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14 results for “Sound Environment”
Descriptive data file for information regarding microbial genetic research in the environs of Plum Island Sound watersheds, PIE LTER, Massachusetts.
This is a descriptive, tabular dataset of publications related to microbial or genomic research conducted within PIE. Assession numbers for genetic sequences generated from PIE samples are provided where available, followed by a very brief description of analysis type and study objectives. Sampling locations within PIE, sampling dates, and habitat type (sea water, fresh water, sediment, marsh) are also given. Environmental data are included in some publications and are listed here (if brief) or availability is described. Links to sequence archives are given in Methods.
"It was recorded on Sunday, morning of the 28th of September as some of the slower runners of the Berlin Marathon made it past Torstrasse near my flat. Iwas out to buy some bread for breakfast, but Iusually bring a camera and my Edirol R-1 recorder whenever Igo out. Since Iwas freshly returned to Berlin Iguess Iwas sensitive to the more antiquated sounds which still survive there, like that of the organ grinder. Iam generally interested in how human beings are replacing the presence of Nature with an artificial environment made entirely by human hands (and thus far more understandable, it is hoped). In this new Human Nature, the sounds of Nature are also Human made. Iwrite about these things, but Ialso use the sounds in my videos and my interactive and generative media work, so generally Iam wandering around building up my archive of media documents for use as material in future works." [Baruch/ gottlieb]17 in Collecting Sounds. Online Sharing of Field Recordings as Cultural Practice
"It was recorded on Sunday, morning of the 28th of September as some of the slower runners of the Berlin Marathon made it past Torstrasse near my flat. Iwas out to buy some bread for breakfast, but Iusually bring a camera and my Edirol R-1 recorder whenever Igo out. Since Iwas freshly returned to Berlin Iguess Iwas sensitive to the more antiquated sounds which still survive there, like that of the organ grinder. Iam generally interested in how human beings are replacing the presence of Nature with an artificial environment made entirely by human hands (and thus far more understandable, it is hoped). In this new Human Nature, the sounds of Nature are also Human made. Iwrite about these things, but Ialso use the sounds in my videos and my interactive and generative media work, so generally Iam wandering around building up my archive of media documents for use as material in future works." [Baruch/ gottlieb]17
Semi-Supervised Active Learning for Sound Classification in Hybrid Learning Environments
<p>There are 16,930 sound instances in our database with durations ranging 242 from 1 to 10 seconds, which correspond to (approximately) 15 hours of environmental 243 sounds. All sound files were converted into raw 16 bit encoding, mono-channel, and 16 244 kHz sampling rate, as various formats and rates were used in the original versions 245 retrieved from the web.</p>
Semi-Supervised Active Learning for Sound Classification in Hybrid Learning Environments
<p>There are 16,930 sound instances in our database with durations ranging 242 from 1 to 10 seconds, which correspond to (approximately) 15 hours of environmental 243 sounds. All sound files were converted into raw 16 bit encoding, mono-channel, and 16 244 kHz sampling rate, as various formats and rates were used in the original versions 245 retrieved from the web.</p>
Multi-Environment Audio Dataset using RPi based Sound Logger
<p>The proposed dataset is a compilation of 10-12 audios from diverse environments. These audio sets are recorded using a portable sound logger. The audios(.wav audio file format) range from 30-60 mins with sampling frequency of 44.1KHz. Comprehensive literature illustrating the details of the system under test as well as the acoustic conditions around it can be found in the attached paper. The primary aim of releasing this database is to foster research in the field of medicine, especially otology along with other areas such as speech recognition, event detection, anomaly detection, etc.</p>
Self-organising cicada choruses respond to the local sound and light environment
<p>1. Periodical cicadas exhibit an extraordinary capacity for self-organising spatially synchronous breeding behavior. The regular emergence of periodical cicada broods across the US is a phenomenon of longstanding public and scientific interest, as the cicadas of each brood emerge in huge numbers and briefly dominate their ecosystem. During the emergence, the 17-year periodical cicada species Magicicada cassini is found to form synchronised choruses, and we investigated their chorusing behavior from the standpoint of spatial synchrony.</p> <p>2. Cicada choruses were observed to form in trees, calling regularly every five seconds. In order to determine the limits of this self-organising behaviour, we set out to quantify the spatial synchronisation between cicada call choruses in different trees, and how and why this varies in space and time.</p> <p>3. We performed 20 simultaneous recordings in Clinton State Park, Kansas, in June 2015 (Brood IV) with a team of citizen-science volunteers using consumer equipment (smartphones). We use a wavelet approach to show in detail how spatially synchronous, self-organised chorusing varies across the forest.</p> <p>4. We show how conditions that increase the strength of audio interactions between cicadas also increase the spatial synchrony of their chorusing. Higher forest canopy light levels increase cicada activity, corresponding to faster and higher-amplitude chorus cycling and to greater synchrony of cycles across space. We implemented a relaxation-oscillator-ensemble model of interacting cicadas, finding that a tendency to call more often, driven by light levels, results in all these effects.</p> <p>5. Results demonstrate how the capacity to self-organise in ecology depends sensitively on environmental conditions. Spatially correlated modulation of cycling rate by an external driver can also promote self-organisation of phase synchrony.</p>
