Outdoor Sound Source Detection Using a Quadcopter with Microphone Array

This paper presents a novel method for sound objects detection and localization. The proposed approach in the problem of recognition and localization of sound sources is based on the combination of decision trees and microphone arrays. More specific the novelty of the proposed approach relies on the use of decision tree classifiers for the detection of sound objects using microphone array filtered data. That is, the array performs a separation of the investigated sound from background noise, using beam-forming techniques such as minimum variance beam-former. Then the filtered signal which is enhanced from background noise and reverberation is processed by the classifier. Furthermore the microphone data are processed in order to find the direction of arrival using microphone array signal processing algorithms such as the MUSIC algorithm. So the microphone array data are used both for localization and signal enhancement. The filtered signal is then processed by decision trees classifiers.

2018 26th European Signal Processing Conference (EUSIPCO), 2018

The problem of acoustic source localization and signal enhancement through beamforming techniques is especially challenging when the acoustic recording is performed using microphone arrays installed on multirotor unmanned aerial vehicles (UAVs), The principal source of disturbances in this class of devices is given by the high frequency, narrowband noise originated by the electrical engines, and by the broadband aerodynamic noise induced by the propellers. A solution to this problem is investigated, which employs an efficient beamforming-based spectral distance response algorithm for both localization and enhancement of the source. The beamforming relies on a diagonal unloading (DU) transformation. The proposed algorithm was tested on a multirotor micro aerial vehicle (MAV) equipped with a compact uniform linear array (ULA) of four microphones, perpendicular to the rear-front axis of the drone. The array is positioned slightly above the plane of propellers, and centered with respect...

A possible algorithm for sound source localization in a security system that is based on beamforming of a microphone array is described in this paper. It is shown that the adaptive beamforming algorithm, Minimum Variance Distortionless Response (MVDR), can be a part of the signal processing implemented in a security system. This signal processing includes the following stages: sound source localization, signal parameter estimation, signal priority analysis and, finally, control of protective and warning means (for example, video camera). The adaptive beamforming method MVDR is used for estimating the direction-of arrival (DOA) of signals generated by different sound sources, which arrive at the microphone array from different directions of the protected area. The scenario, in which four sound sources located at different points of the protected area generate different sound signals (warning, alarm, emergency and natural noise), is simulated in order to verify the algorithm for DOA estimation. The simulation results show that an adaptive microphone array can be successfully used for accurate localization of all sound sources in the observation area. The parallel version of the described algorithm is tested in Blue Gene environment using the interface MPI.

In recent years, there has been growing interest in the development of noise prediction and reduction techniques. The ability to localise problematic sound sources and determine their contribution to the overall perceived sound provides an excellent first step towards reducing noise. Several well-known methods can be applied in order to achieve a detailed acoustic assessment using microphone phased arrays. However, pressure-based solutions encounter difficulties assessing low frequency problems and their performance is often limited by spatial coherence losses. Alternatively, the use of acoustic vector sensor (AVS) offers several advantages in such conditions due to their vector nature. Each AVS is comprised of a pressure microphone and three orthogonal particle velocity sensors, allowing for the sound direction of arrival to be determined at any frequency within the audible frequency range. Sound localisation techniques using AVS are evaluated in this paper, comparing the character...

1999

To find the position of an acoustic source in a room, the relative delay between two ͑or more͒ microphone signals for the direct sound must be determined. The generalized cross-correlation method is the most popular technique to do so and is well explained in a landmark paper by Knapp and Carter. In this paper, a new approach is proposed that is based on eigenvalue decomposition. Indeed, the eigenvector corresponding to the minimum eigenvalue of the covariance matrix of the microphone signals contains the impulse responses between the source and the microphone signals ͑and therefore all the information we need for time delay estimation͒. In experiments, the proposed algorithm performs well and is very accurate.

