Direction of Arrival Estimation with Microphone Arrays Using SRP-PHAT and Neural Networks

Diaz-Guerra, David; Beltrán, José Ramón

doi:10.1109/sam.2018.8448492

Cited by 5 publications

(3 citation statements)

References 25 publications

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“…This technique tries to maximize the output signal of a DAS beamformer but in a determined direction, and directly provides the power of the signal that arrives from such direction [ 22 , 23 ].…”

Section: Beamforming-based Acoustic Energy Mappingmentioning

confidence: 99%

On the Challenges of Acoustic Energy Mapping Using a WASN: Synchronization and Audio Capture

García-Unzueta

Méndez-Monroy

Rascón

2023

Sensors

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Acoustic energy mapping provides the functionality to obtain characteristics of acoustic sources, as: presence, localization, type and trajectory of sound sources. Several beamforming-based techniques can be used for this purpose. However, they rely on the difference of arrival times of the signal at each capture node (or microphone), so it is of major importance to have synchronized multi-channel recordings. A Wireless Acoustic Sensor Network (WASN) can be very practical to install when used for mapping the acoustic energy of a given acoustic environment. However, they are known for having low synchronization between the recordings from each node. The objective of this paper is to characterize the impact of current popular synchronization methodologies as part of the WASN to capture reliable data to be used for acoustic energy mapping. The two evaluated synchronization protocols are: Network Time Protocol (NTP) y Precision Time Protocol (PTP). Additionally, three different audio capture methodologies were proposed for the WASN to capture the acoustic signal: two of them, recording the data locally and one sending the data through a local wireless network. As a real-life evaluation scenario, a WASN was built using nodes conformed by a Raspberry Pi 4B+ with a single MEMS microphone. Experimental results demonstrate that the most reliable methodology is using the PTP synchronization protocol and audio recording locally.

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Section: Beamforming-based Acoustic Energy Mappingmentioning

confidence: 99%

On the Challenges of Acoustic Energy Mapping Using a WASN: Synchronization and Audio Capture

García-Unzueta

Méndez-Monroy

Rascón

2023

Sensors

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“…The main metric used for the single source experiment was the Root Mean Square Angular Error (RMSAE) [40], defined for a pair of positions (u,û) each with azimuth and elevations (θ,ϕ) and ( θ, φ) respectively, as E(p,p) = arccos 2 (cosθcos θ+sinθsin θcos(ϕ− φ)), (12) where (12) was averaged for all frames in the dataset. For multiple sources, the localization error is defined for each correctly detected source using the ground truth association matrix A.…”

Section: A Evaluation Metricsmentioning

confidence: 99%

The Neural-SRP Method for Universal Robust Multi-source Tracking

Grinstein,

Hicks,

Waterschoot

et al. 2023

IEEE Open J. Signal Process.

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The research leading to these results has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 956369 and from the European Research Council under the European Union's Horizon 2020 research and innovation program / ERC Consolidator Grant: SONORA (no. 773268). This paper reflects only the authors' views and the Union is not liable for any use that may be made of the contained information.

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“…Initially, the most common input features were the GCCs between the signals of each sensor, but we can also find other approaches such as using the eigenvectors of the spatial covariance matrix [17]. In [31], we proposed using low-resolution SRP-PHAT maps, in that case combined with fully connected perceptrons. More recently, some techniques have been proposed using 2D convolutional networks over the spectrogram of the microphone signals, sometimes using only the phase information [28], and sometimes using both the magnitude and the phase [20]; some transformations, such as using the cepstrogram [19], have also been proposed .…”

Section: B Input Features and Network Architecturementioning

confidence: 99%

Robust Sound Source Tracking Using SRP-PHAT and 3D Convolutional Neural Networks

Diaz-Guerra,

Miguel,

Beltran

2020

Preprint

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In this paper, we present a new sound source DOA estimation and tracking system based on the well known SRP-PHAT algorithm and a three-dimensional Convolutional Neural Network. It uses SRP-PHAT power maps as input features of a fully convolutional causal architecture that uses 3D convolutional layers to accurately perform the tracking of a sound source even in highly reverberant scenarios where most of the state of the art techniques fail. Unlike previous methods, since we do not use bidirectional recurrent layers and all our convolutional layers are causal in the time dimension, our system is feasible for realtime applications and it provides a new DOA estimation for each new SRP-PHAT map. To train the model, we introduce a new procedure to simulate random trajectories as they are needed during the training, equivalent to an infinite-size dataset with high flexibility to modify its acoustical conditions such as the reverberation time. We use both acoustical simulations on a large range of reverberation times and the actual recordings of the LOCATA dataset to prove the robustness of our system and its good performance even using low-resolution SRP-PHAT maps.

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Direction of Arrival Estimation with Microphone Arrays Using SRP-PHAT and Neural Networks

Cited by 5 publications

References 25 publications

On the Challenges of Acoustic Energy Mapping Using a WASN: Synchronization and Audio Capture

On the Challenges of Acoustic Energy Mapping Using a WASN: Synchronization and Audio Capture

The Neural-SRP Method for Universal Robust Multi-source Tracking

Robust Sound Source Tracking Using SRP-PHAT and 3D Convolutional Neural Networks

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