Data from: Multiple signaling in a variable environment: expression of song and color traits as a function of ambient sound and light
Many animals communicate using more than one signal, and several hypotheses exist to explain the evolution of multiple signals. However, these hypotheses typically assume static selection pressures and previous work has not addressed how spatial and temporal environmental variation can shape variation in signaling systems. In particular, environmental variability, such as ambient lighting or noise, may affect efficacy (e.g. detectability/perception by receivers) of signals. To examine how signal expression varies intraspecifically as a function of habitat characteristics, we evaluated relationships between spatial environmental variation and song and plumage color expression in a tropical songbird, the red-throated ant-tanager (Habia fuscicauda) in Panama. We recorded male ant-tanager song, plucked feathers to measure coloration, and recorded the acoustic and light environments from each male's territory. In addition, we took several morphometric measurements from each male to assess the potential information content of song and plumage color. We found that males with redder and more saturated crowns occurred on darker territories, and males that sang shorter and lower frequency songs occurred on noisier territories. We also found that more colorful males tended to sing longer and lower frequency songs. Finally we found that song and color correlated similarly with male morphology (e.g. tarsus length, body mass). Altogether these results indicate that spatial variation in the environment is related to male coloration and song, and that males might be optimizing color and song expression for their particular territorial environment.
Data from: Uncovering spatial variation in acoustic environments using sound mapping
Animals select and use habitats based on environmental features relevant to their ecology and behavior. For animals that use acoustic communication, the sound environment itself may be a critical feature, yet acoustic characteristics are not commonly measured when describing habitats and as a result, how habitats vary acoustically over space and time is poorly known. Such considerations are timely, given worldwide increases in anthropogenic noise combined with rapidly accumulating evidence that noise hampers the ability of animals to detect and interpret natural sounds. Here, we used microphone arrays to record the sound environment in three terrestrial habitats (forest, prairie, and urban) under ambient conditions and during experimental noise introductions. We mapped sound pressure levels (SPLs) over spatial scales relevant to diverse taxa to explore spatial variation in acoustic habitats and to evaluate the number of microphones needed within arrays to capture this variation under both ambient and noisy conditions. Even at small spatial scales and over relatively short time spans, SPLs varied considerably, especially in forest and urban habitats, suggesting that quantifying and mapping acoustic features could improve habitat descriptions. Subset maps based on input from 4, 8, 12 and 16 microphones differed slightly (< 2 dBA/pixel) from those based on full arrays of 24 microphones under ambient conditions across habitats. Map differences were more pronounced with noise introductions, particularly in forests; maps made from only 4-microphones differed more (> 4 dBA/pixel) from full maps than the remaining subset maps, but maps with input from eight microphones resulted in smaller differences. Thus, acoustic environments varied over small spatial scales and variation could be mapped with input from 4–8 microphones. Mapping sound in different environments will improve understanding of acoustic environments and allow us to explore the influence of spatial variation in sound on animal ecology and behavior.
Effects of Natural Sounds on Attention Restoration in Noisy Environment
ClinicalTrials.gov study NCT05009784. IPD Sharing: NO. Countries: 1. Publications: 1.
Data from: Multiple signaling in a variable environment: expression of song and color traits as a function of ambient sound and light
Open the record for dataset details and reuse information.
Self-organising cicada choruses respond to the local sound and light environment
Open the record for dataset details and reuse information.
Data from: Uncovering spatial variation in acoustic environments using sound mapping
Open the record for dataset details and reuse information.
WildDESED: An LLM-Powered Dataset for Wild Domestic Environment Sound Event Detection System
<p>A new large language model (LLM)-powered dataset namely wild domestic environment sound event detection (WildDESED). It is crafted as an extension to the original DESED dataset to reflect diverse acoustic variability and complex noises in home settings. We leveraged LLMs to generate eight different domestic scenarios based on target sound categories of the DESED dataset. Then we enriched the scenarios with a carefully tailored mixture of noises selected from AudioSet and ensured no overlap with target sound. We consider widely popular convolutional neural recurrent network to study WildDESED dataset, which depicts its challenging nature. We then apply curriculum learning by gradually increasing noise complexity to enhance the model's generalization capabilities across various noise levels.</p>
Assessment of a Method to Improve Cochlear Implant Users' Speech Perception in Noisy Environments While Maintaining Their Ability to Determine Where a Sound Originates From
ClinicalTrials.gov study NCT07323862. IPD Sharing: YES. Countries: 1. Publications: 0.
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International Brain Laboratory public data
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OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.