scholarly journals Acoustic profiling of Orthoptera for species monitoring and discovery: present state and future needs

2017 ◽  
Author(s):  
Klaus Riede
Keyword(s):  
Animal Communication ◽  
Field Application ◽  
Computer Algorithms ◽  
Global Biodiversity Information Facility ◽  
Communication Signals ◽  
Efficient Management ◽  
Data Loggers ◽  
Identification Software ◽  
Species Descriptions ◽  
Acoustic Profiling

Background: Bioacoustic monitoring and classification of animal communication signals has developed into a powerful tool for measuring and monitoring species diversity within complex communities and habitats. The high number of stridulating species among Orthoptera allows their detection and classification in a non-invasive and economic way, particularly in habitats where visual observations are difficult or even impossible, such as tropical rainforests. Methods: Major sound archives where queried for Orthoptera songs, with special emphasis on usability as reference training libraries for computer algorithms. Results: Orthoptera songs are highly stereotyped, reliable taxonomic features. However, exploitation of songs for acoustic profiling is limited by the small number of reference recordings: existing song libraries represent only about 1,000 species, mainly from Europe and North America, covering less that 10% of extant stridulating Orthoptera species. Available databases are fragmented and lack tools for song annotation and efficient feature-based search. Results from recent bioacoustic surveys illustrate the potential of the method, but also challenges and bottlenecks impeding further progress. A major problem is time-consuming data analysis of recordings. Computer-aided identification software has been developed for classification and identification of cricket and grasshopper songs, but these tools are still far from practical field application. Discussion: A framework for acoustic profiling of Orthoptera should consist of the following components: (1) Protocols for standardised acoustic sampling, at species and community level, using acoustic data loggers for autonomous long-term recordings; (2) Open access to and efficient management of song data and voucher specimens, involving the Orthoptera Species File (OSF) and Global Biodiversity Information Facility (GBIF); (3) An infrastructure for automatised analysis and song classification; (4) Complementation and improvement of Orthoptera sound libraries, using Orthoptera Species File as taxonomic backbone and repository for representative song recordings. Taxonomists should be encouraged to deposit original recordings, particularly if they form part of species descriptions or revisions.

scholarly journals Acoustic profiling of Orthoptera for species monitoring and discovery: present state and future needs

2017 ◽  
Author(s):  
Klaus Riede
Keyword(s):  
Animal Communication ◽  
Field Application ◽  
Computer Algorithms ◽  
Global Biodiversity Information Facility ◽  
Communication Signals ◽  
Efficient Management ◽  
Data Loggers ◽  
Identification Software ◽  
Species Descriptions ◽  
Acoustic Profiling

Background: Bioacoustic monitoring and classification of animal communication signals has developed into a powerful tool for measuring and monitoring species diversity within complex communities and habitats. The high number of stridulating species among Orthoptera allows their detection and classification in a non-invasive and economic way, particularly in habitats where visual observations are difficult or even impossible, such as tropical rainforests. Methods: Major sound archives where queried for Orthoptera songs, with special emphasis on usability as reference training libraries for computer algorithms. Results: Orthoptera songs are highly stereotyped, reliable taxonomic features. However, exploitation of songs for acoustic profiling is limited by the small number of reference recordings: existing song libraries represent only about 1,000 species, mainly from Europe and North America, covering less that 10% of extant stridulating Orthoptera species. Available databases are fragmented and lack tools for song annotation and efficient feature-based search. Results from recent bioacoustic surveys illustrate the potential of the method, but also challenges and bottlenecks impeding further progress. A major problem is time-consuming data analysis of recordings. Computer-aided identification software has been developed for classification and identification of cricket and grasshopper songs, but these tools are still far from practical field application. Discussion: A framework for acoustic profiling of Orthoptera should consist of the following components: (1) Protocols for standardised acoustic sampling, at species and community level, using acoustic data loggers for autonomous long-term recordings; (2) Open access to and efficient management of song data and voucher specimens, involving the Orthoptera Species File (OSF) and Global Biodiversity Information Facility (GBIF); (3) An infrastructure for automatised analysis and song classification; (4) Complementation and improvement of Orthoptera sound libraries, using Orthoptera Species File as taxonomic backbone and repository for representative song recordings. Taxonomists should be encouraged to deposit original recordings, particularly if they form part of species descriptions or revisions.

