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Academic literature on the topic 'Simultaneous Sound Sources'

Author: Grafiati
Published: 4 June 2021
Last updated: 20 February 2023

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Journal articles on the topic "Simultaneous Sound Sources"

1

Xiang, Ning, and Christopher Landschoot. "Bayesian Inference for Acoustic Direction of Arrival Analysis Using Spherical Harmonics." Entropy 21, no. 6 (2019): 579. http://dx.doi.org/10.3390/e21060579.

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This work applies two levels of inference within a Bayesian framework to accomplish estimation of the directions of arrivals (DoAs) of sound sources. The sensing modality is a spherical microphone array based on spherical harmonics beamforming. When estimating the DoA, the acoustic signals may potentially contain one or multiple simultaneous sources. Using two levels of Bayesian inference, this work begins by estimating the correct number of sources via the higher level of inference, Bayesian model selection. It is followed by estimating the directional information of each source via the lower level of inference, Bayesian parameter estimation. This work formulates signal models using spherical harmonic beamforming that encodes the prior information on the sensor arrays in the form of analytical models with an unknown number of sound sources, and their locations. Available information on differences between the model and the sound signals as well as prior information on directions of arrivals are incorporated based on the principle of the maximum entropy. Two and three simultaneous sound sources have been experimentally tested without prior information on the number of sources. Bayesian inference provides unambiguous estimation on correct numbers of sources followed by the DoA estimations for each individual sound sources. This paper presents the Bayesian formulation, and analysis results to demonstrate the potential usefulness of the model-based Bayesian inference for complex acoustic environments with potentially multiple simultaneous sources.
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2

Frank, Matthias. "Source Width of Frontal Phantom Sources: Perception, Measurement, and Modeling." Archives of Acoustics 38, no. 3 (2013): 311–19. http://dx.doi.org/10.2478/aoa-2013-0038.

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Abstract Phantom sources are known to be perceived similar to real sound sources but with some differences. One of the differences is an increase of the perceived source width. This article discusses the perception, measurement, and modeling of source width for frontal phantom sources with different symmetrical arrangements of up to three active loudspeakers. The perceived source width is evaluated on the basis of a listening test. The test results are compared to technical measures that are applied in room acoustics: the inter-aural cross correlation coefficient (IACC) and the lateral energy fraction (LF). Adaptation of the latter measure makes it possible to predict the results by considering simultaneous sound incidence. Finally, a simple model is presented for the prediction of the perceived source width that does not require acoustic measurements as it is solely based on the loudspeaker directions and gains.
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3

Vannier, Michaël, and Etienne Parizet. "Loudness of a multi-tonal sound field, consisting of either one two-component complex sound source or two simultaneous spatially distributed sound sources." Journal of the Acoustical Society of America 136, no. 4 (2014): 2309. http://dx.doi.org/10.1121/1.4900356.

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4

Chen, Xiaohui, Hao Sun, and Heng Zhang. "A New Method of Simultaneous Localization and Mapping for Mobile Robots Using Acoustic Landmarks." Applied Sciences 9, no. 7 (2019): 1352. http://dx.doi.org/10.3390/app9071352.

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The simultaneous localization and mapping (SLAM) problem for mobile robots has always been a hotspot in the field of robotics. Simultaneous localization and mapping for robots using visual sensors and laser radar is easily affected by the field of view and ground conditions. According to the problems of traditional sensors applied in SLAM, this paper presents a novel method to perform SLAM using acoustic signals. This method enables robots equipped with sound sources, moving within a working environment and interacting with microphones of interest, to locate itself and map the objects simultaneously. In our case, a method of microphone localization based on a sound source array is proposed, and it was applied as a pre-processing step to the SLAM procedure. A microphone capable of receiving sound signals can be directly used as a feature landmark of a robot observation model without feature extraction. Meanwhile, to eliminate the random error caused by hardware equipment, a sound settled in the middle of two microphones was applied as a calibration sound source to determine the value of the random error. Simulations and realistic experimental results demonstrate the feasibility and effectiveness of the proposed method.
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5

Suzuki, Takuya, Hiroaki Otsuka, Wataru Akahori, Yoshiaki Bando, and Hiroshi G. Okuno. "Influence of Different Impulse Response Measurement Signals on MUSIC-Based Sound Source Localization." Journal of Robotics and Mechatronics 29, no. 1 (2017): 72–82. http://dx.doi.org/10.20965/jrm.2017.p0072.

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[abstFig src='/00290001/07.jpg' width='300' text='Six impulse response measurement signals' ] Two major functions, sound source localization and sound source separation, provided by robot audition open source software HARK exploit the acoustic transfer functions of a microphone array to improve the performance. The acoustic transfer functions are calculated from the measured acoustic impulse response. In the measurement, special signals such as Time Stretched Pulse (TSP) are used to improve the signal-to-noise ratio of the measurement signals. Recent studies have identified the importance of selecting a measurement signal according to the applications. In this paper, we investigate how six measurement signals – up-TSP, down-TSP, M-Series, Log-SS, NW-SS, and MN-SS – influence the performance of the MUSIC-based sound source localization provided by HARK. Experiments with simulated sounds, up to three simultaneous sound sources, demonstrate no significant difference among the six measurement signals in the MUSIC-based sound source localization.
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6

MARKOU, Dimitris. "Exploring spatial patterns of environmental noise and perceived sound source dominance in urban areas. Case study: the city of Athens, Greece." European Journal of Geography 13, no. 4 (2022): 60–78. http://dx.doi.org/10.48088/ejg.d.mar.13.2.060.078.

