird song detection. In particular, we describe novel algorithms for the detection of the vocalisations of two endangered bird species and show how these can be used in automatic habitat mapping. These methods are based on detecting temporal patterns in a given frequency band typical for the species. Special effort is put into the suppression of the noise present in real-world audio scenes. Our results show that even in real-world recording conditions high recognition rates with a tolerable rate of false positive detections are possible.},
	keywords = {asr, bioacoustics, birds, monitoring, neuroinofrmatics},
	issn = {01678655},
	doi = {10.1016/j.patrec.2009.09.014},
	url = {http://dx.doi.org/10.1016/j.patrec.2009.09.014},
	author = {Bardeli, R. and Wolff, D. and Kurth, F. and Koch, M. and Tauchert, K. H. and Frommolt, K. H.}
}
@article {Riede1998Acoustic,
	title = {Acoustic monitoring of Orthoptera and its potential for conservation},
	journal = {Journal of Insect Conservation},
	volume = {2},
	number = {3},
	year = {1998},
	month = {December},
	pages = {217{\textendash}223},
	abstract = {Songs of Orthoptera can be used for inventorying and monitoring of individual species and communities. Acoustic parameters such as carrier frequency and pulse rates allow the definition of recognizable taxonomic units (RTUs) which help to overcome the taxonomic impediment due to our scanty knowledge, particularly of tropical faunas. Bioacoustic diversity is a first estimate for species richness and provides baseline data which can be a prerequisite for conservation. Additional ecological and behavioural information such as habitat preference and singing schedules can be inferred. Many Orthoptera are sensitive indicator species for habitat quality in temperate and tropical ecosystems. Examples are given for evaluation of habitat quality and deterioration by acoustic detection of Orthoptera.},
	keywords = {acoustic\_detection, acoustic\_parameters, annotation, asr, biodiversity, carrier\_frequency, indicator\_species, orthoptera, species\_richness, taxonomic\_impediment, taxonomic\_units},
	doi = {10.1023/A:1009695813606},
	url = {http://www.groms.de/data/zoology/riede/RiedeetalJOR06.pdf},
	author = {Riede, Klaus}
}
@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 {Selin2007Wavelets,
	title = {Wavelets in Recognition of Bird Sounds},
	journal = {EURASIP Journal on Advances in Signal Processing},
	volume = {2007},
	year = {2007},
	pages = {1{\textendash}10},
	keywords = {asr, bioacoustics, birds, songs},
	issn = {1687-6172},
	doi = {10.1155/2007/51806},
	url = {http://dx.doi.org/10.1155/2007/51806},
	author = {Selin, Arja and Turunen, Jari and Tanttu, Juha T.}
}
@article {Mazarakis2007Vehicle,
	title = {Vehicle classification in Sensor Networks using time-domain signal processing and Neural Networks},
	journal = {Microprocessors and Microsystems},
	volume = {31},
	number = {6},
	year = {2007},
	month = {September},
	pages = {381{\textendash}392},
	abstract = {Vehicle classification is a demanding application of Wireless Sensor Networks. In many cases, sensor nodes detect and classify vehicles from their acoustic and/or seismic signature using spectral or wavelet based feature extraction methods. Such methods, while providing good results are quite demanding in computational power and energy and are difficult to implement on low-cost sensor nodes with limited resources. In this work, we investigate the use of a time-domain encoding and feature extraction method, to produce simple, fixed-size matrices from complex acoustic and seismic signatures of vehicles for classification purposes. Classification is accomplished using an Artificial Neural Network and a basic, L1 distance, archetype classifier. Hardware implementation issues on a prototype sensor node, based on an 8-bit microcontroller, are also discussed. For evaluation purposes we use real data from DARPA{\textquoteright}s SensIt project, which contains various acoustic and seismic signatures from two different vehicle types, a tracked vehicle and a heavy truck.},
	keywords = {asr, classification, it, neuralnetworks},
	issn = {01419331},
	doi = {10.1016/j.micpro.2007.02.005},
	url = {http://dx.doi.org/10.1016/j.micpro.2007.02.005},
	author = {Mazarakis, G. and Avaritsiotis, J.}
}
@article {Zeng2005Development,
	title = {Development of a noise sources classification system based on new method for feature selection},
	journal = {Applied Acoustics},
	volume = {66},
	number = {10},
	year = {2005},
	month = {October},
	pages = {1196{\textendash}1205},
	abstract = {Feature selection of noise sources is important for noise sources detection and classification. In this paper, a new rough set based feature selection method has been given. Based on the method, a noise sources automatic classification system (NSACS) has been designed and validated. The key idea of the method is that most effective features can distinguish the most number of samples belonging to different classes of noise sources, if they are used for classification. This new approach has been applied into the system NSACS to select relevant features for artificial datasets and real-world datasets and the results have shown that this approach can correctly select all the relevant features of artificial datasets and at the same time it can drastically reduce the number of features. From the experiments, it can be found that to consider all the five datasets, the number of classification features after selection drops to 35\% and the accurate classification rate increases about 14\%. For the underwater noise sources dataset the number of features drops to 1/5 and the accurate classification rate increases about 6\% after feature selection.},
