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Performance of an Electrothermal MEMS Cantilever Resonator with Fano-Resonance Annoyance under Cigarette Smoke Exposure (Data)
<p>Origin projects, figures and LabVIEW software used for the article "Performance of an Electrothermal MEMS Cantilever Resonator with Fano-Resonance Annoyance under Cigarette Smoke Exposure", published in <em>Sensors </em>on 14 Jun 2021.</p>
Vehicle driving actions for loudness and annoyance perception
<p>This dataset contains 360º videos of 36 driving actions. The videos are organized by vehicles: a white car (Opel Corsa 2016), a dark red motorbike (Suzuki VX 800 800cc 1994), a dark blue van (Fort Transit FT100 1999) and a street sweeper (Kärcher MC 50).</p> <p>The recordings were done with a 360º camera (Xiami Mi Sphere Camera) and a tethraedral microphone (Core Sound TetraMic). The microphone recordings were synthesized to stereo recordings (as if the microphones were pointing at +-60º azimuth) with VVMic from VVAudio. The sound pressure level was measured with a level meter.</p> <p>The driving actions are the following. The sound pressure level was calculated as the fast maximum level (maximum dB SPL in windows of 125ms).</p> <ul> <li>Car <ol> <li><a href="https://www.youtube.com/watch?v=zvmhiE3NXx8&t=0s">00:00</a> Scene 1 - Stand by (close) - 72.5 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=zvmhiE3NXx8&t=18s">00:18</a> Scene 2 - Accelerate (close) LR - 84.9 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=zvmhiE3NXx8&t=36s">00:36</a> Scene 3 - 30 km/h (far) RL - 70.4 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=zvmhiE3NXx8&t=54s">00:54</a> Scene 4 - 50 km/h (close) LR - 81.0 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=zvmhiE3NXx8&t=72s">01:12</a> Scene 5 - Break and stop (far) RL - 79.8 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=zvmhiE3NXx8&t=90s">01:30</a> Scene 6 - Stand by (far) - 67.8 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=zvmhiE3NXx8&t=108s">01:48</a> Scene 7 - Accelerate (far) RL - 79.3 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=zvmhiE3NXx8&t=126s">02:06</a> Scene 8 - 30 km/h (close) LR - 82.4 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=zvmhiE3NXx8&t=144s">02:24</a> Scene 9 - 50 km/h (far) RL - 75.7 dB SP</li> <li><a href="https://www.youtube.com/watch?v=zvmhiE3NXx8&t=162s">02:42</a> Scene 10 - Break and stop (close) LR - 75.9 dB SPL</li> </ol> </li> <li>Motorbike <ol> <li><a href="https://www.youtube.com/watch?v=3wa6zeEbJ5w&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=2&t=0s">00:00</a> Scene 1 - Stand by (close) - 83.7 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=3wa6zeEbJ5w&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=2&t=18s">00:18</a> Scene 2 - Accelerate (close) LR - 92.5 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=3wa6zeEbJ5w&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=2&t=36s">00:36</a> Scene 3 - 30 km/h (far) RL - 83.2 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=3wa6zeEbJ5w&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=2&t=54s">00:54</a> Scene 4 - 50 km/h (close) LR - 90.2 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=3wa6zeEbJ5w&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=2&t=72s">01:12</a> Scene 5 - Break and stop (far) RL - 81.8 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=3wa6zeEbJ5w&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=2&t=90s">01:30</a> Scene 6 - Stand by (far) - 78.1 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=3wa6zeEbJ5w&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=2&t=108s">01:48</a> Scene 7 - Accelerate (far) RL - 86.8 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=3wa6zeEbJ5w&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=2&t=126s">02:06</a> Scene 8 - 30 km/h (close) LR - 91.2 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=3wa6zeEbJ5w&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=2&t=144s">02:24</a> Scene 9 - 50 km/h (far) RL - 84.3 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=3wa6zeEbJ5w&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=2&t=162s">02:42</a> Scene 10 - Break and stop (close) LR - 83.4 dB SPL</li> </ol> </li> <li>Van <ol> <li><a href="https://www.youtube.com/watch?v=DdaWvKfVILo&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=3&t=0s">00:00</a> Scene 1 - Stand by (close) - 84.4 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=DdaWvKfVILo&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=3&t=18s">00:18</a> Scene 2 - Accelerate (close) LR - 93.8 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=DdaWvKfVILo&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=3&t=36s">00:36</a> Scene 3 - 30 km/h (far) RL - 81.4 