Proceedings of the 5th International Conference of Control, Dynamic Systems, and Robotics (CDSR'18), 2018

This paper presents a novel three-dimensional (3D) sound source localization (SSL) technique based on only Interaural Time Difference (ITD) signals, acquired by a self-rotational two-microphone array on an Unmanned Ground Vehicle. Both the azimuth and elevation angles of a stationary sound source are identified using the phase angle and amplitude of the acquired ITD signal. An SSL algorithm based on an extended Kalman filter (EKF) is developed. The observability analysis reveals the singularity of the state when the sound source is placed above the microphone array. A means of detecting this singularity is then proposed and incorporated into the proposed SSL algorithm. The proposed technique is tested in both a simulated environment and two hardware platforms, i.e., a KEMAR dummy binaural head and a robotic platform. All results show the fast and accurate convergence of estimates.

IEEE Access

This work was supported in part by the European Union from the European Regional Development Fund (ERDF) under Project KK.01.2.1.01.0103 4D Acoustical Camera (in Croatian: 4D Akustička kamera).

The Journal of the Acoustical Society of America, 2008

Direction finding of more sources than sensors is appealing in situations with small sensor arrays. Potential applications include surveillance, teleconferencing, and auditory scene analysis for hearing aids. A new technique for time-frequency-sparse sources, such as speech and vehicle sounds, uses a coherence test to identify low-rank time-frequency bins. These low-rank bins are processed in one of two ways: ͑1͒ narrowband spatial spectrum estimation at each bin followed by summation of directional spectra across time and frequency or ͑2͒ clustering low-rank covariance matrices, averaging covariance matrices within clusters, and narrowband spatial spectrum estimation of each cluster. Experimental results with omnidirectional microphones and colocated directional microphones demonstrate the algorithm's ability to localize 3-5 simultaneous speech sources over 4 s with 2-3 microphones to less than 1 degree of error, and the ability to localize simultaneously two moving military vehicles and small arms gunfire.

EURASIP Journal on Advances in Signal Processing, 2010

We conduct an objective analysis on musical noise generated by two methods of integrating microphone array signal processing and spectral subtraction. To obtain better noise reduction, methods of integrating microphone array signal processing and nonlinear signal processing have been researched. However, nonlinear signal processing often generates musical noise. Since such musical noise causes discomfort to users, it is desirable that musical noise is mitigated. Moreover, it has been recently reported that higherorder statistics are strongly related to the amount of musical noise generated. This implies that it is possible to optimize the integration method from the viewpoint of not only noise reduction performance but also the amount of musical noise generated. Thus, we analyze the simplest methods of integration, that is, the delay-and-sum beamformer and spectral subtraction, and fully clarify the features of musical noise generated by each method. As a result, it is clarified that a specific structure of integration is preferable from the viewpoint of the amount of generated musical noise. The validity of the analysis is shown via a computer simulation and a subjective evaluation.

2018

Preface This report has been carried out during the Spring of 2018 as an Acoustics and Audio Technology Master's Thesis at Aalborg University by group: 10GR1062. The group would like to thank Søren Krarup Olesen (Associate Professor, AAU) and Karim Haddad (Research engineer, Brüel & Kjaer) for their supervision throughout the project. The figures in the report are produced by the group unless a source is specified.

The Journal of the Acoustical Society of America, 2008

By using all time shifts between the arrivals of the acoustic signal at the four microphones in a spatial configuration the source direction is obtained. The used correlation technique reduces significantly noise from other sources and the error in the estimated noise from the tracked aircraft. By taking into account a motion model, the history of the motion and the observed acoustic data a very robust sound monitor system is obtained. The sensitivity for other sources, like wind and rain noise, is greatly reduced -theoretically almost unlimited -in comparison with classical monitor systems. Four aircraft can be tracked simultaneously. Another system capability is the estimation of the true flight path with a system error depending on the system configuration and the environmental conditions. Using this path information the soundscape can be calculated by inter-and extrapolation. The design goal of the system is truly met!