	keywords = {asr, bioacoustics, classification, it},
	issn = {0003682X},
	doi = {10.1016/j.apacoust.2005.02.007},
	url = {http://dx.doi.org/10.1016/j.apacoust.2005.02.007},
	author = {Zeng, X. and Zhan, Y.}
}
@article {Adi2010Acoustic,
	title = {Acoustic censusing using automatic vocalization classification and identity recognition},
	journal = {The Journal of the Acoustical Society of America},
	volume = {127},
	number = {2},
	year = {2010},
	pages = {874+},
	keywords = {asr, bioacoustics, birds, monitoring},
	issn = {00014966},
	doi = {10.1121/1.3273887},
	url = {http://dx.doi.org/10.1121/1.3273887},
	author = {Adi, Kuntoro and Johnson, Michael T. and Osiejuk, Tomasz S.}
}
@article {VanCompernolle2001Recognizing,
	title = {Recognizing speech of goats, wolves, sheep and {\textellipsis} non-natives},
	journal = {Speech Communication},
	volume = {35},
	number = {1-2},
	year = {2001},
	month = {August},
	pages = {71{\textendash}79},
	abstract = {This paper reviews the current understanding of acoustic{\textendash}phonetic issues and the problems arising when trying to recognize speech from non-native speakers. Conceptually, regional accents are well modeled by systematic shifts in pronunciation. Therefore, simultaneous recognition of multiple regional variants may be performed by using multiple acoustic models in parallel, or by adding pronunciation variants in the dictionary. Recognition of non-native speech is much more difficult because it is influenced both by the native language of the speaker and non-native target language. It is characterized by a much greater speaker variability due to different levels of proficiency. A few language-pair specific transformation rules describing prototypical nativized pronunciations was found to be useful both in general speech recognition and in dedicated applications. However, due to the nature of the errors and the cross-language transformations, non-native speech recognition will remain inherently much harder. Moreover, the trend in speech recognition towards more detailed modeling seems to be counterproductive for the recognition of non-native speech and limits progress in this field.},
	keywords = {asr, bioacoustics, linguistics, transcription},
	issn = {01676393},
	doi = {10.1016/S0167-6393(00)00096-0},
	url = {http://dx.doi.org/10.1016/S0167-6393(00)00096-0},
	author = {Van Compernolle, D.}
}
@article {Benzeghiba2007Automatic,
	title = {Automatic speech recognition and speech variability: A review},
	journal = {Speech Communication},
	volume = {49},
	number = {10-11},
	year = {2007},
	month = {October},
	pages = {763{\textendash}786},
	abstract = {Major progress is being recorded regularly on both the technology and exploitation of automatic speech recognition (ASR) and spoken language systems. However, there are still technological barriers to flexible solutions and user satisfaction under some circumstances. This is related to several factors, such as the sensitivity to the environment (background noise), or the weak representation of grammatical and semantic knowledge. Current research is also emphasizing deficiencies in dealing with variation naturally present in speech. For instance, the lack of robustness to foreign accents precludes the use by specific populations. Also, some applications, like directory assistance, particularly stress the core recognition technology due to the very high active vocabulary (application perplexity). There are actually many factors affecting the speech realization: regional, sociolinguistic, or related to the environment or the speaker herself. These create a wide range of variations that may not be modeled correctly (speaker, gender, speaking rate, vocal effort, regional accent, speaking style, non-stationarity, etc.), especially when resources for system training are scarce. This paper outlines current advances related to these topics.},
	keywords = {asr, bioacoustics, review, speechrecognition},
	issn = {01676393},
	doi = {10.1016/j.specom.2007.02.006},
	url = {http://dx.doi.org/10.1016/j.specom.2007.02.006},
	author = {Benzeghiba, M. and Demori, R. and Deroo, O. and Dupont, S. and Erbes, T. and Jouvet, D. and Fissore, L. and Laface, P. and Mertins, A. and Ris, C.}
}
@article {Kroger2009Towards,
	title = {Towards a neurocomputational model of speech production and perception},
	journal = {Speech Communication},
	volume = {51},
	number = {9},
	year = {2009},
	month = {September},
	pages = {793{\textendash}809},
