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

[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.

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Towards Robust Multiple Blind Source Localization Using Source Separation and Beamforming

Sensors ◽

10.3390/s21020532 ◽

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.

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Phase calibration method for microphone array based on multiple sound sources

INTER-NOISE and NOISE-CON Congress and Conference Proceedings ◽

10.3397/in-2021-1620 ◽

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.

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DESIGNING PLANAR MICROPHONE ARRAY FOR SOUND SOURCE LOCALIZATION

Akustika ◽

10.36336/akustika201932123 ◽

2019 ◽

Vol 32 ◽

pp. 123-129 ◽

Cited By ~ 1

Author(s):

Victor Ershov ◽

Vadim Palchikovskiy

Keyword(s):

Inclination Angle ◽

Source Localization ◽

Sound Source ◽

Dynamic Range ◽

Microphone Array ◽

Sound Source Localization ◽

Maximum Diameter ◽

Design Parameters ◽

Sound Sources ◽

The Individual

Mathematical background for designing planar microphone array for localization of sound sources are described shortly. The designing is based on optimization of objective function, which is maximum dynamic range of sound source localization. The design parameters are radial coordinates (distance along the beam from the center of the array) and angle coordinates (beam inclination) of the microphones. It is considered the arrays with the same radial coordinates of the microphones for each beam and the independent radial coordinates of each microphone, as well as the same inclination angle for all beams and the individual inclination angle of each beam. As constraints, it is used the minimum allowable distance between two adjacent microphones, and minimum and maximum diameter of the working area of the array. The solution of the optimization problem is performed by the Minimax method. An estimation of the resolution quality of designed arrays was carried out based on localization of three monopole sources. The array of 3 m in diameter without inclination of the beams and with different radial coordinates of the microphones on each beam was found to be the most efficient configuration among the considered ones.

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Position-dependent hearing in three species of bushcrickets (Tettigoniidae, Orthoptera)

Royal Society Open Science ◽

10.1098/rsos.140473 ◽

2015 ◽

Vol 2 (6) ◽

pp. 140473 ◽

Cited By ~ 5

Author(s):

Reinhard Lakes-Harlan ◽

Jan Scherberich

Keyword(s):

Source Localization ◽

Sound Source ◽

Transfer Functions ◽

Frequency Tuning ◽

Three Dimensional ◽

Sound Source Localization ◽

Sound Sources ◽

Ear Morphology ◽

Hearing Thresholds ◽

Species Specific

A primary task of auditory systems is the localization of sound sources in space. Sound source localization in azimuth is usually based on temporal or intensity differences of sounds between the bilaterally arranged ears. In mammals, localization in elevation is possible by transfer functions at the ear, especially the pinnae. Although insects are able to locate sound sources, little attention is given to the mechanisms of acoustic orientation to elevated positions. Here we comparatively analyse the peripheral hearing thresholds of three species of bushcrickets in respect to sound source positions in space. The hearing thresholds across frequencies depend on the location of a sound source in the three-dimensional hearing space in front of the animal. Thresholds differ for different azimuthal positions and for different positions in elevation. This position-dependent frequency tuning is species specific. Largest differences in thresholds between positions are found in Ancylecha fenestrata . Correspondingly, A. fenestrata has a rather complex ear morphology including cuticular folds covering the anterior tympanal membrane. The position-dependent tuning might contribute to sound source localization in the habitats. Acoustic orientation might be a selective factor for the evolution of morphological structures at the bushcricket ear and, speculatively, even for frequency fractioning in the ear.

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Adaptive Speech Separation Based on Beamforming and Frequency Domain-Independent Component Analysis

Applied Sciences ◽

10.3390/app10072593 ◽

2020 ◽

Vol 10 (7) ◽

pp. 2593

Author(s):

Ke Zhang ◽

Yangjie Wei ◽

Dan Wu ◽

Yi Wang

Keyword(s):

Independent Component Analysis ◽

Frequency Domain ◽

Source Localization ◽

Sound Source ◽

Microphone Array ◽

Component Analysis ◽

Independent Component ◽

Sound Source Localization ◽

Speech Separation ◽

Sound Sources

Voice signals acquired by a microphone array often include considerable noise and mutual interference, seriously degrading the accuracy and speed of speech separation. Traditional beamforming is simple to implement, but its source interference suppression is not adequate. In contrast, independent component analysis (ICA) can improve separation, but imposes an iterative and time-consuming process to calculate the separation matrix. As a supporting method, principle component analysis (PCA) contributes to reduce the dimension, retrieve fast results, and disregard false sound sources. Considering the sparsity of frequency components in a mixed signal, we propose an adaptive fast speech separation algorithm based on multiple sound source localization as preprocessing to select between beamforming and frequency domain ICA according to different mixing conditions per frequency bin. First, a fast positioning algorithm allows calculating the maximum number of components per frequency bin of a mixed speech signal to prevent the occurrence of false sound sources. Then, PCA reduces the dimension to adaptively adjust the weight of beamforming and ICA for speech separation. Subsequently, the ICA separation matrix is initialized based on the sound source localization to notably reduce the iteration time and mitigate permutation ambiguity. Simulation and experimental results verify the effectiveness and speedup of the proposed algorithm.

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

Mokslas - Lietuvos ateitis ◽

10.3846/mla.2020.11462 ◽

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.

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