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=DdaWvKfVILo&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=3&t=54s">00:54</a> Scene 4 - 50 km/h (close) LR - 92.3 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=DdaWvKfVILo&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=3&t=72s">01:12</a> Scene 5 - Break and stop (far) RL - 81.4 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=DdaWvKfVILo&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=3&t=90s">01:30</a> Scene 6 - Stand by (far) - 79.8 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=DdaWvKfVILo&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=3&t=108s">01:48</a> Scene 7 - Accelerate (far) RL - 85.0 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=DdaWvKfVILo&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=3&t=126s">02:06</a> Scene 8 - 30 km/h (close) LR - 85.9 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=DdaWvKfVILo&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=3&t=144s">02:24</a> Scene 9 - 50 km/h (far) RL - 85.6 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=DdaWvKfVILo&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=3&t=162s">02:42</a> Scene 10 - Break and stop (close) LR - 83.1 dB SPL</li> </ol> </li> <li>Street sweeper <ol> <li><a href="https://www.youtube.com/watch?v=4ssqZk78JYc&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=4&t=0s">00:00</a> Scene 1 - Stand by (close) - max 81.9 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=4ssqZk78JYc&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=4&t=18s">00:18</a> Scene 2 - Sweeper on (close) - max 93.7 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=4ssqZk78JYc&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=4&t=36s">00:36</a> Scene 3 - Move forward (close) LR - max 94.6 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=4ssqZk78JYc&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=4&t=54s">00:54</a> Scene 4 - Stand by (far) - max 79.6 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=4ssqZk78JYc&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=4&t=72s">01:12</a> Scene 5 - Sweeper on (far) - max 84.4 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=4ssqZk78JYc&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=4&t=90s">01:30</a> Scene 6 - Move forward (far) RL - max 84.3dB SPL</li> </ol> </li> </ul> <p> </p> <p>You can also find the videos in <a href="https://www.youtube.com/playlist?list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB">Youtube</a>.</p> <p>Reference:</p> <p>Llorach, Gerard, Matthias Vormann, Volker Hohmann, Dirk Oetting, Christina Fitschen, Markus Meis, Melanie Krüger, and Michael Schulte. "Vehicle noise: Loudness ratings, loudness models and future experiments with audiovisual immersive simulations." In <em>INTER-NOISE and NOISE-CON Congress and Conference Proceedings</em>, vol. 259, no. 3, pp. 6752-6759. Institute of Noise Control Engineering, 2019.</p>
DeLTA (Deep Learning Techniques for noise Annoyance detection) Dataset
<p>The Deep Learning Techniques for noise Annoyance detection (DeLTA) dataset comprises 2,980 15-second binaural audio recordings collected in urban public spaces across London, Venice, Granada, and Groningen (sourced from <a href="https://doi.org/10.5281/zenodo.5578572">International Soundscape Database</a>). A remote listening experiment was designed and hosted on Gorilla Experiment Builder, a professional online platform used for studying complex behaviours. The survey was then distributed via Prolific to a pool of pre-registered participants (N=1,221), and data collected between July 5th and July 23rd, 2021.</p> <p>During the listening experiment, participants listened to ten 15-second-long binaural recordings of urban environments and were instructed to select all the sound sources they could identify within the recording and then to provide an annoyance rating (from 1 to 10). For the sound source recognition task, participants were provided with a list of 24 labels they could select from. To collapse these into a single set of sound sources per recording, a “consensus” approach was considered, i.e., if two or more participants identified a source as being present in a recording, this source was considered to be effectively present. This resulted in a 2890 by 24 data frame (2890 recordings, each with up to 23 possible labels present and an average annoyance rating). On average, each recording has 3.2 identified sound sources present.</p> <p>Due to the constraints of the online survey software, Mp3 files were used for the listening experiment. Higher quality 24- or 32-bit 48kHz WAV files can be made available from the authors upon request. Each binaural audio recording consists of a 2 channel Mp3 file.</p>
Cognitive Annoyance by Task Interruptions