2009 Fifth International Conference on Information Assurance and Security, 2009

In this paper, we propose a system that can detect unusual sounds and directions by observing sound environment with microphone arrays. One of the attractive features of the system is to detect the unusual information through daily environmental sound measurement. Therefore the system does not require such troublesome processes that the detected sounds must be predefined, the predefined sounds must be collected, and using the collected sounds, their features must be modeled, where the conventional systems have such troublesomeness. Moreover, unlike conventional systems using video cameras, our system is not limited by video camera angles. A simple experimental result shows the validity of the proposed system.

Journal of Intelligent & Robotic Systems

This work presents a novel technique that performs both orientation and distance localization of a sound source in a three-dimensional (3D) space using only the interaural time difference (ITD) cue, generated by a newly-developed self-rotational bi-microphone robotic platform. The system dynamics is established in the spherical coordinate frame using a state-space model. The observability analysis of the state-space model shows that the system is unobservable when the sound source is placed with elevation angles of 90 and 0 degree. The proposed method utilizes the difference between the azimuth estimates resulting from respectively the 3D and the two-dimensional models to check the zero-degreeelevation condition and further estimates the elevation angle using a polynomial curve fitting approach. Also, the proposed method is capable of detecting a 90-degree elevation by extracting the zero-ITD signal 'buried' in noise. Additionally, a distance localization is performed by first rotating the microphone array to face toward the sound source and then shifting the microphone perpendicular to the source-robot vector by a predefined distance of a fixed number of steps. The integrated rotational and translational motions of the microphone array provide a complete orientation and distance localization using only the ITD cue. A novel robotic platform using a self-rotational bi-microphone array was also developed for unmanned ground robots performing sound source localization. The proposed technique was first tested in simulation and was then verified on the newly-developed robotic platform. Experimental data collected by the microphones installed on a KEMAR Deepak Gala,

Circuits, Systems, and Signal Processing, 2015

Sound event detection and localization (SDL) is helpful for extracting information about the position of sound sources in real time using a microphone array. This paper develops an SDL system for intelligent outdoor security cameras, so that it can listen and react to the surrounding acoustic events. In outdoor environments, this task is challenging due to high-energy and non-stationary noises such as wind noise. This paper proposes new methods for improving both detection and localization, based on a new feature, namely cross-channel power difference (XPD). The XPD is estimated from the difference of short-term power between microphones that are sensitive to wind noise. In the detection step, a time frame with high XPD is regarded as wind noise, and periods of wind, which cause false alarms, are removed from the localization step. Furthermore, the XPD is used to create a binary mask for separating the wind noise and other sound sources, thus preventing the wind noise from degrading the localization of target sounds. The proposed system is evaluated using a hardware prototype that consists of four microphones attached to the housing of a pan-tiltzoom camera. Through real environmental experiments, we indicate that the proposed methods outperform other state-of-the-art SDL methods in windy conditions.

Robotics and Autonomous Systems, 2019

Human-robot interaction in natural settings requires filtering out the different sources of sounds from the environment. Such ability usually involves the use of microphone arrays to localize, track and separate sound sources online. Multimicrophone signal processing techniques can improve robustness to noise but the processing cost increases with the number of microphones used, limiting response time and widespread use on different types of mobile robots. Since sound source localization methods are the most expensive in terms of computing resources as they involve scanning a large 3D space, minimizing the amount of computations required would facilitate their implementation and use on robots. The robot's shape also brings constraints on the microphone array geometry and configurations. In addition, sound source localization methods usually return noisy features that need to be smoothed and filtered by tracking the sound sources. This paper presents a novel sound source localization method, called SRP-PHAT-HSDA, that scans space with coarse and fine resolution grids to reduce the number of memory lookups. A microphone directivity model is used to reduce the number of directions to scan and ignore non significant pairs of microphones. A configuration method is also introduced to automatically set parameters that are normally empirically tuned according to the shape of the microphone array. For sound source tracking, this paper presents a modified 3D Kalman (M3K) method capable of simultaneously tracking in 3D the directions of sound sources. Using a 16-microphone array and low cost hardware, results show that SRP-PHAT-HSDA and M3K perform at least as well as other sound source localization and tracking methods while using up to 4 and 30 times less computing resources respectively.