N-dimensional N-microphone sound source localization

Pourmohammad, Ali; Ahadi, Seyed Mohammad

doi:10.1186/1687-4722-2013-27

Cited by 21 publications

(15 citation statements)

References 59 publications

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“…used to find the time of arrival of signals in many applications [8] [9]. According to [10] when a signal is filtered by matched filter, the noise can be efficiently removed and higher SNR is achieved.…”

Section: Envelope Based Methods For Cycle Identification Using Matchedmentioning

confidence: 99%

High Performance, Low Cost Loran-C Cycle Identification and ECD Estimation

Tehrani¹,

Pourmohammad²

2017

ISSPJ

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Abstract:A Loran-C system is a hyperbolic navigation system which works based on time difference of arrival (TDOA). Cycle identification is the task of finding time of arrival of the incoming signal. Finding time of arrival of the signal needs choosing a reference point which is defined to be the third zero crossing of the signal. ECD is the varied Loran-C signal's envelope from the original pulse. Cycle identification and ECD estimation accuracies have considerable effects on the Loran-C system receivers' localization accuracy and the error in cycle identification or ECD estimation has direct effect on each other so it is important to estimate the reference point and ECD as precise as possible. In this paper, algorithms for cycle identification and ECD estimation are proposed.Furthermore, this paper addresses the problem of the reference points existence between two samples and proposes two algorithms to estimate the reference points time of arrival between two samples which leads to reach high accuracy using low sampling frequency. The simulation results show that the proposed methods for cycle identification and ECD estimation are robust in noisy conditions and intersample cycle identification algorithms give accurate estimate of the reference points between two samples.

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Section: Envelope Based Methods For Cycle Identification Using Matchedmentioning

confidence: 99%

High Performance, Low Cost Loran-C Cycle Identification and ECD Estimation

Tehrani¹,

Pourmohammad²

2017

ISSPJ

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“…Another similar approach is to have the highly directional microphones movable and having them scan area of interest for a sound source. However, the problem with these ideas is that resolution is generally low [6]. The most popularly used methods are based on arrival of sound.…”

Section: Introductionmentioning

confidence: 99%

Real time acoustic source localization of emergency signals

Mandewalkar

Kulkarni

Veena

et al. 2014

2014 International Conference on Advances in Electronics Computers and Communications

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In this paper, we have chronicled the development of sound localization system based on TDOA (Time Difference of Arrival). Acoustic Source Localization (ASL) is a technique used to track and locate the exact location of a sound source using an array of microphones. The concept of ASL uses sound signals captured from an array of microphones and they are processed using TDOA localization method to estimate the probable direction of sound source w.r.t to the microphone location. TDOA algorithm is a time delay estimation technique which estimates the time difference in the signal received at each microphone pair. These time delays obtained are then used in the Linear Least Squares (LSQR) Algorithm to estimate the source position w.r.t the microphone array. This system is implemented in real-time by using an on-board DSP processor TMS320C6748.

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“…The calculation of the speed of sound depended on ambient temperature and azimuth angle is shown in (1) and (2), respectively [21].…”

Section: Amplitude (V)mentioning

confidence: 99%

“…In this approach, the inverse square law of energy propagation is used for sound localization. The received sound signals are defined as 1 and 2 [21].…”

Section: Amplitude (V)mentioning

confidence: 99%

Estimation of Trajectory and Location for Mobile Sound Source

Çatalbaş¹,

Yıldırım²,

Gülten³

et al. 2016

ijacsa

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Abstract-In this paper, we present an approach to estimate mobile sound source trajectory. An artificially created sound source signal is used in this work. The main aim of this paper is to estimate the mobile object trajectory via sound processing methods. The performance of generalized cross correlation techniques is compared with that of noise reduction filters for the success of trajectory estimation. The azimuth angle between the sound source and receiver is calculated during the whole movement. The parameter of Interaural Time Difference (ITD) is utilized for determining azimuth angle. The success of estimated delay is compared with different types of Generalized Cross Correlation (GCC) algorithms. In this study, an approach for sound localization and trajectory estimation on 2D space is proposed. Besides, different types of pre-filter method are tried for removing the noise and signal smoothing of recorded sound signals. Some basic parameters of sound localization process are also explained. Moreover, the calculation error of average azimuth angle is compared with different GCC and pre-filtered methods. To conclude, it is observed that estimation of location and trajectory information of a mobile object from a stereo sound recording is realized successfully.

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N-dimensional N-microphone sound source localization

Cited by 21 publications

References 59 publications

High Performance, Low Cost Loran-C Cycle Identification and ECD Estimation

High Performance, Low Cost Loran-C Cycle Identification and ECD Estimation

Real time acoustic source localization of emergency signals

Estimation of Trajectory and Location for Mobile Sound Source

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