Adjoint-Based Identification of Sound Sources for Sound Reinforcement and Source Localization

2020 ◽  
pp. 263-278
Author(s):  
Mathias Lemke ◽  
Lewin Stein
Keyword(s):  
Source Localization ◽  
Sound Sources ◽  
Sound Reinforcement

scholarly journals Towards Robust Multiple Blind Source Localization Using Source Separation and Beamforming

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 532
Author(s):  
Henglin Pu ◽  
Chao Cai ◽  
Menglan Hu ◽  
Tianping Deng ◽  
Rong Zheng ◽  
...  
Keyword(s):  
Source Localization ◽  
Sound Source ◽  
Indoor Localization ◽  
Weighting Function ◽  
Signal To Noise Ratio ◽  
Source Separation ◽  
Sound Source Localization ◽  
Angle Of Arrival ◽  
Sound Sources ◽  
Localization Algorithms

Multiple blind sound source localization is the key technology for a myriad of applications such as robotic navigation and indoor localization. However, existing solutions can only locate a few sound sources simultaneously due to the limitation imposed by the number of microphones in an array. To this end, this paper proposes a novel multiple blind sound source localization algorithms using Source seParation and BeamForming (SPBF). Our algorithm overcomes the limitations of existing solutions and can locate more blind sources than the number of microphones in an array. Specifically, we propose a novel microphone layout, enabling salient multiple source separation while still preserving their arrival time information. After then, we perform source localization via beamforming using each demixed source. Such a design allows minimizing mutual interference from different sound sources, thereby enabling finer AoA estimation. To further enhance localization performance, we design a new spectral weighting function that can enhance the signal-to-noise-ratio, allowing a relatively narrow beam and thus finer angle of arrival estimation. Simulation experiments under typical indoor situations demonstrate a maximum of only 4∘ even under up to 14 sources.

Analytical Passive Time Reversal Method Combined With Equivalent Source Method for Sound Source Localization in an Enclosure

2019 ◽  
Vol 141 (3) ◽  
Author(s):  
Chuan-Xing Bi ◽  
Yong-Chang Li ◽  
Yong-Bin Zhang ◽  
Rong Zhou
Keyword(s):  
Frequency Response ◽  
Response Function ◽  
Source Localization ◽  
Time Reversal ◽  
Arbitrary Shape ◽  
Frequency Response Function ◽  
Sound Source Localization ◽  
Measurement Point ◽  
Sound Sources ◽  
Equivalent Source

The analytical passive time reversal method (APTRM) is a powerful technique for sound source localization. In that technique, it generally requires that the frequency response function relating the measurement point to the focusing point should be known in advance. However, inside an enclosure of arbitrary shape, there is no theoretical formulation of this frequency response function, and using the APTRM with the free-field Green's function might lead to inaccurate localization of sound sources. This paper proposes a method combining the APTRM with the equivalent source method (ESM) to locate sound sources in an enclosure of arbitrary shape. In this method, the frequency response function relating the measurement point to the focusing point inside the enclosure is first calculated numerically using the ESM, and then the APTRM with this numerical frequency response function is used to realize the localization of sound sources. Numerical simulations in a rectangular enclosure and an enclosure of arbitrary shape as well as an experiment in a rectangular wooden cabinet are performed to verify the validity of the proposed method. The results demonstrate that the frequency response function in an enclosure can be accurately calculated using the ESM; based on measurements with a spherical array composed of 48 microphones, the proposed method can effectively locate the sound sources in enclosures of different shapes and work stably under the situation of low signal-to-noise ratio.

scholarly journals Acoustic Sensor for Detecting Intruders in Wild Environments

2018 ◽  
Vol 11 (2) ◽  
pp. 56-59 ◽  
Author(s):  
Olimpiu Pop ◽  
Corneliu Rusu ◽  
Lacrimioara Grama
Keyword(s):  
Transfer Function ◽  
Source Localization ◽  
Experimental Results ◽  
Acoustic Sensor ◽  
Acoustic Sensors ◽  
Audio Signals ◽  
Acoustic Source ◽  
Sound Sources ◽  
Acoustic Source Localization ◽  
Head Related Transfer Function

Abstract The aim of this work is to develop a device capable to record multiple audio signals (in our case 4 audio signals from 4 microphones of the area) and transmit the information through a network for acoustic source localization. We briefly discuss the first two versions, then the HRTF (Head Related Transfer Function) version of the acoustic sensors is detailed. Experimental results for identifying sound sources are also presented.

Phase calibration method for microphone array based on multiple sound sources

2021 ◽  
Vol 263 (6) ◽  
pp. 659-669
Author(s):  
Bo Jiang ◽  
XiaoQin Liu ◽  
Xing Wu
Keyword(s):  
Nonlinear Equation ◽  
Source Localization ◽  
Sound Source ◽  
Microphone Array ◽  
Phase Error ◽  
Phase Mismatch ◽  
Single Frequency ◽  
Sound Source Localization ◽  
Sound Sources ◽  
Error Calibration

In the microphone array, the phase error of each microphone causes a deviation in sound source localization. At present, there is a lack of effective methods for phase error calibration of the entire microphone array. In order to solve this problem, a phase mismatch calculation method based on multiple sound sources is proposed. This method requires collecting data from multiple sound sources in turn, and constructing a nonlinear equation setthrough the signal delay and the geometric relationship between the microphones and the sound source positions. The phase mismatch of each microphone can be solved from the nonlinear equation set. Taking the single frequency signal as an example, the feasibility of the method is verified by experiments in a semi-anechoic chamber. The phase mismatches are compared with the calibration results of exchanging microphone. The difference of the phase error values measured by the two methods is small. The experiment also shows that the accuracy of sound source localization by beamforming is improved. The method is efficient for phase error calibration of arrays with a large number of microphones.

