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Relevant bibliographies by topics / Data over sound

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Journal articles

Academic literature on the topic 'Data over sound'

Author: Grafiati

Published: 6 September 2023

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Journal articles on the topic "Data over sound"

1

Fejfar, Jiří, Jiří Šťastný, Martin Pokorný, Jiří Balej, and Petr Zach. "Analysis of sound data streamed over the network." Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 61, no. 7 (2013): 2105–10. http://dx.doi.org/10.11118/actaun201361072105.

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In this paper we inspect a difference between original sound recording and signal captured after streaming this original recording over a network loaded with a heavy traffic. There are several kinds of failures occurring in the captured recording caused by network congestion. We try to find a method how to evaluate correctness of streamed audio. Usually there are metrics based on a human perception of a signal such as “signal is clear, without audible failures”, “signal is having some failures but it is understandable”, or “signal is inarticulate”. These approaches need to be statistically eva

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Kim, Hyun-Don, Kazunori Komatani, Tetsuya Ogata, and Hiroshi G. Okuno. "Binaural Active Audition for Humanoid Robots to Localise Speech over Entire Azimuth Range." Applied Bionics and Biomechanics 6, no. 3-4 (2009): 355–67. http://dx.doi.org/10.1155/2009/817874.

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We applied motion theory to robot audition to improve the inadequate performance. Motions are critical for overcoming the ambiguity and sparseness of information obtained by two microphones. To realise this, we first designed a sound source localisation system integrated with cross-power spectrum phase (CSP) analysis and an EM algorithm. The CSP of sound signals obtained with only two microphones was used to localise the sound source without having to measure impulse response data. The expectation-maximisation (EM) algorithm helped the system to cope with several moving sound sources and reduc

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3

Saldanha, Jane, Shaunak Chakraborty, Shruti Patil, Ketan Kotecha, Satish Kumar, and Anand Nayyar. "Data augmentation using Variational Autoencoders for improvement of respiratory disease classification." PLOS ONE 17, no. 8 (2022): e0266467. http://dx.doi.org/10.1371/journal.pone.0266467.

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Computerized auscultation of lung sounds is gaining importance today with the availability of lung sounds and its potential in overcoming the limitations of traditional diagnosis methods for respiratory diseases. The publicly available ICBHI respiratory sounds database is severely imbalanced, making it difficult for a deep learning model to generalize and provide reliable results. This work aims to synthesize respiratory sounds of various categories using variants of Variational Autoencoders like Multilayer Perceptron VAE (MLP-VAE), Convolutional VAE (CVAE) Conditional VAE and compare the infl

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Aiello, Luca Maria, Rossano Schifanella, Daniele Quercia, and Francesco Aletta. "Chatty maps: constructing sound maps of urban areas from social media data." Royal Society Open Science 3, no. 3 (2016): 150690. http://dx.doi.org/10.1098/rsos.150690.

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Urban sound has a huge influence over how we perceive places. Yet, city planning is concerned mainly with noise, simply because annoying sounds come to the attention of city officials in the form of complaints, whereas general urban sounds do not come to the attention as they cannot be easily captured at city scale. To capture both unpleasant and pleasant sounds, we applied a new methodology that relies on tagging information of georeferenced pictures to the cities of London and Barcelona. To begin with, we compiled the first urban sound dictionary and compared it with the one produced by coll

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5

Cushing, Colby W., Jason D. Sagers, and Megan Ballard. "Ambient sound observations from beamformed horizontal array data in the Pacific Arctic." Journal of the Acoustical Society of America 152, no. 4 (2022): A71. http://dx.doi.org/10.1121/10.0015582.