	abstract = {The limitation in performance of current speech synthesis and speech recognition systems may result from the fact that these systems are not designed with respect to the human neural processes of speech production and perception. A neurocomputational model of speech production and perception is introduced which is organized with respect to human neural processes of speech production and perception. The production{\textendash}perception model comprises an artificial computer-implemented vocal tract as a front-end module, which is capable of generating articulatory speech movements and acoustic speech signals. The structure of the production{\textendash}perception model comprises motor and sensory processing pathways. Speech knowledge is collected during training stages which imitate early stages of speech acquisition. This knowledge is stored in artificial self-organizing maps. The current neurocomputational model is capable of producing and perceiving vowels, VC-, and CV-syllables (V~=~vowels and C~=~voiced plosives). Basic features of natural speech production and perception are predicted from this model in a straight forward way: Production of speech items is feedforward and feedback controlled and phoneme realizations vary within perceptually defined regions. Perception is less categorical in the case of vowels in comparison to consonants. Due to its human-like production{\textendash}perception processing the model should be discussed as a basic module for more technical relevant approaches for high-quality speech synthesis and for high performance speech recognition.},
	keywords = {asr, bioacoustics, linguistics, speech, speechrecognition},
	issn = {01676393},
	doi = {10.1016/j.specom.2008.08.002},
	url = {http://dx.doi.org/10.1016/j.specom.2008.08.002},
	author = {Kr{\"o}ger, Bernd J. and Kannampuzha, Jim and Neuschaefer-Rube, Christiane}
}
@article {Barker1999Sinewave,
	title = {Is the sine-wave speech cocktail party worth attending?},
	journal = {Speech Communication},
	volume = {27},
	number = {3-4},
	year = {1999},
	month = {April},
	pages = {159{\textendash}174},
	abstract = {Listeners are remarkably adept at recognising speech that has undergone extensive spectral reduction. Natural speech can be reproduced using as few as three time-varying sinusoids mimicking the corresponding speech formants. Untrained listeners are able to transcribe this \&lsquo;sine-wave{\textquoteright} speech with a high degree of reliability. Coherent phonetic percepts generated by sine-wave speech occur despite an apparent lack of those cues on which low level auditory grouping processes are believed to operate. Consequently, it has been proposed that speech perception is governed by processes operating independently of those described by auditory scene analysis. This paper re-examines the evidence provided by previous perceptual studies of sine-wave speech and presents new data, from perceptual studies employing stimuli constructed from simultaneous sine-wave wave speech sources. These new studies suggest that in conditions that are closer to those of everyday listening, grouping cues have an important role in the formation of coherent speech percepts. In conjunction with these perceptual studies, results from automatic segregation and recognition tasks suggest that sine-wave speech contains sufficient low level, non speech-specific, structure to allow partial descriptions of sine-wave sources to be recovered from two-source mixtures. It is argued that these partial descriptions may be sufficient to account for the limited intelligibility observed in two-source sine-wave speech listening tests.},
	keywords = {asr, bioacoustics, partyeffect, recognition},
	doi = {10.1016/S0167-6393(98)00081-8},
	url = {http://dx.doi.org/10.1016/S0167-6393(98)00081-8},
	author = {Barker, Jon and Cooke, Martin}
}
@article {CelisMurillo2009Using,
	title = {Using soundscape recordings to estimate bird species abundance, richness, and composition},
	journal = {Journal of Field Ornithology},
	volume = {80},
	number = {1},
	year = {2009},
	month = {March},
	pages = {64{\textendash}78},
	publisher = {Blackwell Publishing},
	keywords = {asr, bioaocustics, monitoring, soundscape},
	issn = {02738570},
	doi = {10.1111/j.1557-9263.2009.00206.x},
	url = {http://dx.doi.org/10.1111/j.1557-9263.2009.00206.x},
	author = {Celis-Murillo, Antonio and Deppe, Jill L. and Allen, Michael F.}
}
@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.}
}
@conference {Solomon2008Maritime,
	title = {Maritime acoustic detection of aircraft to increase flight safety and homeland security: an experimental study},
	booktitle = {Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Security and Homeland Defense VII},
	volume = {6943},
	number = {1},
	year = {2008},
	pages = {69430Z+},
	publisher = {SPIE},
	organization = {SPIE},
	abstract = {For several years ARL has studied acoustics to track vehicles, helicopters, Unmanned Aerial Vehicles (UAV) and others targets of interest. More recently these same acoustic sensors were placed on a simulated buoy in an attempt to detect and track aircraft over a large body of water. This report will investigate the advantages of using acoustic arrays to track air and water craft from a fixed floating platform as well as potential concerns associated with this technology. Continuous monitoring of aircraft overflight will increase situational awareness while persistent monitoring of commercial and military flight paths increases overall homeland security.},