scholarly journals Acoustic profiling of Orthoptera: present state and future needs

2018 ◽  
Vol 27 (2) ◽  
pp. 203-215 ◽  
Author(s):  
Klaus Riede
Keyword(s):  
Animal Communication ◽  
Field Application ◽  
Computer Algorithms ◽  
Global Biodiversity Information Facility ◽  
Communication Signals ◽  
Efficient Management ◽  
Data Loggers ◽  
Identification Software ◽  
Species Descriptions ◽  
Acoustic Profiling

Bioacoustic monitoring and classification of animal communication signals has developed into a powerful tool for measuring and monitoring species diversity within complex communities and habitats. The high number of stridulating species among Orthoptera allows their detection and classification in a non-invasive and economic way, particularly in habitats where visual observations are difficult or even impossible, such as tropical rainforests. Major sound archives were queried for Orthoptera songs, with special emphasis on usability as reference training libraries for computer algorithms. Orthoptera songs are highly stereotyped, reliable taxonomic features. However, exploitation of songs for acoustic profiling is limited by the small number of reference recordings: existing song libraries represent only about 1000 species, mainly from Europe and North America, covering less than 10% of extant stridulating Orthoptera species. Available databases are fragmented and lack tools for song annotation and efficient feature-based searching. Results from recent bioacoustic surveys illustrate the potential of the method, but also the challenges and bottlenecks impeding further progress. A major problem is time-consuming data analysis of recordings. Computer-aided identification software exists for classification and identification of cricket and grasshopper songs, but these tools are still far from practical for field application.A framework for acoustic profiling of Orthoptera should consist of the following components: (1) Protocols for standardized acoustic sampling, at species and community levels, using acoustic data loggers for autonomous long-term recordings; (2) Open access to and efficient management of song data and voucher specimens, involving the Orthoptera Species File (OSF) and Global Biodiversity Information Facility (GBIF); (3) An infrastructure for automatized analysis and song classification; and (4) Complementation and improvement of Orthoptera sound libraries using OSF as the taxonomic backbone and repository for representative song recordings. Taxonomists should be encouraged, or even obliged, to deposit original recordings, particularly if they form part of species descriptions or revisions.

Patterns of evolution in human speech processing and animal communication

1998 ◽  
Vol 21 (2) ◽  
pp. 282-283
Author(s):  
Michael J. Ryan ◽  
Nicole M. Kime ◽  
Gil G. Rosenthal
Keyword(s):  
Sexual Selection ◽  
Speech Processing ◽  
Communication Systems ◽  
Animal Communication ◽  
Low Noise ◽  
Communication Signals ◽  
Human Speech ◽  
Strong Selection ◽  
Processing Data ◽  
Receiver System

We consider Sussman et al.'s suggestion that auditory biases for processing low-noise relationships among pairs of acoustic variables is a preadaptation for human speech processing. Data from other animal communication systems, especially those involving sexual selection, also suggest that neural biases in the receiver system can generate strong selection on the form of communication signals.

scholarly journals Development of Communication Behaviour: Receiver Ontogeny in Túngara Frogs and a Prospectus for a Behavioural Evolutionary Development

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Alexander T. Baugh ◽  
Kim L. Hoke ◽  
Michael J. Ryan
Keyword(s):  
Communication Systems ◽  
Animal Communication ◽  
Mate Recognition ◽  
Evolutionary Development ◽  
Communication Signals ◽  
Tungara Frogs ◽  
Signal Production ◽  
Evolution Of Signals ◽  
Evolution Of Communication ◽  
Behaviour Recognition

