3008:) is fundamentally limited by the physical size of the array. If the array is too small, then the microphones are spaced too closely together so that they all record essentially the same sound (with ITF near zero), making it extremely difficult to estimate the orientation. Thus, it is not uncommon for microphone arrays to range from tens of centimeters in length (for desktop applications) to many tens of meters in length (for underwater localization). However, microphone arrays of this size then become impractical to use on small robots. even for large robots, such microphone arrays can be cumbersome to mount and to maneuver. In contrast, the ability to localize sound using a single microphone (which can be made extremely small) holds the potential of significantly more compact, as well as lower cost and power, devices for localization.
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of the sound source, and the amplitude of the ICTD signal can be represented as a function of the elevation angle of the sound source and the distance between the two microphones. In the case of multiple sources, the ICTD signal has data points forming multiple discontinuous sinusoidal waveforms. Machine learning techniques such as Random sample consensus (RANSAC) and
Density-based spatial clustering of applications with noise (DBSCAN) can be applied to identify phase shifts (mapping to azimuths) and amplitudes (mapping to elevations) of each discontinuous sinusoidal waveform in the ICTD signal.
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3024:). First, compute HRTFs for the 3D sound localization, by formulating two equations; one represents the signal of a given sound source and the other indicates the signal output from the robot head microphones for the sound transferred from the source. Monaural input data are processed by these HRTFs, and the results are output from stereo headphones. The disadvantage of this method is that many parametric operations are necessary for the whole set of filters to realize the 3D sound localization, resulting in high computational complexity.
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in a direction-dependent way). The approach models the typical distribution of natural and artificial sounds, as well as the direction-dependent changes to sounds induced by the pinna. The experimental results also show that the algorithm is able to fairly accurately localize a wide range of sounds, such as human speech, dog barking, waterfall, thunder, and so on. In contrast to microphone arrays, this approach also offers the potential of significantly more compact, as well as lower cost and power, devices for sound localization.
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binaural hearing model. The HRTF can be derived based on various cues for localization. Sound localization with HRTF is filtering the input signal with a filter which is designed based on the HRTF. Instead of using the neural networks, a head-related transfer function is used and the localization is based on a simple correlation approach.
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The measurement procedure involves manually moving the AVS sensor around the sound source while a stereo camera is used to extract the instantaneous position of the sensor in three-dimensional space. The recorded signals are then split into multiple segments and assigned to a set of positions using a
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Scan-based techniques are a powerful tool for localizing and visualizing time-stationary sound sources, as they only require the use of a single sensor and a position tracking system. One popular method for achieving this is through the use of an
Acoustic Vector Sensor (AVS), also known as a 3D Sound
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As shown in the figure, the implementation procedure of this realtime algorithm is divided into three phases, (i) Frequency
Division, (ii) Sound Localization, and (iii) Mixing. In the case of 3D sound localization for a monaural sound source, the audio input data are divided into two: left and right
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The rotation of the two-microphone array (also referred as bi-microphone array ) leads to a sinusoidal inter-channel time difference (ICTD) signal for a stationary sound source present in a 3D environment. The phase shift of the resulting sinusoidal signal can be directly mapped to the azimuth angle
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Monaural localization is made possible by the structure of the pinna (outer ear), which modifies the sound in a way that is dependent on its incident angle. A machine learning approach is adapted for monaural localization using only a single microphone and an “artificial pinna” (that distorts sound
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Typically, sound localization is performed by using two (or more) microphones. By using the difference of arrival times of a sound at the two microphones, one can mathematically estimate the direction of the sound source. However, the accuracy with which an array of microphones can localize a sound
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In the real sound localization, the robot head and the torso play a functional role, in addition to the two pinnae. This functions as spatial linear filtering and the filtering is always quantified in terms of Head-Related
Transfer Function (HRTF). HRTF also uses the robot head sensor, which is the
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by rotating the head with a settled white noise sound source and analyzing the spectrum. Experiments show that the system can identify the direction of the source well in a certain range of angle of arrival. It cannot identify the sound coming outside the range due to the collapsed spectrum pattern
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method. The sensor is a robot dummy head with 2 sensor microphones along with the artificial pinna (reflector). The robot head has 2 rotation axes and can rotate horizontally and vertically. The reflector causes the spectrum change into a certain pattern for incoming white noise sound wave and this
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The results of the AVS analysis can be presented over a 3D sketch of the tested object, providing a visual representation of the sound distribution around a 3D mesh of the object or environment. This can be useful for localizing sound sources in a variety of fields, such as architectural acoustics,
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The AVS is one kind of collocated multiple microphone array, it makes use of a multiple microphone array approach for estimating the sound directions by multiple arrays and then finds the locations by using reflection information such as where the direction is detected where different arrays cross.