	keywords = {acoustics, amibio, asr, monitoring},
	url = {http://scitation.aip.org/getabs/servlet/GetabsServlet?prog=normal\&id=PSISDG00694300000169430Z000001\&idtype=cvips\&gifs=yes},
	author = {Solomon, Latasha and Sim, Leng and Tenney, Stephen},
	editor = {Carapezza, Edward M.}
}
@article {Sessler1991Acoustic,
	title = {Acoustic sensors},
	journal = {Sensors and Actuators A: Physical},
	volume = {26},
	number = {1-3},
	year = {1991},
	month = {March},
	pages = {323{\textendash}330},
	abstract = {An overview of new developments in the field of microphones or acoustic sensors, based on silicon, optical-waveguide and polymer technologies, is given. The silicon sensors are produced on silicon wafers by micromechanical methods. Subminiature transducers of this kind, utilizing the condenser or piezoelectric principles, are made with membranes about 1 mm2 in area and less than 1 [mu]m thick. Other micromechanical sensors based on sound-pressure-induced modulation of the drain current in a field-effect transistor are also under study. Optical-waveguide sensors are either made of glass fibers or of waveguides on glass or polymer substrates and detect phase or intensity modulations of the transmitted light caused by the sound waves. Acoustic sensors of this type are already in use as hydrophones and are presently under study as microphones. Polymer electret sensors find widespread applications as microphones and ultrasonic transducers. New developments include the use of electret biasing in silicon sensors and the design of electret-microphone arrays. Piezoelectric polymer sensors are being improved by the use of better materials and by innovative poling techniques, such as monomorph poling. Hybrid sensors using silicon structures covered with piezoelectric polymer layers are also of interest.},
	keywords = {asr, bioacoustics, microphones},
	doi = {10.1016/0924-4247(91)87011-Q},
	url = {http://dx.doi.org/10.1016/0924-4247(91)87011-Q},
	author = {Sessler, G.}
}
@article {Cowling2003Comparison,
	title = {Comparison of techniques for environmental sound recognition},
	journal = {Pattern Recognition Letters},
	volume = {24},
	number = {15},
	year = {2003},
	month = {November},
	pages = {2895{\textendash}2907},
	abstract = {This paper presents a comprehensive comparative study of artificial neural networks, learning vector quantization and dynamic time warping classification techniques combined with stationary/non-stationary feature extraction for environmental sound recognition. Results show 70\% recognition using mel frequency cepstral coefficients or continuous wavelet transform with dynamic time warping.},
	keywords = {asr, environment, it, recognition, soundscapes},
	issn = {01678655},
	doi = {10.1016/S0167-8655(03)00147-8},
	url = {http://dx.doi.org/10.1016/S0167-8655(03)00147-8},
	author = {Cowling, M.}
}
@conference {Girod2006Design,
	title = {The design and implementation of a self-calibrating distributed acoustic sensing platform},
	booktitle = {SenSys {\textquoteright}06: Proceedings of the 4th international conference on Embedded networked sensor systems},
	year = {2006},
	pages = {71{\textendash}84},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {We present the design, implementation, and evaluation of the Acoustic Embedded Networked Sensing Box (ENSBox), a platform for prototyping rapid-deployable distributed acoustic sensing systems, particularly distributed source localization. Each ENSBox integrates an ARM processor running Linux and supports key facilities required for source localization: a sensor array, wireless network services, time synchronization, and precise self-calibration of array position and orientation. The ENSBox{\textquoteright}s integrated, high precision self-calibration facility sets it apart from other platforms. This self-calibration is precise enough to support acoustic source localization applications in complex, realistic environments: e.g., 5 cm average 2D position error and 1.5 degree average orientation error over a partially obstructed 80x50 m outdoor area. Further, our integration of array orientation into the position estimation algorithm is a novel extension of traditional multilateration techniques. We present the result of several different test deployments, measuring the performance of the system in urban settings, as well as forested, hilly environments with obstructing foliage and 20-30 m distances between neighboring nodes.},
	keywords = {amibio, asr, bioacoustics, calibration, ensbox, measurement},
	isbn = {1-59593-343-3},
	doi = {10.1145/1182807.1182815},
	url = {http://dx.doi.org/10.1145/1182807.1182815},
	author = {Girod, Lewis and Lukac, Martin and Trifa, Vlad and Estrin, Deborah}
}
@booklet {Marine,
	title = {Marine Ecology Progress Series "Acoustics in marine ecology"},
	year = {Submitted},
	keywords = {amibio, asr, bioacoustics, marine, underwater, wireless},
	url = {http://www.int-res.com/articles/theme/m395p223.pdf}
}