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The aim of the present study is to map spatial patterns related to noise pollution and the acoustic environment -in a broader context- in the urban area of Athens, Greece. The primary goal of this thesis is to present a comprehensive approach that combines elements of two basic methodologies related to acoustic environment studies: a) noise mapping and b) the soundscape approach. The main inputs are environmental noise measurements and perceptual sound source-related observations. The results feature three noise pollution maps (LAeq,30 sec, L10, and L90 indices) and three sound source maps which reflect the way in which the human ear perceives the presence of sounds. Additionally, the question of whether the spatial distribution of sound source dominance can be explained by the dispersion of environmental noise levels was examined using geographically weighted regressions (GWR). The GWR models showed that sound source-related observations are explained to a significant extent by all three indicators. Four important findings emerge from the analysis. Firstly, areas with high levels of noise pollution are characterized by high to moderate presence of technological and absence of anthropic and natural sounds. Secondly, regions, where there is a simultaneous presence of all sound sources, are characterized by moderate to low noise levels. Thirdly, the absence of technological sounds is observed in quiet areas. Finally, areas featuring a moderate presence of technological and natural sounds are mostly urban green spaces built-in proximity to the main road network.
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7

Yaitskov, Ivan. "On the issue of formation the air noise component at workplaces of the diesel locomotives crews." MATEC Web of Conferences 224 (2018): 02024. http://dx.doi.org/10.1051/matecconf/201822402024.

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The article is devoted to the general acoustic system of the diesel locomotives which is a combination of the diverse noise and vibration sources creating the increased levels of the sound pressure in the workplaces of the machinists and locomotive crews. Factually, the sound field at the calculated points is created by the simultaneous action of the air and structural noise component sources. It is included the emitters to the sources of the air noise component which inboard the body structures and, accordingly, emit sound energy into the closed air volumes. And it is the sources which are set externally in particular the “wheel-rail” subsystems. It can be assumed that among the internal sources the maximum sound power is radiated by the power system namely by the internal combustion engines. For different types of the diesel locomotives, the setting of the internal combustion engines has significant differences to the work places of the locomotive crews. Therefore, this article considers four computational schemes for the diesel locomotive, obtains the analytical dependences of the sound pressure levels and reduces to the convenient form for engineering calculations at the design stage of the research objects. To reduce the noise source of the internal combustion engine while it is appearing in the conditions of the machine-building processes is almost impossible. Moreover, the main practical and technological feasible ways are to choose sound-absorbing materials and achieve sound insulation based on the existing sanitary noise standards.
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8

Folland, Nicole A., Blake E. Butler, Jennifer E. Payne, and Laurel J. Trainor. "Cortical Representations Sensitive to the Number of Perceived Auditory Objects Emerge between 2 and 4 Months of Age: Electrophysiological Evidence." Journal of Cognitive Neuroscience 27, no. 5 (2015): 1060–67. http://dx.doi.org/10.1162/jocn_a_00764.

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Sound waves emitted by two or more simultaneous sources reach the ear as one complex waveform. Auditory scene analysis involves parsing a complex waveform into separate perceptual representations of the sound sources [Bregman, A. S. Auditory scene analysis: The perceptual organization of sounds. London: MIT Press, 1990]. Harmonicity provides an important cue for auditory scene analysis. Normally, harmonics at integer multiples of a fundamental frequency are perceived as one sound with a pitch corresponding to the fundamental frequency. However, when one harmonic in such a complex, pitch-evoking sound is sufficiently mistuned, that harmonic emerges from the complex tone and is perceived as a separate auditory object. Previous work has shown that the percept of two objects is indexed in both children and adults by the object-related negativity component of the ERP derived from EEG recordings [Alain, C., Arnott, S. T., & Picton, T. W. Bottom–up and top–down influences on auditory scene analysis: Evidence from event-related brain potentials. Journal of Experimental Psychology: Human Perception and Performance, 27, 1072–1089, 2001]. Here we examine the emergence of object-related responses to an 8% harmonic mistuning in infants between 2 and 12 months of age. Two-month-old infants showed no significant object-related response. However, in 4- to 12-month-old infants, a significant frontally positive component was present, and by 8–12 months, a significant frontocentral object-related negativity was present, similar to that seen in older children and adults. This is in accordance with previous research demonstrating that infants younger than 4 months of age do not integrate harmonic information to perceive pitch when the fundamental is missing [He, C., Hotson, L., & Trainor, L. J. Maturation of cortical mismatch mismatch responses to occasional pitch change in early infancy: Effects of presentation rate and magnitude of change. Neuropsychologia, 47, 218–229, 2009]. The results indicate that the ability to use harmonic information to segregate simultaneous sounds emerges at the cortical level between 2 and 4 months of age.
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9

Hu, Jwu-Sheng, Chen-Yu Chan, Cheng-Kang Wang, Ming-Tang Lee, and Ching-Yi Kuo. "Simultaneous Localization of a Mobile Robot and Multiple Sound Sources Using a Microphone Array." Advanced Robotics 25, no. 1-2 (2011): 135–52. http://dx.doi.org/10.1163/016918610x538525.

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10

Valin, Jean-Marc, François Michaud, and Jean Rouat. "Robust localization and tracking of simultaneous moving sound sources using beamforming and particle filtering." Robotics and Autonomous Systems 55, no. 3 (2007): 216–28. http://dx.doi.org/10.1016/j.robot.2006.08.004.

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