<p>This data set corresponds to the analyses carried out in the following article: Bostan, I., Özcan, E., Gommers, D., & van Egmond, R. (2025). Annoyance by Task Interruptions in Healthcare Workflows: Underlying Cognitive Mechanisms. <em>Cognition, Technology, and Work</em>.</p> <div> </div> <p> </p>
Loudness and annoyance ratings of vehicle noise
<p>Loudness and annoyance ratings of the field experiment described in Llorach, Gerard; Oetting, Dirk; Krüger, Melanie; Vormann, Matthias; Fitschen, Christina; Schulte, Michael; Hohmann, Volker; Meis, Markus (2019, September). Vehicle noise: Loudness ratings, loudness models and future experiments with audiovisual immersive simulations. In <em>INTER-NOISE and NOISE-CON Congress and Conference Proceedings</em> (Vol. 259, No. 3, pp. 6752-6759). Institute of Noise Control Engineering. <a href="https://doi.org/10.5281/zenodo.4276090">https://doi.org/10.5281/zenodo.4276090</a>,</p> <p>and of the laboratory experiment in the process of publication.</p> <p>Do not hesitate to contact the main author for more information.</p> <p> </p> <p><strong>Acknowledgments</strong></p> <p>This work received funding from the EU’s H2020 research and innovation program under the MSCA GA 675324 (ENRICH), from the Deutsche Forschungsgemeinschaft (DFG, Cluster of Excellence EXC 1077/1 “Hearing4all”, and SFB1330 Projects B1 and C4).</p>
FIGURES 27–35. Paraeurycorypha Massa n. gen. ocellata Massa et Annoyer n in Orthoptera Tettigoniidae (Conocephalinae, Hexacentrinae, Phaneropterinae Mecopodinae, Hetrodinae) from some protected areas of Central African Republic
FIGURES 27–35. Paraeurycorypha Massa n. gen. ocellata Massa et Annoyer n. sp., male: habitus in dorsal view (27), lateral view of hind tibia (28), lateral view of inner tympanum of fore tibia (29), fronto-dorsal view of the head (30), lateral view of fore femur (31), dorsal view of head and pronotum (32; arrows show the mirror on the right and the stridulatory area on the left), subgenital plate, with styli and right cercus (33), lateral view of the right cercus (34), stridulatory file (35).
Figure 8 from: Moulin N, Decaëns T, Annoyer P (2017) Diversity of mantids (Dictyoptera: Mantodea) of Sangha-Mbaere Region, Central African Republic, with some ecological data and DNA barcoding. Journal of Orthoptera Research 26: 117-141. https://doi.org/10.3897/jor.26.19863
Figure 8 - Neighbor-joining tree of DNA barcodes (COI) obtained from BOLD 4.0. beta using the Kimura 2 parameter for distance model. Filters: sequence length > 200 bp.
Figure 6 from: Moulin N, Decaëns T, Annoyer P (2017) Diversity of mantids (Dictyoptera: Mantodea) of Sangha-Mbaere Region, Central African Republic, with some ecological data and DNA barcoding. Journal of Orthoptera Research 26: 117-141. https://doi.org/10.3897/jor.26.19863
Figure 6 - Specific richness of Mantodea collected with two main sampling methods; day capture (Day) and trapping with UV light at night (UV); grey chart = observed richness; dashed chart = estimated richness (ACE index) and associated standard deviation.
Figure 4 from: Moulin N, Decaëns T, Annoyer P (2017) Diversity of mantids (Dictyoptera: Mantodea) of Sangha-Mbaere Region, Central African Republic, with some ecological data and DNA barcoding. Journal of Orthoptera Research 26: 117-141. https://doi.org/10.3897/jor.26.19863
Figure 4 - Live habitus. A. Theopompella aurivillii (female) from DNNP; B. Theopompella sp. (young) from DNNP; C. Dactylopteryx flexuosa (female) from DNNP; D. Amorphoscelis griffinii (male) from DNNP; E. Amorphoscelis pulchra (male) from DNNP; F. Galinthias amoena (female) from LNP; G. Panurgica feae (female) from DNNP; H. Chloroharpax modesta (male) from DNNP; I. Anasigerpes bifasciata (female) from DNNP; J. Pseudocreobotra ocellata (female) from LNP; K. Pseudocreobotra ocellata (male) from DNNP; L. Oxypiloidea (Catasigerpes) margarethae (female) from DNNP.
Figure 2 from: Moulin N, Decaëns T, Annoyer P (2017) Diversity of mantids (Dictyoptera: Mantodea) of Sangha-Mbaere Region, Central African Republic, with some ecological data and DNA barcoding. Journal of Orthoptera Research 26: 117-141. https://doi.org/10.3897/jor.26.19863
Figure 2 - Illustration of some sampling methods used during surveys in CAR. A. Beating sheet; B. Sweep net; C. Tree climbing; D. Remote Canopy Trap.
Figure 9 from: Moulin N, Decaëns T, Annoyer P (2017) Diversity of mantids (Dictyoptera: Mantodea) of Sangha-Mbaere Region, Central African Republic, with some ecological data and DNA barcoding. Journal of Orthoptera Research 26: 117-141. https://doi.org/10.3897/jor.26.19863
Figure 9 - Rarefaction curve of Mantodea from Sangha-Mbaere Region (black curve) with an extrapolation in a hypothetical situation where sampling effort would be doubled (dashed curve); grey area = confidence interval.
