On the application of SEGAN for the attenuation of the ego-noise in the speech sound source localization problem

Spadini, Tito; Aldeia, Guilherme Seidyo Imai; Barreto, Guilherme A.; Alves, Kaleb; Ferreira, Henrique; Suyama, Ricardo; Nose-Filho, Kenji

doi:10.1109/wcnps.2019.8896308

Cited by 2 publications

(1 citation statement)

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“…In addition to promising results for speech enhancement, recent years saw the emergence of deep learning applied to sound source localization. DNN sound enhancement is typically a pre-processing step for traditional source localization algorithms [36]. While a DNN can also be trained to predict the location of the sound source directly from the multi-channel microphone signal, the performance typically drops significantly in low-SNR scenarios [37].…”

Section: Introductionmentioning

confidence: 99%

Deep-Learning-Assisted Sound Source Localization From a Flying Drone

Wang

Cavallaro

2022

IEEE Sensors J.

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Sound source localization from a flying drone is a challenging task due to the strong ego-noise from rotating motors and propellers as well as the movement of the drone and the sound sources. To address this challenge, we propose a deep learning-based framework that integrates single-channel noise reduction and multi-channel source localization. In this framework we suppress the ego-noise and estimate a time-frequency soft ratio mask with a single-channel deep neural network (DNN). Then we design two downstream multi-channel source localization algorithms, based on Steered Response Power (SRP-DNN) and Time-Frequency Spatial filtering (TFS-DNN). The main novelty lies in the proposed TFS-DNN approach, which estimates the presence probability of the target sound at individual time-frequency bins by combining the DNN-inferred soft ratio mask and the instantaneous direction of arrival of the sound received by the microphone array. The time-frequency presence probability of the target sound is then used to design a set of spatial filters to construct a spatial likelihood map for source localization. By jointly exploiting spectral and spatial information, TFS-DNN robustly processes signals in short segments (e.g. 0.5 seconds) in dynamic and low signal-noise-ratio scenarios (e.g. SNR -20 dB). Results on real and simulated data in a variety of scenarios (static sources, moving sources and moving drones) indicate the advantage of TFS-DNN over competing methods, including SRP-DNN and the state-of-the-art time-frequency spatial filtering.

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Section: Introductionmentioning

confidence: 99%

Deep-Learning-Assisted Sound Source Localization From a Flying Drone

Wang

Cavallaro

2022

IEEE Sensors J.

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Generative Adversarial LSTM Networks Learning for Resource Allocation in UAV-Served M2M Communications

Liu

Zhou

et al. 2021

IEEE Wireless Commun. Lett.

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This letter investigates the resource allocation problem for multiple Unmanned Aerial Vehicles (UAVs)-served Machine-to-Machine (M2M) communications. Our goal is to maximize the sum-rate of UAVs-served M2M communications by jointly considering the transmission power, transmission mode, frequency spectrum, relay selection and the trajectory of UAVs. In order to model the uncertainty of stochastic environments, we formulate the resource allocation problem to be a Markov game, which is the generalization of Markov Decision Process (MDP) for the case of multiple agents. However, owning to the UAVs mobility poses the difficulty of perceiving the environment, we propose a Long Short-Term Memory (LSTM) with Generative Adversarial Networks (GANs) framework to better track and forecast the UAVs mobility and improving the network reward. Numerical results demonstrate that the proposed framework outperforms the conventional LSTM and Deep Q-Network (DQN) algorithms.

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On the application of SEGAN for the attenuation of the ego-noise in the speech sound source localization problem

Cited by 2 publications

References 10 publications

Deep-Learning-Assisted Sound Source Localization From a Flying Drone

Deep-Learning-Assisted Sound Source Localization From a Flying Drone

Generative Adversarial LSTM Networks Learning for Resource Allocation in UAV-Served M2M Communications

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