Sound Source Localization Using Head-Related Transfer Functions and Weighted Error Function

2019 ◽  
Vol 105 (4) ◽  
pp. 657-667
Author(s):  
Sungmok Hwang
Keyword(s):  
Transfer Function ◽  
Source Localization ◽  
Sound Source ◽  
Background Noise ◽  
Weighting Function ◽  
Sound Source Localization ◽  
Localization Method ◽  
Input Signals ◽  
Localization Performance ◽  
Weighted Error

This study proposes a sound source localization method using binaural input signals. The method is based on the head-related transfer function (HRTF) database and the interaural transfer function (ITF) obtained from two measured input signals. An algorithm to reduce the effect of background noise on the localization performance in a noisy environment was adopted in the proposed localization method. Weighted error functions (WEFs), defined using the ITF and the ratio of HRTFs for two ears, were used with a special frequency weighting function derived to reduce the effect of noise and to render the WEF a physical meaning. Computer simulations confirmed that the weighting function can effectively reduce the effect of background noise on the localization performance even if the noise power is very high. Localization tests in an actual room confirmed that both the azimuth and elevation angles of sound source can be estimated simultaneously with high accuracy. In particular, the front-back and updown confusions, which are critical limitations for conventional localization methods, could be resolved using two input signals.

Robotic Sound Source Localization Using Bio-Inspired Acoustic Sensors

2012 ◽  
Author(s):  
Laith Sawaqed ◽  
Haijun Liu ◽  
Miao Yu
Keyword(s):  
Lower Bound ◽  
Source Localization ◽  
Sound Source ◽  
Structural Parameters ◽  
Sound Source Localization ◽  
Directional Microphones ◽  
Localization Method ◽  
Mechanical Coupling ◽  
Model Free ◽  
Localization Performance

In sound source localization, there is a fundamental size limit; the smaller the size, the smaller the directional cues that are relied on to pinpoint the sound source. As such, it is challenging to develop miniature sound source localization robotic system where space is too confined to employ conventional microphone arrays without compromising localization performance. Our previous studies show that through mechanical coupling with well-tuned structural parameters, directional microphones inspired by the parasitic fly Ormia ochracea can amplify the minute interaural time delay (ITD) by more than ten times, which enables the reduction of device size significantly while maintaining localization performance. In this paper, Cramer Rao lower bound (CRLB) is derived for the fly-ear inspired sensor and the conventional directional microphones to study the effects of mechanical coupling on the decrease of the theoretical lower bound of azimuth estimation. This improvement gives mobile robots the capability to reactively localize sound in an indoor environment. Using this miniature sensor, new sound source localization method is proposed to localize a stationary sound source in 2-D (azimuth and elevation). In the proposed sound localization method, Model-Free Gradient Descent (MFGD) optimization method, one of the main challenges is to choose the appropriate cost function to achieve minimum number of iterations and the smallest absolute error. To this end, different cost functions are proposed and investigated with different control schemes. Simulation results showed the ability of this technique to solve the ambiguity problem and localize the sound source.

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.

Denoising Weighting Beamforming Method Applied to Sound Source Localization With Airflow Using Microphone Array

2018 ◽  
Vol 140 (6) ◽  
Author(s):  
Zhongming Xu ◽  
Kai Tian ◽  
Yansong He ◽  
Zhifei Zhang ◽  
Shu Li
Keyword(s):  
Source Localization ◽  
Sound Source ◽  
Background Noise ◽  
Sound Propagation ◽  
Phase Mismatch ◽  
Sound Source Localization ◽  
Time Averaging ◽  
Iterative Regularization ◽  
Spectral Matrix ◽  
Incoherent Noise

Conventional frequency domain beamforming (FDBF) relies on the measured cross-spectral matrix (CSM). However, in wind tunnel tests, the CSM diagonal is contaminated by the interference of incoherent noise after long-time averaging which leads the source map to poor resolution. Diagonal removal (DR) can suppress the noise in beamforming results via the deletion of CSM diagonal, but this method leads to the underestimation of source levels and some negative powers in source maps. Some advanced methods, such as background subtraction, make use of background noise reference to counteract the effects of contamination; however, the results usually become unreliable, because the background noise is difficult to keep constant in different measurements. Diagonal denoising (DD) beamforming is a recent approach to suppress the contamination effects, but it attenuates the noise suppression performance. To overcome the limitations of the above methods, a new method called denoising weighting beamforming (DWB) is proposed in this study on the basis of CSM DD and an iterative regularization method is applied to solve the acoustical inverse problem. Besides, in order to correct the phase mismatch caused by the influence of flow on sound propagation, the shear flow correction is added before using DWB. Experiments on sound source reconstruction are conducted in the environment with the flow. Acoustics data obtained via this method show the successful removal of incoherent noise and the corrected phase mismatch. Furthermore, the sound source localization results are promising and the proposed method is simple to implement.

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