Airy pattern approximation of a phased microphone array response to a rotating point source

Debrouwere, Maarten; Angland, David

doi:10.1121/1.4976068

Cited by 11 publications

(5 citation statements)

References 18 publications

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“…Audio source localization has been widely studied in the past. However, most of the studies are theoretical and simulation-based as they involve the development of signal processing techniques for overcoming sensor noise, mutual coupling, obtaining accurate DOA estimation in the presence of fewer snapshots, operation in harsh and low signal-to-noise-ratio (SNR) conditions etc [22]- [24]. Another portion of research papers is devoted to the hardware design of microphone arrays [25]- [28].An outdoor sound source localization system wherein a microphone array was fitted to a drone was demonstrated in [29].A huge microphone array with 512 microphones was designed and demonstrated in [30].…”

Section: Existing Methodsmentioning

confidence: 99%

Microphone Array and Raspberry Pi Interfacing for Real-time DOA Estimation and Tracking of Audio Sources

Patwari¹

2020

IJETER

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Microphone arrays are commonly used for Direction of Arrival (DOA) estimation of audio sources. This paper presents the design and implementation of a real-time embedded system (RTES) for audio source localization. A commercially available microphone array assembly (consisting of four microphones) has been interfaced to Raspberry Pi 3B for estimating the arrival angles of audio/sound/acoustic sources. The acoustic signals received a teach of the microphones are used for DOA estimation using the Generalized Cross Correlation Phase Transform (GCC-PHAT) algorithm. The estimated DOA is then displayed on the serial monitor. Servo motors are interfaced to the Raspberry Pi and are programmed to point towards the estimated direction of the audio source (on the basis of the estimated DOA value). The estimated DOA value is also updated to the cloud-based server Firebase using the inbuilt Wi-Fi on Raspberry Pi, and can be remotely accessed through a smartphone running on android platform. The proposed system has been designed and tested successfully and it is observed that it provides accurate DOA estimation and audio source tracking in real-time. This has various uses in applications like voice assistant robots, machine audition, hearing aids, source remixing, augmented reality headsets, security cameras, sound tracking (sonar sensor) devices, bird identification, mammal localization and so on.

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Section: Existing Methodsmentioning

confidence: 99%

Microphone Array and Raspberry Pi Interfacing for Real-time DOA Estimation and Tracking of Audio Sources

Patwari¹

2020

IJETER

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“…Pan et al [16] developed a correction method for the TRB to restrain the singular problem in identifying rotating sources with the non-uniform directivity. In recent years, deconvolution algorithms were subsequently used to improve the spatial resolution of the TRB [17][18][19][20][21]. Nevertheless, the TRB utilizes the advanced time method to calculate the receiver time based on the source time, which causes inconsistency between the calculated receiver time and the sampling time.…”

Section: Introductionmentioning

confidence: 99%

An Improved Time-Domain Inverse Technique for Localization and Quantification of Rotating Sound Sources

Zhang,

Li,

Zhang

et al. 2023

Chin. J. Mech. Eng.

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The time-domain inverse technique based on the time-domain rotating equivalent source method has been proposed to localize and quantify rotating sound sources. However, this technique encounters two problems to be addressed: one is the time-consuming process of solving the transcendental equation at each time step, and the other is the difficulty of controlling the instability problem due to the time-varying transfer matrix. In view of that, an improved technique is proposed in this paper to resolve these two problems. In the improved technique, a de-Dopplerization method in the time-domain rotating reference frame is first applied to eliminate the Doppler effect caused by the source rotation in the measured pressure signals, and then the restored pressure signals without the Doppler effect are used as the inputs of the time-domain stationary equivalent source method to locate and quantify sound sources. Compared with the original technique, the improved technique can avoid solving the transcendental equation at each time step, and facilitate the treatment of the instability problem because the transfer matrix does not change with time. Numerical simulation and experimental results show that the improved technique can eliminate the Doppler effect effectively, and then localize and quantify the rotating nonstationary or broadband sources accurately. The results also demonstrate that the improved technique can guarantee a more stable reconstruction and compute more efficiently than the original one.

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“…The method was later combined with deconvolution algorithms as a hybrid time and frequency domain method [2] and applied to rotating sources [3]. An improved way to calculate the point spread function for the deconvolution of rotating sources was derived by Debrouwere et al [4].…”

Section: Introductionmentioning

confidence: 99%

An Extension of the Virtual Rotating Array Method Using Arbitrary Microphone Configurations for the Localization of Rotating Sound Sources

Jekosch¹,

Sarradj²

2020

Acoustics

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The characterization of rotating aeroacoustic sources using microphone array methods has been proven to be a useful tool. One technique to identify rotating sources is the virtual rotating array method. The method interpolates the pressure time data signals between the microphones in a stationary array to compensate the motion of the rotating sources. One major drawback of the method is the requirement of ring array geometries that are centred around the rotating axis. This contribution extends the virtual rotating array method to arbitrary microphone configurations. Two different ways to interpolate the time signals between the microphone locations are proposed. The first method constructs a mesh between the microphone positions using Delaunay-triangulation and interpolates over the mesh faces using piecewise linear functions. The second one is a meshless technique which is based on radial basis function interpolation. The methods are tested on synthetic array data from a benchmark test case as well as on experimental data obtained with a spiral array and a five-bladed fan.

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Airy pattern approximation of a phased microphone array response to a rotating point source

Cited by 11 publications

References 18 publications

Microphone Array and Raspberry Pi Interfacing for Real-time DOA Estimation and Tracking of Audio Sources

Microphone Array and Raspberry Pi Interfacing for Real-time DOA Estimation and Tracking of Audio Sources

An Improved Time-Domain Inverse Technique for Localization and Quantification of Rotating Sound Sources

An Extension of the Virtual Rotating Array Method Using Arbitrary Microphone Configurations for the Localization of Rotating Sound Sources

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