Drone Audition: Sound Source Localization Using On-Board Microphones

Manamperi, Wageesha; Abhayapala, Thushara D.; Zhang, Jihui; Samarasinghe, Prasanga N.

doi:10.1109/taslp.2022.3140550

Cited by 32 publications

(21 citation statements)

References 29 publications

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“…Quaternion Neural Networks (QNNs) define each input, weight, bias and output as a quaternion in (1). Therefore, the multiplication in (15) becomes a multiplication between two quaternions and has to be performed following the Hamilton product in (2). The quaternion FC (Q-FC) layer with a weight matrix W = W W + W X î + W Y  + W Z κ is then:…”

Section: Dual Quaternion Ambisonics Signalsmentioning

confidence: 99%

“…The weight matrix W ∈ H n×m comprises realvalued submatrices, which are composed according to (2). Due to the reusing of submatrices, a quaternion weight matrix has n × m/4 parameters, thus, quaternion layers save 75% of parameters [31].…”

Section: Dual Quaternion Ambisonics Signalsmentioning

confidence: 99%

“…In recent years, spatial audio is knowing an increasing attention also due to the widespread developing of applications requiring an immersive audio experience, such as virtual reality, scene characterization, speech enhancement or separation, and sound source localization [1][2][3][4]. Indeed, while virtual reality (VR) builds spaces different from reallife, augmented reality (AR) expands them through an enlarged user listening experience that is often based on the user immersion into a 3D sound field [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

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Dual Quaternion Ambisonics Array for Six-Degree-of-Freedom Acoustic Representation

Grassucci¹,

Mancini²,

Brignone³

et al. 2022

Preprint

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Spatial audio methods are gaining a growing interest due to the spread of immersive audio experiences and applications, such as virtual and augmented reality. For these purposes, 3D audio signals are often acquired through arrays of Ambisonics microphones, each comprising four capsules that decompose the sound field in spherical harmonics. In this paper, we propose a dual quaternion representation of the spatial sound field acquired through an array of two First Order Ambisonics (FOA) microphones. The audio signals are encapsulated in a dual quaternion that leverages quaternion algebra properties to exploit correlations among them. This augmented representation with 6 degrees of freedom (6DOF) involves a more accurate coverage of the sound field, resulting in a more precise sound localization and a more immersive audio experience. We evaluate our approach on a sound event localization and detection (SELD) benchmark. We show that our dual quaternion SELD model with temporal convolution blocks (DualQSELD-TCN) achieves better results with respect to real and quaternion-valued baselines thanks to our augmented representation of the sound field. Full code is available at: https://github.com/ispamm/DualQSELD-TCN.

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Section: Dual Quaternion Ambisonics Signalsmentioning

confidence: 99%

Section: Dual Quaternion Ambisonics Signalsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Dual Quaternion Ambisonics Array for Six-Degree-of-Freedom Acoustic Representation

Grassucci¹,

Mancini²,

Brignone³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Moreover, θ and θ ∈ −90°, 90°). The center of the array is used as a reference point, and the sound source coordinates (x, y) can be calculated by the following: Figure 5 shows the direction estimation results of the SRP-PHAT and the SRP-MCCC when the source signal is a 600 Hz sine signal with source coordinates (14,4) and the SNR is −5 dB. The brightness of a point represents the probability that the point is the location of the sound source.…”

Section: Simulation Analysismentioning

confidence: 99%

“…Sound source localization techniques have a wide range of application prospects in civil and military systems, such as intelligent medical systems, security monitoring and sonar detection [ 1 , 2 , 3 , 4 , 5 ]. Existing sound source localization techniques can be divided into the subspace, time delay estimation and beamforming.…”

Section: Introductionmentioning

confidence: 99%

Sound Source Localization Based on Multi-Channel Cross-Correlation Weighted Beamforming

Liu

Zeng

et al. 2022

Micromachines

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Beamforming and its applications in steered-response power (SRP) technology, such as steered-response power delay and sum (SRP-DAS) and steered-response power phase transform (SRP-PHAT), are widely used in sound source localization. However, their resolution and accuracy still need improvement. A novel beamforming method combining SRP and multi-channel cross-correlation coefficient (MCCC), SRP-MCCC, is proposed in this paper to improve the accuracy of direction of arrival (DOA). Directional weight (DW) is obtained by calculating the MCCC. Based on DW, suppressed the non-incoming wave direction and gained the incoming wave direction to improve the beamforming capabilities. Then, sound source localizations based on the dual linear array under different conditions were simulated. Compared with SRP-PHAT, SRP-MCCC has the advantages of high positioning accuracy, strong spatial directivity and robustness under the different signal–noise ratios (SNRs). When the SNR is −10 dB, the average positioning error of the single-frequency sound source at different coordinates decreases by 5.69%, and that of the mixed frequency sound sources at the same coordinate decreases by 5.77%. Finally, the experimental verification was carried out. The results show that the average error of SRP-MCCC has been reduced by 8.14% and the positioning accuracy has been significantly improved, which is consistent with the simulation results. This research provides a new idea for further engineering applications of sound source localization based on beamforming.

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Real-time TDOA-based stationary sound source direction finding

Heydari

Mahabadi

2023

Multimed Tools Appl

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Drone Audition: Sound Source Localization Using On-Board Microphones

Cited by 32 publications

References 29 publications

Dual Quaternion Ambisonics Array for Six-Degree-of-Freedom Acoustic Representation

Dual Quaternion Ambisonics Array for Six-Degree-of-Freedom Acoustic Representation

Sound Source Localization Based on Multi-Channel Cross-Correlation Weighted Beamforming

Real-time TDOA-based stationary sound source direction finding

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