Development of the Source Reconstruction System by Combining Sound Source Localization and Time Reversal Method

2016 ◽  
Vol 34 (1) ◽  
pp. 35-40
Author(s):  
S.-C. Lin ◽  
G.-P. Too ◽  
C.-W. Tu
Keyword(s):  
Impulse Response ◽  
Response Function ◽  
Source Localization ◽  
Sound Source ◽  
Time Reversal ◽  
Impulse Response Function ◽  
Source Location ◽  
Sound Source Localization ◽  
Target Sound ◽  
Reconstructed Signal

AbstractThis study explored the target sound source location at unknown situation and processed the received signal to determine the location of the target, including the reconstructed signal of source immediately. In this paper, it used triangulation sound sources localization and time reversal method (TRM) to reconstruct the source signals. The purpose is to use a sound source localization method with a simple device to quickly locate the position of the sound source. This method uses the microphone array to measure signal from the target sound source. Then, the sound source location is calculated and is indicated by Cartesian coordinates. The sound source location is then used to evaluate free field impulse response function which can replace the impulse response function used in time-reversal method. This process reduces the computation time greatly which makes possible for a real time source localization and source signal separation.

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 Sound Source Localization Using a Convolutional Neural Network and Regression Model

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 8031
Author(s):  
Tan-Hsu Tan ◽  
Yu-Tang Lin ◽  
Yang-Lang Chang ◽  
Mohammad Alkhaleefah
Keyword(s):  
Neural Network ◽  
Regression Model ◽  
Convolutional Neural Network ◽  
Impulse Response ◽  
Source Localization ◽  
Sound Source ◽  
Real Life ◽  
Sound Source Localization ◽  
Time Frequency ◽  
Simulation Scenario

In this research, a novel sound source localization model is introduced that integrates a convolutional neural network with a regression model (CNN-R) to estimate the sound source angle and distance based on the acoustic characteristics of the interaural phase difference (IPD). The IPD features of the sound signal are firstly extracted from time-frequency domain by short-time Fourier transform (STFT). Then, the IPD features map is fed to the CNN-R model as an image for sound source localization. The Pyroomacoustics platform and the multichannel impulse response database (MIRD) are used to generate both simulated and real room impulse response (RIR) datasets. The experimental results show that an average accuracy of 98.96% and 98.31% are achieved by the proposed CNN-R for angle and distance estimations in the simulation scenario at SNR = 30 dB and RT60 = 0.16 s, respectively. Moreover, in the real environment, the average accuracies of the angle and distance estimations are 99.85% and 99.38% at SNR = 30 dB and RT60 = 0.16 s, respectively. The performance obtained in both scenarios is superior to that of existing models, indicating the potential of the proposed CNN-R model for real-life applications.

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.

ANALYSIS OF ALTERNATIVE METHODS FOR IMPULSE RESPONSE FUNCTIONS BASED ON SIGNAL-TO-NOISE RATIO ENHANCEMENT AND COMPLETENESS OF SOURCE SIGNAL RECONSTRUCTION USING PASSIVE TIME REVERSAL

2013 ◽  
Vol 21 (03) ◽  
pp. 1350008 ◽  
Author(s):  
YU-HAO HSIEH ◽  
GEE-PINN TOO
Keyword(s):  
Impulse Response ◽  
Response Function ◽  
Time Reversal ◽  
Signal To Noise Ratio ◽  
Impulse Response Function ◽  
Computation Time ◽  
Alternative Methods ◽  
Impulse Response Functions ◽  
Response Functions ◽  
Source Signal

Noise reduction and signal separation are important functions of acoustic signal processing. This study presents a detailed analysis for designing an acoustic signal processing procedure based on the time-reversal method. For some applications, setting transducers to retransmit at source locations is impracticable. Modeling a wave propagation path between two points using impulse response function is one way to overcome this limitation. This paper introduces alternative methods to calculate impulse response function, including an adaptive digital filter, deconvolution with singular value decomposition and Tikhonov regularization, and correlation. A discussion is also provided on the applicable frequency range and anti-noise ability of the impulse response functions obtained by all three techniques through simulation, and subsequently applies them to the designed time reversal process to enhance the signal-to-noise ratio (SNR) and restore source signals through experimentation. The conclusions of this study are given based on the level of accuracy using the SNR and correlation coefficient as indicators, and the computation time required by alternative methods is also an important factor to be discussed for real-time system design. Results prove that the proposed passive time reversal process is capable of enhancing the SNR and restoring the source signal. The alternative methods of calculating the impulse response function offer various advantages, and should be selected according to the application. If the time-cost is the first consideration and there is no dominant noise source, then correlation is the best choice for calculating impulse response function. If completeness of the reconstructed signal is the key point, the optimal deconvolution process is appropriate. If noise reduction is the highest priority in extracting a useful signal from noisy environments while ensuring acceptable restoration capability and computation time, an adaptive digital filter is suitable.

Design and Assessment of Sound Source Localization System with a UAV-Embedded Microphone Array

2017 ◽  
Vol 29 (1) ◽  
pp. 154-167 ◽  
Author(s):  
Kotaro Hoshiba ◽  
◽  
Osamu Sugiyama ◽  
Akihide Nagamine ◽  
Ryosuke Kojima ◽  
...  
Keyword(s):  
Source Localization ◽  
Sound Source ◽  
Microphone Array ◽  
Outdoor Environment ◽  
Sound Source Localization ◽  
Detection Accuracy ◽  
Localization Method ◽  
Localization System ◽  
Target Sound ◽  
Sound Environment

[abstFig src='/00290001/15.jpg' width='300' text='Visualization of localization result' ] We have studied on robot-audition-based sound source localization using a microphone array embedded on a UAV (unmanned aerial vehicle) to locate people who need assistance in a disaster-stricken area. A localization method with high robustness against noise and a small calculation cost have been proposed to solve a problem specific to the outdoor sound environment. In this paper, the proposed method is extended for practical use, a system based on the method is designed and implemented, and results of sound source localization conducted in the actual outdoor environment are shown. First, a 2.5-dimensional sound source localization method, which is a two-dimensional sound source localization plus distance estimation, is proposed. Then, the offline sound source localization system is structured using the proposed method, and the accuracy of the localization results is evaluated and discussed. As a result, the usability of the proposed extended method and newly developed three-dimensional visualization tool is confirmed, and a change in the detection accuracy for different types or distances of the sound source is found. Next, the sound source localization is conducted in real-time by extending the offline system to online to ensure that the detection performance of the offline system is kept in the online system. Moreover, the relationship between the parameters and detection accuracy is evaluated to localize only a target sound source. As a result, indices to determine an appropriate threshold are obtained and localization of a target sound source is realized at a designated accuracy.

Fractional Vector-Order h-Realisation of the Impulse Response Function

2020 ◽  
Vol 14 (2) ◽  
pp. 108-113
Author(s):  
Ewa Pawłuszewicz
Keyword(s):  
Control Systems ◽  
Impulse Response ◽  
Response Function ◽  
Impulse Response Function ◽  
Linear Control ◽  
Linear Control Systems

AbstractThe problem of realisation of linear control systems with the h–difference of Caputo-, Riemann–Liouville- and Grünwald–Letnikov-type fractional vector-order operators is studied. The problem of existing minimal realisation is discussed.

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