Influence of Different Impulse Response Measurement Signals on MUSIC-Based Sound Source Localization

2017 ◽  
Vol 29 (1) ◽  
pp. 72-82 ◽  
Author(s):  
Takuya Suzuki ◽  
◽  
Hiroaki Otsuka ◽  
Wataru Akahori ◽  
Yoshiaki Bando ◽  
...  
Keyword(s):  
Impulse Response ◽  
Source Localization ◽  
Sound Source ◽  
Transfer Functions ◽  
Signal To Noise Ratio ◽  
Microphone Array ◽  
Sound Source Localization ◽  
Sound Sources ◽  
Response Measurement ◽  
Significant Difference

[abstFig src='/00290001/07.jpg' width='300' text='Six impulse response measurement signals' ] Two major functions, sound source localization and sound source separation, provided by robot audition open source software HARK exploit the acoustic transfer functions of a microphone array to improve the performance. The acoustic transfer functions are calculated from the measured acoustic impulse response. In the measurement, special signals such as Time Stretched Pulse (TSP) are used to improve the signal-to-noise ratio of the measurement signals. Recent studies have identified the importance of selecting a measurement signal according to the applications. In this paper, we investigate how six measurement signals – up-TSP, down-TSP, M-Series, Log-SS, NW-SS, and MN-SS – influence the performance of the MUSIC-based sound source localization provided by HARK. Experiments with simulated sounds, up to three simultaneous sound sources, demonstrate no significant difference among the six measurement signals in the MUSIC-based sound source localization.

scholarly journals Compressing Sensing Based Source Localization for Controlled Acoustic Signals Using Distributed Microphone Arrays

2017 ◽  
Vol 2017 ◽  
pp. 1-11
Author(s):  
Wei Ke ◽  
Xiunan Zhang ◽  
Yanan Yuan ◽  
Jianhua Shao
Keyword(s):  
Source Localization ◽  
Sound Source ◽  
Reconstruction Algorithm ◽  
Acoustic Signals ◽  
Substantial Improvement ◽  
Microphone Arrays ◽  
Sound Source Localization ◽  
Sound Sources ◽  
Reconstruction Performance ◽  
Long Time

In order to enhance the accuracy of sound source localization in noisy and reverberant environments, this paper proposes an adaptive sound source localization method based on distributed microphone arrays. Since sound sources lie at a few points in the discrete spatial domain, our method can exploit this inherent sparsity to convert the localization problem into a sparse recovery problem based on the compressive sensing (CS) theory. In this method, a two-step discrete cosine transform- (DCT-) based feature extraction approach is utilized to cover both short-time and long-time properties of acoustic signals and reduce the dimensions of the sparse model. In addition, an online dictionary learning (DL) method is used to adjust the dictionary for matching the changes of audio signals, and then the sparse solution could better represent location estimations. Moreover, we propose an improved block-sparse reconstruction algorithm using approximate l0 norm minimization to enhance reconstruction performance for sparse signals in low signal-noise ratio (SNR) conditions. The effectiveness of the proposed scheme is demonstrated by simulation results and experimental results where substantial improvement for localization performance can be obtained in the noisy and reverberant conditions.

Functional generalized inverse beamforming with regularization matrix applied to sound source localization

2016 ◽  
Vol 23 (18) ◽  
pp. 2977-2988 ◽  
Author(s):  
Shu Li ◽  
Zhongming Xu ◽  
Zhifei Zhang ◽  
Yansong He ◽  
Jin Mao
Keyword(s):  
Source Localization ◽  
Dynamic Range ◽  
Generalized Inverse ◽  
Free Field ◽  
Computation Time ◽  
Side Lobe ◽  
Sound Sources ◽  
Low Frequencies ◽  
Common Application ◽  
Regularization Matrix

Microphone arrays have become a popular technique to identify sound sources. They can be utilized to localize the sources for various applications. The most common application is the conventional beamforming that provides the source maps with strong side lobes and poor spatial resolution at low frequencies. To overcome these problems, the focus is set on deconvolution and generalized inverse techniques such as a deconvolution approach for the mapping of acoustic sources (DAMAS) and generalized inverse beamforming (GIB). Although the source maps are clearly improved, these methods have the shortcomings of expensive computing and limited dynamic range. In this paper, we propose a source localization method called functional generalized inverse beamforming with regularization matrix (FGIBR) based on an inverse problem. Compared with GIB, the accuracy of FGIBR could be improved by introducing a new beamforming regularization matrix and a scaling parameter c0. Also the dynamic range of the source maps can be increased by applying FGIBR with an exponent parameter called order v. Several simulated examples are given to illustrate that the side lobes are suppressed and the main lobe becomes much narrow; moreover, if order v is increased, the beamforming side lobes can be sharply reduced and the actual position of the noise source can be precisely located. Then FGIBR is implemented to deal with experimental data in the free field. In the case of the experiment, the source is correctly located. The proposed FGIBR demonstrates a good performance in terms of resolution and side lobe rejection compared with other beamforming methods. Furthermore, the computation time is shown to be low if the iteration and order are reasonable.

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