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The advantage of this method is that it detects the direction of the sound and derives the distance of sound sources. The main drawback of the beamforming approach is the imperfect nature of sound localization accuracy and capability, versus the neural network approach, which uses moving speakers.
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uses sound source localization techniques to identify the location of a target. 3D sound localization is also used for effective human-robot interaction. With the increasing demand for robotic hearing, some applications of 3D sound localization such as human-machine interface, handicapped aid, and
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IID-based or ITD-based sound localization methods have a main problem called Front-back confusion. In this sound localization based on a hierarchical neural network system, to solve this issue, an IID estimation is with ITD estimation. This system was used for broadband sounds and be deployed for
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The first clue our hearing uses is interaural time difference. Sound from a source directly in front of or behind us will arrive simultaneously at both ears. If the source moves to the left or right, our ears pick up the sound from the same source arriving at both ears - but with a certain delay.
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The
Hierarchical Fuzzy Artificial Neural Networks Approach sound localization system was modeled on biologically binaural sound localization. Some primitive animals with two ears and small brains can perceive 3D space and process sounds, although the process is not fully understood. Some animals
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A sound signal is first windowed using a rectangular window, then each resulting segment signal is created as a frame. 4 parallel frames are detected from XYZO array and used for DOA estimation. The 4 frames are split into small blocks with equal size, then the
Hamming window and FFT are used to
422:
2053:
818:{\displaystyle {{R}^{\text{RWPHAT}}}_{i,j}\left(\tau \right)=\sum _{k=0}^{L-1}{\frac {{\zeta }_{i}\left(k\right){X}_{i}\left(k\right){\zeta }_{j}\left(k\right){{X}_{j}}^{*}\left(k\right)}{\left|{X}_{i}\left(k\right)\right|\left|{X}_{j}\left(k\right)\right|}}{e}^{j2\pi k\tau /L}}
2991:(ITD-based) and interaural intensity difference(IID-based) sound localization methods for higher accuracy that is similar to that of humans. Hierarchical Fuzzy Artificial Neural Networks were used with the goal of the same sound localization accuracy as human ears.
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CSP method is also used for the binaural model. The idea is that the angle of arrival can be derived through the time delay of arrival (TDOA) between two microphones, and TDOA can be estimated by finding the maximum coefficients of CSP. CSP coefficients are derived
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Sound reflections always occur in an actual environment and microphone arrays cannot avoid observing those reflections. This multiple array approach was tested using fixed arrays in the ceiling; the performance of the moving scenario still need to be tested.
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This method relates to the technique of Real-Time sound localization utilizing an
Acoustic Vector Sensor (AVS) array, which measures all three components of the acoustic particle velocity, as well as the sound pressure, unlike conventional acoustic
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In order to estimate the location of a source in 3D space, two line sensor arrays can be placed horizontally and vertically. An example is a 2D line array used for underwater source localization. By processing the data from two arrays using the
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spatial discretization algorithm. This allows for the computation of a vector representation of the acoustic variations across the sound field, using combinations of the sound pressure and the three orthogonal acoustic particle velocities.
1592:{\displaystyle dist\left(dir_{1},dir_{2}\right)={\frac {\left({\overrightarrow {v_{1}}}\times {\overrightarrow {v_{2}}}\right)\times {\overrightarrow {p_{1}p_{2}}}}{\left|{\overrightarrow {v_{1}}}\times {\overrightarrow {v_{2}}}\right|}}}
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convert each block from a time domain to a frequency domain. Then the output of this system is represented by a horizontal angle and a vertical angle of the sound sources which is found by the peak in the combined 3D spatial spectrum.
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in order to find the maximum value of the output of a beamformer steered in all possible directions. Using the
Reliability Weighted Phase Transform (RWPHAT) method, The output energy of M-microphone delay-and-sum beamformer is
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30:. The source location is usually determined by the direction of the incoming sound waves (horizontal and vertical angles) and the distance between the source and sensors. It involves the structure arrangement design of the
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The motivation of using this method is that based on previous research. This method is used for multiple sound source tracking and localizing despite soundtracking and localization only apply for a single sound source.
232:
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wide band sound sources simultaneously. Applying an O array can make more available acoustic information, such as amplitude and time difference. Most importantly, XYZO array has a better performance with a tiny size.
