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Soundsource 22/28/2023 ![]() It is natural to conclude that incorporating the additional data about the UAV dynamics can benefit SSL. In the context of UAVs, the drone contains multiple sensors that can provide additional real-time data about the UAV itself such as its rotor speeds and trajectory. ![]() For noise-robust SSL, a generalized eigenvalue decomposition-based multiple signal classification (GEVD-MUSIC) algorithm combined with an adaptive estimation method of the noise correlation matrix was proposed by. The TDOA can be estimated using various algorithms such as multiple signal classification (MUSIC) and generalized cross-correlation (GCC). SSL algorithms generally utilize the time difference of arrival (TDOA) feature from multiple microphone pairs. In this article, we report on our efforts to improve upon existing techniques employed in SSL systems for UAVs. However, SSL is made difficult by the presence of high ego-noise generated by the rotors and propellers of the UAV. It is evident that a sound source localization (SSL)-based detection system can compliment the visual detection in scenarios where the field of view may be occluded due to obstacles or bad lighting or even operations carried out at night. More recently, there has been research on using embedded microphone arrays in the UAVs to triangulate the sound coming from emergency whistles or humans trapped beneath debris. In search and rescue scenarios, UAVs have typically been equipped with cameras that help locate areas with rubble and debris where people might be trapped. They can also cover a larger area than a group of human rescuers could on foot. UAVs have been effective because of their ability to reach areas not easily accessible by humans. Reports by the United Nations and other humanitarian organizations document the successful deployment of UAVs in relief efforts after natural disasters such as the major earthquakes in Haiti and Nepal in 20, respectively. Unmanned aerial vehicles (UAVs), ubiquitously known as drones, have found great use in a wide range of applications-from casual use in photography to search and rescue operations where human lives are at stake. ![]() To evaluate the different methods, we also introduce a well-known parameter-area under the curve (AUC) of cumulative histogram plots of angular deviations-as a performance indicator which, to our knowledge, has not been used as a performance indicator for this sort of problem before. We then evaluate the SSL performance using the proposed and baseline methods and find that the DOANet shows promising results compared to both the angular spectrum methods with and without SCHC. The advantage of using DOANet is that it does not require any hand-crafted audio features or ego-noise reduction for DOA estimation. ![]() DOANet is based on a one-dimensional dilated convolutional neural network that computes the azimuth and elevation angles of the target sound source from the raw audio signal. Here, we propose an end-to-end deep learning model, called DOANet, for SSL. Though we improve the baseline method by reducing ego-noise using speed correlated harmonics cancellation (SCHC) technique, our main focus is to utilize deep learning techniques to solve this challenging problem. We use angular spectrum-based TDOA (time difference of arrival) estimation methods such as generalized cross-correlation phase-transform (GCC-PHAT), minimum-variance-distortion-less-response (MVDR) as baseline, which are state-of-the-art techniques for SSL. In this paper, we present our work on drone-embedded SSL using recordings from an 8-channel cube-shaped microphone array embedded in an unmanned aerial vehicle (UAV). However, the problem gets complicated by severe drone ego-noise that may result in negative signal-to-noise ratios in the recorded microphone signals. Drone-embedded sound source localization (SSL) has interesting application perspective in challenging search and rescue scenarios due to bad lighting conditions or occlusions.
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