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Changes in the Arctic environment with regard to declining sea ice and changing oceanography are expected to alter the ambient sound field, affecting both the sound generating processes and the acoustic propagation. This talk presents acoustic recordings collected on the 150-m isobath on the Chukchi Shelf during the Canada Basin Acoustic Propagation Experiment (CANAPE), which took place over a yearlong period spanning October 2016 to October 2017. The data were recorded on a 52-channel center-tapered horizontal line array and adaptively beamformed to quantify the azimuthal directionality in lo

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Bessen, Sarah Y., James E. Saunders, Eric A. Eisen, and Isabelle L. Magro. "Perceptions of Sound Quality and Enjoyment After Cochlear Implantation." OTO Open 5, no. 3 (2021): 2473974X2110314. http://dx.doi.org/10.1177/2473974x211031471.

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Objectives To characterize the quality and enjoyment of sound by cochlear implant (CI) recipients and identify predictors of these outcomes after cochlear implantation. Study Design Cross-sectional study. Setting A tertiary care hospital. Methods Surveys based on the Hearing Implant Sound Quality Index were sent to all patients who received a CI at a tertiary care hospital from 2000 to 2019. Survey questions prompted CI recipients to characterize enjoyment and quality of voices, music, and various sounds. Results Of the 339 surveys, 60 (17.7%) were returned with complete data. CI recipients ha

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7

Bandara, Meelan, Roshinie Jayasundara, Isuru Ariyarathne, Dulani Meedeniya, and Charith Perera. "Forest Sound Classification Dataset: FSC22." Sensors 23, no. 4 (2023): 2032. http://dx.doi.org/10.3390/s23042032.

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The study of environmental sound classification (ESC) has become popular over the years due to the intricate nature of environmental sounds and the evolution of deep learning (DL) techniques. Forest ESC is one use case of ESC, which has been widely experimented with recently to identify illegal activities inside a forest. However, at present, there is a limitation of public datasets specific to all the possible sounds in a forest environment. Most of the existing experiments have been done using generic environment sound datasets such as ESC-50, U8K, and FSD50K. Importantly, in DL-based sound

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Schwock, Felix, and Shima Abadi. "Summary of underwater ambient sound from wind and rain in the northeast Pacific continental margin." Journal of the Acoustical Society of America 153, no. 3_supplement (2023): A97. http://dx.doi.org/10.1121/10.0018294.

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Analyzing underwater ambient sound from various sources such as ships, marine mammals, rain, and wind is crucial for characterizing the ocean environment. While efforts to analyze ocean ambient sounds have been ongoing since the 1940s, networks such as the Ocean Observatories Initiative (OOI) provide modern large-scale recording setups for a more in-depth analysis. Here we will summarize results from analyzing over 11,000h of wind generated ambient sound and 280 h of ambient sound during rain collected between 2015 and 2019 by two OOI hydrophones deployed in the northeast Pacific continental m

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Wałęga, Przemysław Andrzej, Mark Kaminski, and Bernardo Cuenca Grau. "Reasoning over Streaming Data in Metric Temporal Datalog." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 3092–99. http://dx.doi.org/10.1609/aaai.v33i01.33013092.

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We study stream reasoning in datalogMTL—an extension of Datalog with metric temporal operators. We propose a sound and complete stream reasoning algorithm that is applicable to a fragment datalogMTLFP of datalogMTL, in which propagation of derived information towards past time points is precluded. Memory consumption in our algorithm depends both on the properties of the rule set and the input data stream; in particular, it depends on the distances between timestamps occurring in data. This is undesirable since these distances can be very small, in which case the algorithm may require large amo

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Phillips, James E. "Verification of an acoustic model of outdoor sound propagation from a natural resource compressor station over complex topography." Journal of the Acoustical Society of America 153, no. 3_supplement (2023): A324. http://dx.doi.org/10.1121/10.0019009.

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Outdoor sound propagation from a natural resource compressor station with multiple, large, reciprocating compressors enclosed within a structure was modeled using DGMR iNoise. Data from sound level measurements taken near the station were used to estimate the sound power of the operating compressor station equipment and used as input to the model. The model was then used to project the sound pressure levels at multiple measurement locations over complex topography. Good agreement was achieved between the projected and measured sound pressure levels as far as ½-mile from the station, particular

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