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.

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 DATASET FOR EVALUATION OF THE PERFORMANCE OF THE METHODS OF SOUND SOURCE LOCALIZATION ALGORITHMS USING TETRAHEDRAL MICROPHONE ARRAYS

2020 ◽  
Vol 12 (0) ◽  
pp. 1-8
Author(s):  
Saulius Sakavičius
Keyword(s):  
Source Localization ◽  
Sound Source ◽  
Microphone Array ◽  
Direction Of Arrival ◽  
Microphone Arrays ◽  
Sound Source Localization ◽  
Direction Of Arrival Estimation ◽  
Sound Sources ◽  
Localization Algorithms ◽  
Geometrical Information

For the development and evaluation of a sound source localization and separation methods, a concise audio dataset with complete geometrical information about the room, the positions of the sound sources, and the array of microphones is needed. Computer simulation of such audio and geometrical data often relies on simplifications and are sufficiently accurate only for a specific set of conditions. It is generally desired to evaluate algorithms on real-world data. For a three-dimensional sound source localization or direction of arrival estimation, a non-coplanar microphone array is needed.Simplest and most general type of non-coplanar array is a tetrahedral array. There is a lack of openly accessible realworld audio datasets obtained using such arrays. We present an audio dataset for the evaluation of sound source localization algorithms, which involve tetrahedral microphone arrays. The dataset is complete with the geometrical information of the room, the positions of the sound sources and the microphone array. Array audio data was captured for two tetrahedral microphone arrays with different distances between microphones and one or two active sound sources. The dataset is suitable for speech recognition and direction-of-arrival estimation, as the signals used for sound sources were speech signals.

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.

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.

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