@book{evansDomaindrivenDesignTackling2004,
  title = {Domain-Driven Design: Tackling Complexity in the Heart of Software},
  shorttitle = {Domain-Driven Design},
  author = {Evans, Eric},
  year = {2004},
  publisher = {{Addison-Wesley Professional}},
  file = {/Users/davidnicholson/Zotero/storage/ZSIKLDE9/Evans - 2004 - Domain-driven design tackling complexity in the h.pdf;/Users/davidnicholson/Zotero/storage/I5FYUS5U/books.html}
}

@article{hauserFacultyLanguageWhat2002,
  title = {The {{Faculty}} of {{Language}}: {{What Is It}}, {{Who Has It}}, and {{How Did It Evolve}}?},
  shorttitle = {The {{Faculty}} of {{Language}}},
  author = {Hauser, Marc D. and Chomsky, Noam and Fitch, W. Tecumseh},
  year = {2002},
  month = nov,
  journal = {Science},
  volume = {298},
  number = {5598},
  pages = {1569--1579},
  issn = {0036-8075, 1095-9203},
  doi = {10.1126/science.298.5598.1569},
  urldate = {2022-07-06},
  abstract = {We argue that an understanding of the faculty of language requires substantial interdisciplinary cooperation. We suggest how current developments in linguistics can be profitably wedded to work in evolutionary biology, anthropology, psychology, and neuroscience. We submit that a distinction should be made between the faculty of language in the broad sense (FLB) and in the narrow sense (FLN). FLB includes a sensory-motor system, a conceptual-intentional system, and the computational mechanisms for recursion, providing the capacity to generate an infinite range of expressions from a finite set of elements. We hypothesize that FLN only includes recursion and is the only uniquely human component of the faculty of language. We further argue that FLN may have evolved for reasons other than language, hence comparative studies might look for evidence of such computations outside of the domain of communication (for example, number, navigation, and social relations).},
  langid = {english},
  file = {/Users/davidnicholson/Zotero/storage/HY959NW7/Hauser et al. - 2002 - The Faculty of Language What Is It, Who Has It, a.pdf}
}

@article{katzdaniels.UnderstandingSoftwareCommunity2022,
  title = {Understanding {{Software Community Formation}}},
  author = {Katz, Daniel S. and Barker, Michelle and Bangerth, Wolfgang and Crawford, T. Daniel and Elmer, Peter and Harrow, Jen and Hwang, Lorraine and Psomopoulos, Fotis and Stewart, Graeme A. and Zentner, Michael},
  year = {2022},
  month = feb,
  publisher = {{Zenodo}},
  doi = {10.5281/ZENODO.6249316},
  urldate = {2022-07-02},
  abstract = {a talk presented in the Topology Optimization Webinar, webinar 20, "Future of Scientific and Engineering Computing in Research Community and Industry", 24 February 2022.},
  copyright = {Creative Commons Attribution 4.0 International, Open Access},
  langid = {english},
  file = {/Users/davidnicholson/Zotero/storage/CAMC93ET/Katz, Daniel S. et al. - 2022 - Understanding Software Community Formation.pdf}
}

@article{kershenbaumAcousticSequencesNonhuman2016,
  title = {Acoustic Sequences in Non-Human Animals: A Tutorial Review and Prospectus: {{Acoustic}} Sequences in Animals},
  shorttitle = {Acoustic Sequences in Non-Human Animals},
  author = {Kershenbaum, Arik and Blumstein, Daniel T. and Roch, Marie A. and Ak{\c c}ay, {\c C}a{\u g}lar and Backus, Gregory and Bee, Mark A. and Bohn, Kirsten and Cao, Yan and Carter, Gerald and C{\"a}sar, Cristiane and Coen, Michael and DeRuiter, Stacy L. and Doyle, Laurance and Edelman, Shimon and {Ferrer-i-Cancho}, Ramon and Freeberg, Todd M. and Garland, Ellen C. and Gustison, Morgan and Harley, Heidi E. and Huetz, Chlo{\'e} and Hughes, Melissa and Hyland Bruno, Julia and Ilany, Amiyaal and Jin, Dezhe Z. and Johnson, Michael and Ju, Chenghui and Karnowski, Jeremy and Lohr, Bernard and Manser, Marta B. and McCowan, Brenda and Mercado, Eduardo and Narins, Peter M. and Piel, Alex and Rice, Megan and Salmi, Roberta and Sasahara, Kazutoshi and Sayigh, Laela and Shiu, Yu and Taylor, Charles and Vallejo, Edgar E. and Waller, Sara and {Zamora-Gutierrez}, Veronica},
  year = {2016},
  month = feb,
  journal = {Biological Reviews},
  volume = {91},
  number = {1},
  pages = {13--52},
  issn = {14647931},
  doi = {10.1111/brv.12160},
  urldate = {2021-11-22},
  abstract = {Animal acoustic communication often takes the form of complex sequences, made up of multiple distinct acoustic units. Apart from the well-known example of birdsong, other animals such as insects, amphibians, and mammals (including bats, rodents, primates, and cetaceans) also generate complex acoustic sequences. Occasionally, such as with birdsong, the adaptive role of these sequences seems clear (e.g. mate attraction and territorial defence). More often however, researchers have only begun to characterise \textendash{} let alone understand \textendash{} the significance and meaning of acoustic sequences. Hypotheses abound, but there is little agreement as to how sequences should be defined and analysed. Our review aims to outline suitable methods for testing these hypotheses, and to describe the major limitations to our current and near-future knowledge on questions of acoustic sequences. This review and prospectus is the result of a collaborative effort between 43 scientists from the fields of animal behaviour, ecology and evolution, signal processing, machine learning, quantitative linguistics, and information theory, who gathered for a 2013 workshop entitled, `Analysing vocal sequences in animals'. Our goal is to present not just a review of the state of the art, but to propose a methodological framework that summarises what we suggest are the best practices for research in this field, across taxa and across disciplines. We also provide a tutorial-style introduction to some of the most promising algorithmic approaches for analysing sequences. We divide our review into three sections: identifying the distinct units of an acoustic sequence, describing the different ways that information can be contained within a sequence, and analysing the structure of that sequence. Each of these sections is further subdivided to address the key questions and approaches in that area. We propose a uniform, systematic, and comprehensive approach to studying sequences, with the goal of clarifying research terms used in different fields, and facilitating collaboration and comparative studies. Allowing greater interdisciplinary collaboration will facilitate the investigation of many important questions in the evolution of communication and sociality.},
  langid = {english},
  file = {/Users/davidnicholson/Zotero/storage/ABH3RYWI/Kershenbaum et al. - 2016 - Acoustic sequences in non-human animals a tutoria.pdf}
}