Most studies addressing the development of animal communication have focused on signal production rather than receiver decoding, and similar emphasis has been given to learning over nonlearning. But receivers are an integral part of a communication network, and nonlearned mechanisms appear to be more ubiquitous than learned ones in the communication systems of most animals. Here we review the results of recent experiments and outline future directions for integrative studies on the development of a primarily nonlearned behaviour—recognition of communication signals during ontogeny in a tropical frog. The results suggest that antecedents to adult behaviours might be a common feature of developing organisms. Given the essential role that acoustic communication serves in reproduction for many organisms and that receivers can exert strong influence on the evolution of signals, understanding the evolutionary developmental basis of mate recognition will provide new insights into the evolution of communication systems.

scholarly journals Latent space visualization, characterization, and generation of diverse vocal communication signals

2019 ◽  
Author(s):  
Tim Sainburg ◽  
Marvin Thielk ◽  
Timothy Q Gentner
Keyword(s):  
Latent Variables ◽  
Vocal Communication ◽  
Animal Communication ◽  
Temporal Structure ◽  
Systematic Sampling ◽  
Acoustic Feature ◽  
Communication Signals ◽  
Animal Vocalizations ◽  
Complex Features ◽  
Low Dimensional

ABSTRACTAnimals produce vocalizations that range in complexity from a single repeated call to hundreds of unique vocal elements patterned in sequences unfolding over hours. Characterizing complex vocalizations can require considerable effort and a deep intuition about each species’ vocal behavior. Even with a great deal of experience, human characterizations of animal communication can be affected by human perceptual biases. We present here a set of computational methods that center around projecting animal vocalizations into low dimensional latent representational spaces that are directly learned from data. We apply these methods to diverse datasets from over 20 species, including humans, bats, songbirds, mice, cetaceans, and nonhuman primates, enabling high-powered comparative analyses of unbiased acoustic features in the communicative repertoires across species. Latent projections uncover complex features of data in visually intuitive and quantifiable ways. We introduce methods for analyzing vocalizations as both discrete sequences and as continuous latent variables. Each method can be used to disentangle complex spectro-temporal structure and observe long-timescale organization in communication. Finally, we show how systematic sampling from latent representational spaces of vocalizations enables comprehensive investigations of perceptual and neural representations of complex and ecologically relevant acoustic feature spaces.

Modeling Complex Phenotypes: Generalized Linear Models Using Spectrogram Predictors of Animal Communication Signals

Biometrics ◽  
2009 ◽  
Vol 66 (3) ◽  
pp. 914-924 ◽  
Author(s):  
Scott H. Holan ◽  
Christopher K. Wikle ◽  
Laura E. Sullivan-Beckers ◽  
Reginald B. Cocroft
Keyword(s):  
Generalized Linear Models ◽  
Linear Models ◽  
Animal Communication ◽  
Communication Signals ◽  
Complex Phenotypes

scholarly journals Robustness and complexity co-constructed in multimodal signalling networks

2007 ◽  
Vol 362 (1479) ◽  
pp. 441-447 ◽  
Author(s):  
Nihat Ay ◽  
Jessica Flack ◽  
David C Krakauer
Keyword(s):  
Information Transfer ◽  
Animal Communication ◽  
Communication Signals ◽  
Multichannel Signals ◽  
Feed Forward Neural Networks ◽  
Selection For ◽  
Highly Correlated ◽  
Multimodal Signalling ◽  
Signalling Systems ◽  
High Information

In animal communication, signals are frequently emitted using different channels (e.g. frequencies in a vocalization) and different modalities (e.g. gestures can accompany vocalizations). We explore two explanations that have been provided for multimodality: (i) selection for high information transfer through dedicated channels and (ii) increasing fault tolerance or robustness through multichannel signals. Robustness relates to an accurate decoding of a signal when parts of a signal are occluded. We show analytically in simple feed-forward neural networks that while a multichannel signal can solve the robustness problem, a multimodal signal does so more effectively because it can maximize the contribution made by each channel while minimizing the effects of exclusion. Multimodality refers to sets of channels where within each set information is highly correlated. We show that the robustness property ensures correlations among channels producing complex, associative networks as a by-product. We refer to this as the principle of robust overdesign . We discuss the biological implications of this for the evolution of combinatorial signalling systems; in particular, how robustness promotes enough redundancy to allow for a subsequent specialization of redundant components into novel signals.

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