Sound source localization of harmonic sources in entire 3D space using just 5 acoustic signals

Thakur, Shikha; Singh, Shalendra

doi:10.1016/j.apacoust.2022.109126

Cited by 5 publications

(3 citation statements)

References 50 publications

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“…The time it takes for the signal to be picked up by the microphone is known as the TDOA. For TDOA estimation, there are methods like GCC, GCC-PHAT, and DFSE [6]. Given that the PHAT method performs better in both simulated Gaussian distribution noise and real-world noisy situations, we took into consideration GCC-PHAT for our TDOA calculation.…”

Section: Sound Source Localization Using Microphonesmentioning

confidence: 99%

Visual object detection using audio data

Chaturvedi,

Sahu,

Tyagi

et al. 2023

J. Phys.: Conf. Ser.

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Nowadays Internet of Things (IoT) and Machine Learning (ML) are growing fields. One application of these two fields is object detection, which detects semantic objects using digital images and videos of classes like humans, vehicles, buildings, etc. Visual object detection systems are very effective and accurate due to the appearance information obtained from the cameras. But they face the problem of a limited Field of View. This paper aims to tackle this issue by using audio data to localize the object. A microphone is used to estimate the angular position of the object emitting the sound. Objects currently in the Field of View of a camera are detected and tracked using optical flow, but when they go out of the Field of View, the sound emitted by the object is used by the microphone to calculate the object‘s angular position. Once the angle is calculated, the camera is rotated in that direction. This thus ensures that the object can be located even if it goes out of frame. Once the object is located through IoT devices, we use ML to identify the person‘s face.

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Section: Sound Source Localization Using Microphonesmentioning

confidence: 99%

Visual object detection using audio data

Chaturvedi,

Sahu,

Tyagi

et al. 2023

J. Phys.: Conf. Ser.

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“…Sound source identification has a wide range of applications in noise management and equipment-fault diagnosis [1][2][3][4]. A variety of advanced sound source identification techniques have been developed, including conventional beamforming [5,6], deconvolution approach for mapping acoustic sources (DAMAS) [7,8], and near-field acoustic holography [9,10].…”

Section: Introductionmentioning

confidence: 99%

Orthogonal matching pursuit algorithm based on weighted cosine similarity for sound source localization

Xiao,

Wang,

Yuan

2023

Phys. Scr.

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Compressive sensing overcomes the limitations of the Nyquist criteria and is one of the most widely used compressive sensing reconstruction algorithms. Orthogonal matching pursuit (OMP) algorithm is simple, in terms of hardware implementation, and has high computational efficiency. However, the OMP algorithm exhibits poor identification performance for low-frequency sound sources and results in large localization deviations when the mesh spacing of the focus plane is small. In this study, a novel atom selection criterion based on weighted cosine similarity was proposed to improve the OMP algorithm for sound source localization and characterization. This method replaces the original inner product criterion to measure the correlation between the column vectors of the sensing matrix and the residuals, which addresses the atom selection error caused by the high correlation between atoms. Numerical simulations and experimental results show that the proposed method has a stronger anti-noise interference capability and higher accuracy for sound source identification with fewer sampling points, particularly in low-frequency and low signal-to-noise ratio environments. Compared to other OMP algorithms, the proposed method improves the performance of the OMP algorithm in sound source localization and widens the sound frequency range. This study is valuable for achieving highly accurate sound source localization and reducing measurement costs in practical applications.

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“…The type of microphones used, as well as the number of microphones [12] and their geometry, or location, contribute a significant effect on the signal processing performance of SSL systems. Binaural, tri-aural, and tetra-aural configurations, clustered arrays, and non-co-planar array structures are only a few of the array configurations that may be employed, each with a different number of microphones and positions [13,14].…”

Section: Introductionmentioning

confidence: 99%

Design and Performance Analysis of Sound Source Localization using Time Difference of Arrival Estimation

Nur Syaza Syahira Mohammud@Mahmud,

Ili Shairah Abdul Halim,

Siti Lailatul Mohd Hassan

et al. 2023

ARAM

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Sound source localization (SSL) is a process of processing sound signals received from sound sensors and locating the sound origin. In many applications, precise localization of these sound sources is crucial. The accuracy of the localization of the sound source depends on several factors, and one of the factors is the number of sound sensors. This work aims to develop a microcontroller-based sound sensor localization system using the Time Difference of Arrival (TDOA) method, as well as to analyze SSL by comparing the time difference received by two and three sound sensors with respect to the distance of sound, and to compare the angle accuracy of the sound localization through TDOA with the real sound location. A few experimental tests were conducted using a constant sound of 90db and a frequency range of sound of 50Hz to 10kHz. The mean error (inaccuracy) of the experimental test of the angle acquired within 5cm of the source is 34.66°, while the angle obtained within 10cm of the source is 34.29°. The difference in angle inaccuracy between two and three sensors has a reduction of 27.64%. By adding more sound sensors, angle accuracy can be increased through the TDOA method.

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Sound source localization of harmonic sources in entire 3D space using just 5 acoustic signals

Cited by 5 publications

References 50 publications

Visual object detection using audio data

Visual object detection using audio data

Orthogonal matching pursuit algorithm based on weighted cosine similarity for sound source localization

Design and Performance Analysis of Sound Source Localization using Time Difference of Arrival Estimation

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