@article{sainburgComputationalNeuroethologyVocal2021,
  title = {Toward a {{Computational Neuroethology}} of {{Vocal Communication}}: {{From Bioacoustics}} to {{Neurophysiology}}, {{Emerging Tools}} and {{Future Directions}}},
  shorttitle = {Toward a {{Computational Neuroethology}} of {{Vocal Communication}}},
  author = {Sainburg, Tim and Gentner, Timothy Q.},
  year = {2021},
  month = dec,
  journal = {Frontiers in Behavioral Neuroscience},
  volume = {15},
  pages = {811737},
  issn = {1662-5153},
  doi = {10.3389/fnbeh.2021.811737},
  urldate = {2022-01-22},
  abstract = {Recently developed methods in computational neuroethology have enabled increasingly detailed and comprehensive quantification of animal movements and behavioral kinematics. Vocal communication behavior is well poised for application of similar large-scale quantification methods in the service of physiological and ethological studies. This review describes emerging techniques that can be applied to acoustic and vocal communication signals with the goal of enabling study beyond a small number of model species. We review a range of modern computational methods for bioacoustics, signal processing, and brain-behavior mapping. Along with a discussion of recent advances and techniques, we include challenges and broader goals in establishing a framework for the computational neuroethology of vocal communication.},
  langid = {english},
  file = {/Users/davidnicholson/Zotero/storage/AZFQEZ85/Sainburg and Gentner - 2021 - Toward a Computational Neuroethology of Vocal Comm.pdf}
}

@article{stowellComputationalBioacousticsDeep2022,
  title = {Computational Bioacoustics with Deep Learning: A Review and Roadmap},
  author = {Stowell, Dan},
  year = {2022},
  pages = {46},
  abstract = {Animal vocalisations and natural soundscapes are fascinating objects of study, and contain valuable evidence about animal behaviours, populations and ecosystems. They are studied in bioacoustics and ecoacoustics, with signal processing and analysis an important component. Computational bioacoustics has accelerated in recent decades due to the growth of affordable digital sound recording devices, and to huge progress in informatics such as big data, signal processing and machine learning. Methods are inherited from the wider field of deep learning, including speech and image processing. However, the tasks, demands and data characteristics are often different from those addressed in speech or music analysis. There remain unsolved problems, and tasks for which evidence is surely present in many acoustic signals, but not yet realised. In this paper I perform a review of the state of the art in deep learning for computational bioacoustics, aiming to clarify key concepts and identify and analyse knowledge gaps. Based on this, I offer a subjective but principled roadmap for computational bioacoustics with deep learning: topics that the community should aim to address, in order to make the most of future developments in AI and informatics, and to use audio data in answering zoological and ecological questions.},
  langid = {english},
  file = {/Users/davidnicholson/Zotero/storage/PAAGY2CU/Stowell - 2022 - Computational bioacoustics with deep learning a r.pdf}
}

@article{wirthlinModularApproachVocal2019,
  title = {A {{Modular Approach}} to {{Vocal Learning}}: {{Disentangling}} the {{Diversity}} of a {{Complex Behavioral Trait}}},
  shorttitle = {A {{Modular Approach}} to {{Vocal Learning}}},
  author = {Wirthlin, Morgan and Chang, Edward F. and Kn{\"o}rnschild, Mirjam and Krubitzer, Leah A. and Mello, Claudio V. and Miller, Cory T. and Pfenning, Andreas R. and Vernes, Sonja C. and Tchernichovski, Ofer and Yartsev, Michael M.},
  year = {2019},
  month = oct,
  journal = {Neuron},
  volume = {104},
  number = {1},
  pages = {87--99},
  issn = {08966273},
  doi = {10.1016/j.neuron.2019.09.036},
  urldate = {2021-05-22},
  langid = {english},
  file = {/Users/davidnicholson/Zotero/storage/L7JW7EGY/Wirthlin et al. - 2019 - A Modular Approach to Vocal Learning Disentanglin.pdf}
}
