Single-Channel Sound Source Localization Based on Discrimination of Acoustic Transfer Functions

[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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Intelligent Sound Source Localization Based on Head-Related Transfer Functions

2007 IEEE International Conference on Intelligent Computer Communication and Processing ◽

10.1109/iccp.2007.4352147 ◽

2007 ◽

Cited By ~ 1

Author(s):

Fakheredine Keyrouz ◽

Abdallah Abou Saleh

Keyword(s):

Source Localization ◽

Sound Source ◽

Transfer Functions ◽

Sound Source Localization

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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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Bias-Compensated Informed Sound Source Localization Using Relative Transfer Functions

IEEE/ACM Transactions on Audio Speech and Language Processing ◽

10.1109/taslp.2018.2825110 ◽

2018 ◽

Vol 26 (7) ◽

pp. 1275-1289 ◽

Cited By ~ 1

Author(s):

Mojtaba Farmani ◽

Michael Syskind Pedersen ◽

Zheng-Hua Tan ◽

Jesper Jensen

Keyword(s):

Source Localization ◽

Sound Source ◽

Transfer Functions ◽

Sound Source Localization ◽

Relative Transfer

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An Analytic Model of Subminiature Auditory Sensation System for Sound Source Localization

Volume 1: 21st Biennial Conference on Mechanical Vibration and Noise, Parts A, B, and C ◽

10.1115/detc2007-35022 ◽

2007 ◽

Author(s):

Qingsheng Wang ◽

Na Ta ◽

Zhushi Rao

Keyword(s):

Source Localization ◽

Sound Source ◽

Sound Pressure ◽

Transfer Functions ◽

Sound Source Localization ◽

Directional Hearing ◽

Analytic Model ◽

Detailed Model ◽

Close Proximity ◽

Cuticular Structure

It is reported that some types of insects have a remarkable ability to detect the direction of an incident sound even though its acoustic sensory organs are in very close proximity to each other. The ears are maybe jointed by a cuticular structure with which the separated motions can be coupled mechanically and thus be magnified. In this paper, a detailed model is setup to describe the principle of this type of localization used a mechanical coupled structure. The transfer functions and the responses of the model in terms of time and frequency are analyzed to describe the mechanism of its ability of directional hearing. This analytical model provides a method to design the experimental model for the predetermined incident sound pressure, and the analysis of this model shows that this structure have the ability to determine the direction of the incident stimulus.

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Single-Channel Multiple-Receiver Sound Source Localization System with Homomorphic Deconvolution and Linear Regression

Sensors ◽

10.3390/s21030760 ◽

2021 ◽

Vol 21 (3) ◽

pp. 760

Author(s):

Yeonseok Park ◽

Anthony Choi ◽

Keonwook Kim

Keyword(s):

Linear Regression ◽

Source Localization ◽

Sound Source ◽

Single Channel ◽

Sound Source Localization ◽

Scalable Algorithms ◽

Model Order ◽

Localization System ◽

Parametric Algorithm ◽

Non Parametric

The conventional sound source localization systems require the significant complexity because of multiple synchronized analog-to-digital conversion channels as well as the scalable algorithms. This paper proposes a single-channel sound localization system for transport with multiple receivers. The individual receivers are connected by the single analog microphone network which provides the superimposed signal over simple connectivity based on asynchronized analog circuit. The proposed system consists of two computational stages as homomorphic deconvolution and machine learning stage. A previous study has verified the performance of time-of-flight estimation by utilizing the non-parametric and parametric homomorphic deconvolution algorithms. This paper employs the linear regression with supervised learning for angle-of-arrival prediction. Among the circular configurations of receiver positions, the optimal location is selected for three-receiver structure based on the extensive simulations. The non-parametric method presents the consistent performance and Yule–Walker parametric algorithm indicates the least accuracy. The Steiglitz–McBride parametric algorithm delivers the best predictions with reduced model order as well as other parameter values. The experiments in the anechoic chamber demonstrate the accurate predictions in proper ensemble length and model order.

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