Binaural Sound Localizer for Azimuthal Movement Detection Based on Diffraction

2015

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A physical structure such as a cylindrical pipe controls the propagated sound spectrum in a predictable way that can be used to localize the sound source. This paper designs a monaural sound localization system based on multiple pyramidal horns around a single microphone. The acoustic resonance within the horn provides a periodicity in the spectral domain known as the fundamental frequency which is inversely proportional to the radial horn length. Once the system accurately estimates the fundamental frequency, the horn length and corresponding angle can be derived by the relationship. The modified Cepstrum algorithm is employed to evaluate the fundamental frequency. In an anechoic chamber, localization experiments over azimuthal configuration show that up to 61% of the proper signal is recognized correctly with 30% misfire. With a speculated detection threshold, the system estimates direction 52% in positive-to-positive and 34% in negative-to-positive decision rate, on average.

Section: Introductionmentioning

confidence: 95%

Section: Structure Designmentioning

confidence: 99%

Monaural Sound Localization Based on Structure-Induced Acoustic Resonance

2015

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“…The reflective monaural localization system [27] placed the multiple plates for the direction-wise time delay to be estimated by homomorphic deconvolution. Other localization works on the subject by the authors are also related to and expanded during the research, such as azimuthal movement detection based on binaural architecture [28] and a target localization algorithm over a distributed acoustic sensor network [29]. Observe that the experiments are performed and evaluated within an identical anechoic chamber [30] to that used in the previous works.…”

Section: Introductionmentioning

confidence: 99%

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

Park

Choi

2020

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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...

“…The RML system of this paper improves the overall size from the pyramidal horn structure method, as well as the working range from the cylindrical pipe structure method. Other localization works on the subject by the authors are also related to and expanded during the research, such as azimuthal movement detection based on binaural architecture [ 42 ] and a target localization algorithm over a distributed acoustic sensor network [ 43 ]. Observe that the RML experiments are performed and evaluated within an identical anechoic chamber [ 44 ] to that used in the previous works.…”

Section: Introductionmentioning

confidence: 99%

Monaural Sound Localization Based on Reflective Structure and Homomorphic Deconvolution

Park

Choi

2017

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The asymmetric structure around the receiver provides a particular time delay for the specific incoming propagation. This paper designs a monaural sound localization system based on the reflective structure around the microphone. The reflective plates are placed to present the direction-wise time delay, which is naturally processed by convolutional operation with a sound source. The received signal is separated for estimating the dominant time delay by using homomorphic deconvolution, which utilizes the real cepstrum and inverse cepstrum sequentially to derive the propagation response’s autocorrelation. Once the localization system accurately estimates the information, the time delay model computes the corresponding reflection for localization. Because of the structure limitation, two stages of the localization process perform the estimation procedure as range and angle. The software toolchain from propagation physics and algorithm simulation realizes the optimal 3D-printed structure. The acoustic experiments in the anechoic chamber denote that 79.0% of the study range data from the isotropic signal is properly detected by the response value, and 87.5% of the specific direction data from the study range signal is properly estimated by the response time. The product of both rates shows the overall hit rate to be 69.1%.