Sound Source Localization in a Reverberant Room Using Harmonic Based Music

Birnie, Lachlan; Abhayapala, Thushara D.; Chen, Hanchi; Samarasinghe, Prasanga N.

doi:10.1109/icassp.2019.8683098

Cited by 16 publications

(10 citation statements)

References 18 publications

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“…The process of locating a sound source with a microphone array is referred to as the beamforming method. Beamforming methods comprise several families of algorithms, including the Delay-and-Sum (DaS) beamformers [ 22 , 23 , 24 ], the Generalized Sidelobe Cancellation (GSC) beamformers [ 25 , 26 , 27 ], beamformers based on the MUltiple SIgnal Classification (MUSIC) algorithm [ 28 , 29 ], and beamformers based on the Estimation of Signal Parameters via Rotational Invariance Technique (ESPRIT) algorithm [ 30 , 31 ].…”

Section: Acoustic Beamformingmentioning

confidence: 99%

XCycles Backprojection Acoustic Super-Resolution

Almasri

Vandendriessche

Segers

et al. 2021

Sensors

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The computer vision community has paid much attention to the development of visible image super-resolution (SR) using deep neural networks (DNNs) and has achieved impressive results. The advancement of non-visible light sensors, such as acoustic imaging sensors, has attracted much attention, as they allow people to visualize the intensity of sound waves beyond the visible spectrum. However, because of the limitations imposed on acquiring acoustic data, new methods for improving the resolution of the acoustic images are necessary. At this time, there is no acoustic imaging dataset designed for the SR problem. This work proposed a novel backprojection model architecture for the acoustic image super-resolution problem, together with Acoustic Map Imaging VUB-ULB Dataset (AMIVU). The dataset provides large simulated and real captured images at different resolutions. The proposed XCycles BackProjection model (XCBP), in contrast to the feedforward model approach, fully uses the iterative correction procedure in each cycle to reconstruct the residual error correction for the encoded features in both low- and high-resolution space. The proposed approach was evaluated on the dataset and showed high outperformance compared to the classical interpolation operators and to the recent feedforward state-of-the-art models. It also contributed to a drastically reduced sub-sampling error produced during the data acquisition.

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Section: Acoustic Beamformingmentioning

confidence: 99%

XCycles Backprojection Acoustic Super-Resolution

Almasri

Vandendriessche

Segers

et al. 2021

Sensors

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“…It is common knowledge that the performance of the subspace-based methods are susceptible to strong reverberation and background noise [16]. Recently a variation of MUSIC was proposed in [17] to improve its robustness in a reverberant room assuming the prior knowledge of room coupling coefficients.…”

Section: A Literature Reviewmentioning

confidence: 99%

Multi-Source DOA Estimation Through Pattern Recognition of the Modal Coherence of a Reverberant Soundfield

Fahim

Samarasinghe

Abhayapala

2020

IEEE/ACM Trans. Audio Speech Lang. Process.

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We propose a novel multi-source direction of arrival (DOA) estimation technique using a convolutional neural network algorithm which learns the modal coherence patterns of an incident soundfield through measured spherical harmonic coefficients. We train our model for individual time-frequency bins in the short-time Fourier transform spectrum by analyzing the unique snapshot of modal coherence for each desired direction. The proposed method is capable of estimating simultaneously active multiple sound sources on a 3D space using a single-source training scheme. This single-source training scheme reduces the training time and resource requirements as well as allows the reuse of the same trained model for different multi-source combinations. The method is evaluated against various simulated and practical noisy and reverberant environments with varying acoustic criteria and found to outperform the baseline methods in terms of DOA estimation accuracy. Furthermore, the proposed algorithm allows independent training of azimuth and elevation during a full DOA estimation over 3D space which significantly improves its training efficiency without affecting the overall estimation accuracy.

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“…The process to locate a sound source with a microphone array is referred as the beamforming method. Beamforming methods comprise several families of algorithms, including the Delay-and-Sum (D&S) beamformers [3,18,19], the Generalised Sidelobe Cancellation (GSC) beamformers [20,21,22], beamformers based on the MUltiple SIgnal Classification (MUSIC) algorithm [23,24] and beamformers based on the Estimation of Signal Parameters via Rotational Invariance Technique (ESPRIT) algorithm [25,26]. The most popular one—the Delay-and-Sum Beamformers—has been described by Dominguez et al [3] by utilizing a microphone array consisting of 52 microphones.…”

Section: Acoustic Beamformingmentioning

confidence: 99%

CABE: A Cloud-Based Acoustic Beamforming Emulator for FPGA-Based Sound Source Localization

Segers

Vandendriessche

Vandervelden

et al. 2019

Sensors

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Microphone arrays are gaining in popularity thanks to the availability of low-cost microphones. Applications including sonar, binaural hearing aid devices, acoustic indoor localization techniques and speech recognition are proposed by several research groups and companies. In most of the available implementations, the microphones utilized are assumed to offer an ideal response in a given frequency domain. Several toolboxes and software can be used to obtain a theoretical response of a microphone array with a given beamforming algorithm. However, a tool facilitating the design of a microphone array taking into account the non-ideal characteristics could not be found. Moreover, generating packages facilitating the implementation on Field Programmable Gate Arrays has, to our knowledge, not been carried out yet. Visualizing the responses in 2D and 3D also poses an engineering challenge. To alleviate these shortcomings, a scalable Cloud-based Acoustic Beamforming Emulator (CABE) is proposed. The non-ideal characteristics of microphones are considered during the computations and results are validated with acoustic data captured from microphones. It is also possible to generate hardware description language packages containing delay tables facilitating the implementation of Delay-and-Sum beamformers in embedded hardware. Truncation error analysis can also be carried out for fixed-point signal processing. The effects of disabling a given group of microphones within the microphone array can also be calculated. Results and packages can be visualized with a dedicated client application. Users can create and configure several parameters of an emulation, including sound source placement, the shape of the microphone array and the required signal processing flow. Depending on the user configuration, 2D and 3D graphs showing the beamforming results, waterfall diagrams and performance metrics can be generated by the client application. The emulations are also validated with captured data from existing microphone arrays.

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Sound Source Localization in a Reverberant Room Using Harmonic Based Music

Cited by 16 publications

References 18 publications

XCycles Backprojection Acoustic Super-Resolution

XCycles Backprojection Acoustic Super-Resolution

Multi-Source DOA Estimation Through Pattern Recognition of the Modal Coherence of a Reverberant Soundfield

CABE: A Cloud-Based Acoustic Beamforming Emulator for FPGA-Based Sound Source Localization

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