Monaural Sound Localization Based on Structure-Induced Acoustic Resonance

Kim, Keonwook; Kim, Youngwoong

doi:10.3390/s150203872

Cited by 9 publications

(13 citation statements)

References 29 publications

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“…The fundamental frequency estimation is performed by the Cepstral parameter previously used by the authors [ 30 ]. The cycle frequency between the peak responses in the frequency domain is computed by a double discrete Fourier transform (DFT) as Cepstrum.…”

Section: Methodsmentioning

confidence: 99%

“…The r values of the second DFT from Equation (3) indicate the scaled time, and an inverse operation is required to obtain the fundamental frequency in Equation (5). Further detail on the derivation can be found in Appendix A of a previous paper [ 30 ].…”

Section: Methodsmentioning

confidence: 99%

“…The NFML structure is realized by a 3D printer (Replicator 2, MakerBot, Brooklyn, NY, USA) based on the polylactic acid (PLA) filament, and is shown in Figure 12 d. The acoustic experiments are executed and analyzed in an anechoic chamber that has been verified to exhibit partial conformance with ISO 3745 for free field and hemi-free field conditions [ 33 ]. The microphone (ECM8000, Behringer, Tortola, British Virgin Island), computer-connected audio device (Quad-Capture, Roland, Hamamatsu, Japan), and audio software (Sonar X2 Studio, Cakewalk, Boston, MA, USA) are identical to the experimental configuration of a previous paper [ 30 ] except for the sound generator. To replicate the near-field point source, a high-output in-ear monitor (UE900, Logitech, Lausanne, Switzerland) is mounted on the positioning device to control the distance and height of the sound source, as shown in the right-hand side of Figure 12 d. The direction is observed by the line laser (GLL 3-80, Bosch, Stuttgart, Germany), and the distance is measured by a laser distance meter (DISTO D3a, Leica, Wetzlar, Germany).…”

Section: Nfml Structure Design and Simulationmentioning

confidence: 99%

“…The aim of this paper is to extend the authors’ previous work on the ML for far-field sound sources based on pyramidal horns [ 30 ]. The horizontal arrangement of the horns denoted the relatively decoupled acoustic interference between inlets for enhanced performance; however, the ML profile reduced the maneuverability due to the oversized structure.…”

Section: Introductionmentioning

confidence: 99%

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Near-Field Sound Localization Based on the Small Profile Monaural Structure

Kim

2015

Sensors

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The acoustic wave around a sound source in the near-field area presents unconventional properties in the temporal, spectral, and spatial domains due to the propagation mechanism. This paper investigates a near-field sound localizer in a small profile structure with a single microphone. The asymmetric structure around the microphone provides a distinctive spectral variation that can be recognized by the dedicated algorithm for directional localization. The physical structure consists of ten pipes of different lengths in a vertical fashion and rectangular wings positioned between the pipes in radial directions. The sound from an individual direction travels through the nearest open pipe, which generates the particular fundamental frequency according to the acoustic resonance. The Cepstral parameter is modified to evaluate the fundamental frequency. Once the system estimates the fundamental frequency of the received signal, the length of arrival and angle of arrival (AoA) are derived by the designed model. From an azimuthal distance of 3–15 cm from the outer body of the pipes, the extensive acoustic experiments with a 3D-printed structure show that the direct and side directions deliver average hit rates of 89% and 73%, respectively. The closer positions to the system demonstrate higher accuracy, and the overall hit rate performance is 78% up to 15 cm away from the structure body.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Nfml Structure Design and Simulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Near-Field Sound Localization Based on the Small Profile Monaural Structure

Kim

2015

Sensors

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“…This paper achieves the aims proposed by the authors' previous SSL publications. The fundamental frequencies induced by the asymmetric horizontal pyramidal horns were arranged for the far-field monaural localization system by utilizing cepstral parameters [25]. The small-profile near-field monaural system was realized by the asymmetric vertical cylindrical pipes around a single microphone [26].…”

Section: Introductionmentioning

confidence: 99%

Parametric Estimations Based on Homomorphic Deconvolution for Time of Flight in Sound Source Localization System

Park

Choi

Kim

2020

Sensors

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Vehicle-mounted sound source localization systems provide comprehensive information to improve driving conditions by monitoring the surroundings. The three-dimensional structure of vehicles hinders the omnidirectional sound localization system because of the long and uneven propagation. In the received signal, the flight times between microphones delivers the essential information to locate the sound source. This paper proposes a novel method to design a sound localization system based on the single analog microphone network. This article involves the flight time estimation for two microphones with non-parametric homomorphic deconvolution. The parametric methods are also suggested with Yule-walker, Prony, and Steiglitz-McBride algorithm to derive the coefficient values of the propagation model for flight time estimation. The non-parametric and Steiglitz-McBride method demonstrated significantly low bias and variance for 20 or higher ensemble average length. The Yule-walker and Prony algorithms showed gradually improved statistical performance for increased ensemble average length. Hence, the non-parametric and parametric homomorphic deconvolution well represent the flight time information. The derived non-parametric and parametric output with distinct length will serve as the featured information for a complete localization system based on machine learning or deep learning in future works.Sensors 2020, 20, 925 2 of 32 deliver the situation over the non-line-of-sight (NLOS) locations. Presently, the human and vehicle are cooperated to drive the transport sorely based on the vision information. Hence, indirect imminent endangerment cannot be realized until the human has the visual contact. For example, a car with emergency braking cannot be perceived by the indirect position observers. The squeal sound provides the situation. However, the system cannot recognize the direction to activate the pre-emptive safety devices which reduce or remove the impact of the secondary collisions. The sound information can be used for improving the safety of future autonomous transport system.The driver barely obtains the acoustic information since the vehicle structure debilitates the propagation by airtight cabin. The acoustic perception on sound source and arrival direction are both required to understand the situation. The SSL system mounted on a vehicle could provide the comprehensive information to improve the driving conditions by monitoring the surroundings including the NLOS observation. The conventional SSL approaches recently employed for transport are as follows. The moving vehicle presences are identified by sound localization based on the arrival time difference between the microphones [8]. A sensing technique to localize an approaching vehicle is proposed by an acoustic cue from the spatial-temporal gradient method [9]. For the sequential movement events of vehicles, robust direction-of-arrival estimation is realized by the incoherent signal-subspace method based on a small microphone array [10].The 3D structure of ve...

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