Figure 12 from: Moulin N, Decaëns T, Annoyer P (2017) Diversity of mantids (Dictyoptera: Mantodea) of Sangha-Mbaere Region, Central African Republic, with some ecological data and DNA barcoding. Journal of Orthoptera Research 26: 117-141. https://doi.org/10.3897/jor.26.19863
Figure 12 - Specific richness in the different vegetation strata; grey = observed richness; dashed chart = estimated richness (ACE index) and associated standard deviation.
Figure 3 from: Moulin N, Decaëns T, Annoyer P (2017) Diversity of mantids (Dictyoptera: Mantodea) of Sangha-Mbaere Region, Central African Republic, with some ecological data and DNA barcoding. Journal of Orthoptera Research 26: 117-141. https://doi.org/10.3897/jor.26.19863
Figure 3 - Live habitus. A. Sphodromantis lineola pinguis (green female) from LNP; B. Sphodromantis lineola pinguis (brown female) from LNP; C. Sphodromantis lineola pinguis (green male) from LNP; D. Alalomantis muta (female) from CMNP; E. Polyspilota aeruginosa (male) from LNP; F. Macrodanuria elongata (male) from DNNP; G. Miomantis preussi (green female) from DNNP; H. Miomantis preussi (brown female) from DNNP; I. Miomantis preussi (male) from DNNP.
Figure 7 from: Moulin N, Decaëns T, Annoyer P (2017) Diversity of mantids (Dictyoptera: Mantodea) of Sangha-Mbaere Region, Central African Republic, with some ecological data and DNA barcoding. Journal of Orthoptera Research 26: 117-141. https://doi.org/10.3897/jor.26.19863
Figure 7 - Number of mantid species collected with the two main sampling methods: day capture (Day) and light trapping (UV).
Figure 11 from: Moulin N, Decaëns T, Annoyer P (2017) Diversity of mantids (Dictyoptera: Mantodea) of Sangha-Mbaere Region, Central African Republic, with some ecological data and DNA barcoding. Journal of Orthoptera Research 26: 117-141. https://doi.org/10.3897/jor.26.19863
Figure 11 - Number of collected specimens in the different vegetation strata; black = females; grey = males; white = nymphs.
Figure 10 from: Moulin N, Decaëns T, Annoyer P (2017) Diversity of mantids (Dictyoptera: Mantodea) of Sangha-Mbaere Region, Central African Republic, with some ecological data and DNA barcoding. Journal of Orthoptera Research 26: 117-141. https://doi.org/10.3897/jor.26.19863
Figure 10 - Species richness in the three zones studied: Ndoki = Dzanga-Ndoki National Park zone; Sangha = Dzanga-Sangha Special Reserve zone; Mbaere = Rest of the Sangha-Mbaere Region; grey chart = observed number of species; dashed chart = estimated richness (ACE index) and associated standard deviation.
Figure 1 from: Moulin N, Decaëns T, Annoyer P (2017) Diversity of mantids (Dictyoptera: Mantodea) of Sangha-Mbaere Region, Central African Republic, with some ecological data and DNA barcoding. Journal of Orthoptera Research 26: 117-141. https://doi.org/10.3897/jor.26.19863
Figure 1 - Sangha-Mbaere Region in Central African Republic. A. Location of Tri National Sangha area in Central Africa; B. Location of principal sampling stations and largest towns. Black dots: Main city in Sangha-Mbaere Region. White dots: Main collection areas. Sources: http://www.diva-gis.org adminstrative area, Sangha-Mbaere Region; C. Illustration of the habitats studied; D. Primary forest of CAR, Dzanga Bai with forest elephants.
Figure 5 from: Moulin N, Decaëns T, Annoyer P (2017) Diversity of mantids (Dictyoptera: Mantodea) of Sangha-Mbaere Region, Central African Republic, with some ecological data and DNA barcoding. Journal of Orthoptera Research 26: 117-141. https://doi.org/10.3897/jor.26.19863
Figure 5 - Number of mantid specimens collected with the two main sampling methods: day capture (Day) and trapping with UV lights at night (UV); black chart = females; grey chart = males; white chart = nymphs.
Figure 13 from: Moulin N, Decaëns T, Annoyer P (2017) Diversity of mantids (Dictyoptera: Mantodea) of Sangha-Mbaere Region, Central African Republic, with some ecological data and DNA barcoding. Journal of Orthoptera Research 26: 117-141. https://doi.org/10.3897/jor.26.19863
Figure 13 - Distribution of the species in the different vegetation strata.
Annoying noise: effect of anthropogenic underwater noise on the movement and feeding performance in the red cherry shrimp, Neocaridina davidi
<p><strong>Supplementary material and statistical analysis</strong></p>
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