Perspective and Promise: a Century of Insect Acoustic Detection and Monitoring

Mankin, Richard W.; Hagstrum, David W.; Smith, Maudy Th.; Roda, Amy; Kairo, M. T. K.

doi:10.1093/ae/57.1.30

Cited by 186 publications

(196 citation statements)

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“…The spectral ranges of impulses detected in this study are similar to those observed in other studies of insect sounds produced in stored maize or wheat (Vick et al 1988b;Mankin et al 2011). Therefore, it can be expected that any insect acoustic detection instrument designed for small-scale storage facilities would need to detect signals in the range of 500-7,000 Hz to ensure coverage of the most prominent frequencies of signals produced by internally feeding stored product insects in maize.…”

Section: Discussionsupporting

confidence: 83%

“…Three recordings conducted on different days without the pouch in the bag of corn confirmed that background sounds occurred at a rate of < 0.01 s -1 . Intervals of 180 s that contained 3-to 30-ms sound impulses (Vick et al 1988b;Mankin et al 1996Mankin et al , 2011 were amplified 78 dB (Model AED-2010, AEC, Inc.), where dB is 20 log 10 (V output ) and V output is the signal delivered by the amplifier in units of microvolts. The amplified signals were collected at a 44.1 kHz sampling rate onto a digital audio recorder (Model PMD661, Marantz, Mahwah, New Jersey, USA).…”

Section: Methodsmentioning

confidence: 99%

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Acoustic Indicators for Targeted Detection of Stored Product and Urban Insect Pests by Inexpensive Infrared, Acoustic, and Vibrational Detection of Movement

Mankin¹,

Hodges²,

Nagle³

et al. 2010

jnl. econ. entom.

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Section: Discussionsupporting

confidence: 83%

Section: Methodsmentioning

confidence: 99%

Acoustic Indicators for Targeted Detection of Stored Product and Urban Insect Pests by Inexpensive Infrared, Acoustic, and Vibrational Detection of Movement

Mankin¹,

Hodges²,

Nagle³

et al. 2010

jnl. econ. entom.

Self Cite

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“…It has been estimated that the inadvertent introduction of the Asian Longhorn Beetle (Anoplophora glabripennis) with a shipment of lumber could cost the US lumber industry tens of billions of dollars (Nowak et al 2001). It has been noted that different beetle species have subtle distinctive chewing sounds, and ultra sensitive sensors that can detect these sounds are being produced (Mankin et al 2011). As a very recent survey of acoustic insect detection noted, "The need for nondestructive, rapid, and inexpensive means of detecting hidden insect infestations is not likely to diminish in the near future" (Nowak et al 2001).…”

Section: Introductionmentioning

confidence: 99%

Monitoring and Mining Animal Sounds in Visual Space

2012

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Monitoring animals by the sounds they produce is an important and challenging task, whether the application is outdoors in a natural habitat, or in the controlled environment of a laboratory setting.In the former case, the density and diversity of animal sounds can act as a measure of biodiversity. In the latter case, researchers often create control and treatment groups of animals, expose them to different interventions, and test for different outcomes. One possible manifestation of different outcomes may be changes in the bioacoustics of the animals.With such a plethora of important applications, there have been significant efforts to build bioacoustic classification tools. However, we argue that most current tools are severely limited. They often require the careful tuning of many parameters (and thus huge amounts of training data), are either too computationally expensive for deployment in resource-limited sensors, specialized for a very small group of species, or are simply not accurate enough to be useful.In this work we introduce a novel bioacoustic recognition/classification framework that mitigates or solves all of the above problems. We propose to classify animal sounds in the visual space, by treating the texture of their sonograms as an acoustic fingerprint using a recently introduced parameter-free texture measure as a distance measure. We further show that by searching for the most representative acoustic fingerprint, we can significantly outperform other techniques in terms of speed and accuracy.

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“…It has been estimated that the inadvertent introduction of the Asian Longhorn Beetle (Anoplophora glabripennis) with a shipment of lumber could cost the US lumber industry tens of billions of dollars [22]. It has been noted that different beetle species have subtlety distinctive chewing sounds, and ultra sensitive sensors that can detect these sounds are being produced [17]. As a very recent survey of acoustic insect detection noted, "The need for nondestructive, rapid, and inexpensive means of detecting hidden insect infestations is not likely to diminish in the near future" [22].…”

Section: Introductionmentioning

confidence: 99%

Monitoring and Mining Insect Sounds in Visual Space

Hao

Campana

Keogh

2012

Proceedings of the 2012 SIAM International Conference on Data Mining

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Monitoring animals by the sounds they produce is an important and challenging task, whether the application is outdoors in a natural habitat, or in the controlled environment of a laboratory setting. In the former case the density and diversity of animal sounds can act as a measure of biodiversity. In the latter case, researchers often create control and treatment groups of animals, expose them to different interventions, and test for different outcomes. One possible manifestation of different outcomes may be changes in the bioacoustics of the animals. With such a plethora of important applications, there have been significant efforts to build bioacoustic classification tools. However, we argue that most current tools are severely limited. They often require the careful tuning of many parameters (and thus huge amounts of training data), they are too computationally expensive for deployment in resource-limited sensors, they are specialized for a very small group of species, or they are simply not accurate enough to be useful. In this work we introduce a novel bioacoustic recognition/classification framework that mitigates or solves all of the above problems. We propose to classify animal sounds in the visual space, by treating the texture of their spectrograms as an acoustic fingerprint using a recently introduced parameter-free texture measure as a distance measure. We further show that by searching for the most representative acoustic fingerprint we can significantly outperform other techniques in terms of speed and accuracy.

show abstract

Perspective and Promise: a Century of Insect Acoustic Detection and Monitoring

Cited by 186 publications

References 0 publications

Acoustic Indicators for Targeted Detection of Stored Product and Urban Insect Pests by Inexpensive Infrared, Acoustic, and Vibrational Detection of Movement

Acoustic Indicators for Targeted Detection of Stored Product and Urban Insect Pests by Inexpensive Infrared, Acoustic, and Vibrational Detection of Movement

Monitoring and Mining Animal Sounds in Visual Space

Monitoring and Mining Insect Sounds in Visual Space

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