Spectral mask estimation using deep neural networks for inter-sensor data ratio model based robust DOA estimation

Zheng, Weiqiao; Zou, Yuexian; Ritz, Christian

doi:10.1109/icassp.2015.7177984

Cited by 10 publications

(6 citation statements)

References 20 publications

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“…is method can accurately locate the speaker in a dynamic acoustic scene. Zheng et al [62] proposed to use different values of SNR and noise to train DNNs, which achieved higher DOA accuracy at low SNR and improved the intersensor data ratio (ISDR) performance of a single acoustic vector sensor (AVS) in a noisy environment. Wang et al [60] proposed to use acoustic vector sensors (AVSs) to estimate the DOA of multiple voice sources through clustering of data ratios between sensors.…”

Section: Speech Doa Estimationmentioning

confidence: 99%

Deep Learning Approach in DOA Estimation: A Systematic Literature Review

Li²,

Rum

2021

Mobile Information Systems

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In array signal processing, the direction of arrival (DOA) of the signal source has drawn broad research interests with its wide applications in fields such as sonar, radar, communications, medical detection, and electronic countermeasures. In recent years, the application of deep learning (DL) to DOA estimation has achieved great success. This study provides a systematic review of research on DOA estimation using deep neural network methods. We manually selected twenty-five papers related to this research from five prominent databases (SpringerLink, IEEE Xplore, ScienceDirect, Scopus, and Google Scholar) for exploration. Six questions describing the overall trend of DOA estimation using deep learning are put forward. Then, we answered these questions by reviewing the literature. This review is helpful for researchers in this field because it provides more specific and comprehensive information needed for future research. Specifically, we first analyzed the background of the selected papers, including the type of publication, the number of citations, and the country of origin. Then, the DL technology used in DOA estimation is systematically analyzed, including the purpose of using DL in DOA estimation, various DL models (convolutional neural network, deep neural network, and combination network), and various DOA estimation schemes. Finally, various evaluation criteria (root-mean-squared error, accuracy, and mean absolute error) are used to evaluate the DL technology in DOA estimation, and various factors (signal-to-noise ratio, number of snapshots, number of antennas, and number of signal sources) affecting DOA estimation are analyzed. Based on our findings, we believe that deep learning can perform DOA estimation well, and there is still room for improvement in deep learning technology. In this study, the factors affecting DOA estimation can be used as the direction for researchers to conduct in-depth research.

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Section: Speech Doa Estimationmentioning

confidence: 99%

Deep Learning Approach in DOA Estimation: A Systematic Literature Review

Li²,

Rum

2021

Mobile Information Systems

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“…If the task is regarded as regression, the output of ANN is the angle of arrival, and the number of output neurons is decided by the algorithm. Moreover, for some other ANN-based DOA estimation methods to complete the regression task [7], [17], post-processing means obtaining the DOA by combining the output features of ANN with special information about the received signals. Phase discontinuity: The azimuth ranges from 0 to 360 • .…”

Section: Post-processing For Cnnmentioning

confidence: 99%

“…These algorithms process the original data to learn simple features in the lower layers and more complicated features in the higher layers and have been successfully applied to DOA estimation. In [7], the restricted Boltzmann machines (RBMs) are employed for unsupervised pre-training, and then a supervised fine-tuning stage is used to accomplish the deep neural network (DNN) training. The system works well for the DOA estimation of the speech signals.…”

Section: Introductionmentioning

confidence: 99%

Broadband Direction of Arrival Estimation Based on Convolutional Neural Network

Zhu

Zhang

et al. 2020

IEICE Trans. Commun.

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A convolutional neural network (CNN) for broadband direction of arrival (DOA) estimation of far-field electromagnetic signals is presented. The proposed algorithm performs a nonlinear inverse mapping from received signal to angle of arrival. The signal model used for algorithm is based on the circular antenna array geometry, and the phase component extracted from the spatial covariance matrix is used as the input of the CNN network. A CNN model including three convolutional layers is then established to approximate the nonlinear mapping. The performance of the CNN model is evaluated in a noisy environment for various values of signal-to-noise ratio (SNR). The results demonstrate that the proposed CNN model with the phase component of the spatial covariance matrix as the input is able to achieve fast and accurate broadband DOA estimation and attains perfect performance at lower SNR values.

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“…We note that similar issues exist in the acoustic signal processing and speech recognition community. The AoA of acoustic signals have been widely studied using temporal-spectral features from microphone arrays [25], [26]. Despite huge differences in propagating speed and wavelength, acoustic and RF AoA estimation share common challenges such as multipath, also known as reverberation, and interference of multiple sources [27].…”

Section: Introductionmentioning

confidence: 99%

DeepAoANet: Learning Angle of Arrival From Software Defined Radios With Deep Neural Networks

Dai

Tran

et al. 2022

IEEE Access

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Direction finding and positioning systems based on RF signals are significantly impacted by multipath propagation, particularly in indoor environments. Existing algorithms (e.g MUSIC) perform poorly in resolving Angle of Arrival (AoA) in the presence of multipath or when operating in a weak signal regime. We note that digitally sampled RF frontends allow for the easy analysis of signals, and their delayed components. Low-cost Software-Defined Radio (SDR) modules enable Channel State Information (CSI) extraction across a wide spectrum, motivating the design of an enhanced Angle-of-Arrival (AoA) solution. We propose a Deep Learning approach to deriving AoA from a single snapshot of the SDR multichannel data. We compare and contrast deep-learning based angle classification and regression models, to estimate up to two AoAs accurately. We have implemented the inference engines on different platforms to extract AoAs in real-time, demonstrating the computational tractability of our approach. To demonstrate the utility of our approach we have collected IQ (In-phase and Quadrature components) samples from a four-element Universal Linear Array (ULA) in various Light-of-Sight (LOS) and Non-Line-of-Sight (NLOS) environments, and published the dataset. Our proposed method demonstrates excellent reliability in determining number of impinging signals and realized mean absolute AoA errors less than 2 • .

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Spectral mask estimation using deep neural networks for inter-sensor data ratio model based robust DOA estimation

Cited by 10 publications

References 20 publications

Deep Learning Approach in DOA Estimation: A Systematic Literature Review

Deep Learning Approach in DOA Estimation: A Systematic Literature Review

Broadband Direction of Arrival Estimation Based on Convolutional Neural Network

DeepAoANet: Learning Angle of Arrival From Software Defined Radios With Deep Neural Networks

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