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Where r is the distance between array center to source, and AU is angle uncertainly. Measurement is used for judging whether two directions cross at some location or not. Minimum distance between two lines:
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that only utilize the pressure information and delays in the propagating acoustic field. Exploiting this extra information, AVS arrays are able to significantly improve the accuracy of source localization.
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A distinctive feature of this approach is that the audible frequency band is divided into three so that a distinct procedure of 3D sound localization can be exploited for each of the three subbands.
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Liang, Yun; Cui, Zheng; Zhao, Shengkui; Rupnow, Kyle; Zhang, Yihao; Jones, Douglas L.; Chen, Deming (2012). "Real-time implementation and performance optimization of 3D sound localization on GPUs".
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experience difficulty in 3D sound location due to small head size. Additionally, the wavelength of communication sound may be much larger than their head diameter, as is the case with
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Angle uncertainty (AU) will occur when estimating direction, and position uncertainty (PU) will also aggravate with increasing distance between the array and the source. We know that:
2105:
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2536:{\displaystyle csp_{ij}(k)={\text{IFFT}}\left\{{\frac {{\text{FFT}}\cdot {\text{FFT}}^{*}}{\left|{\text{FFT}}\right\vert \cdot \left|{\text{FFT}}\right\vert \quad }}\right\}\quad }
205:
This approach utilizes eight microphones combined with a steered beamformer enhanced by the
Reliability Weighted Phase Transform (RWPHAT). The final results are filtered through a
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is also used for localizing several sound sources and reduce the localization errors. The system is capable of identifying several moving sound source using only two microphones.
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to localize sound, by comparing the information received from each ear in a complex process that involves a significant amount of synthesis. It is difficult to localize using
1273:• Can be used in combination with the Offline Calibration Process to measure and interpolate the impulse response of X, Y, Z and O arrays, to obtain their steering vector.
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Gala, Deepak; Lindsay, Nathan; Sun, Liang (July 2018). "Realtime Active Sound Source
Localization for Unmanned Ground Robots Using a Self-Rotational Bi-Microphone Array".
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The advantages of this array, compared with past microphone array, are that this device has a high performance even if the aperture is small, and it can localize multiple
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noise control, and audio engineering, as it allows for a detailed understanding of the sound distribution and its interactions with the surrounding environment.
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Noriaki, Sakamoto; wataru, Kobayashi; Takao, Onoye; Isao, Shirakawa (2001). "DSP implementation of 3D sound localization algorithm for monaural sound source".
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Valin, J.M.; Michaud, F.; Rouat, Jean (14–19 May 2006). "Robust 3D Localization and Tracking of Sound Sources Using Beamforming and Particle Filtering".
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method, the direction, range and depth of the source can be identified simultaneously. Unlike the binaural hearing model, this method is similar to the
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pattern is used for the cue of the vertical localization. The cue for horizontal localization is ITD. The system makes use of a learning process using
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1267:• Contains three orthogonally placed acoustic particle velocity sensors (shown as X, Y and Z array) and one omnidirectional acoustic microphone (O).
1731:
417:{\displaystyle E=K+2\sum _{{m}_{1}=1}^{M-1}\sum _{{m}_{2}=0}^{{m}_{1}-1}{{R}^{\text{RWPHAT}}}_{i,j}\left({\tau }_{{m}_{1}}-{\tau }_{{m}_{2}}\right)}
1316:
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Keyrouz, Fakheredine; Diepold, Klaus; Keyrouz, Shady (September 2007). "High performance 3D sound localization for surveillance applications".
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Keyrouz, Fakheredine; Diepold, Klaus (May 2008). "A novel biologically inspired neural network solution for robotic 3D sound source sensing".
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Hill, P.A.; Nelson, P.A.; Kirkeby, O.; Hamada, H. (December 2000). "Resolution of front-back confusion in virtual acoustic imaging systems".
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Ishi, C.T.; Even, J.; Hagita, N. (November 2013). "Using multiple microphone arrays and reflections for 3D localization of sound sources".
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of the reflector. Binaural hearing use only 2 microphones and is capable of concentrating on one source among multiple sources of noises.
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2048:{\displaystyle \mathrm {POS} _{source}={\frac {\left(\mathrm {POS} _{1}\times w_{1}+\mathrm {POS} _{2}\times w_{2}\right)}{w_{1}+w_{2}}}}
3709:
Multi-Sound-Source Localization Using Machine Learning for Small Autonomous Unmanned Vehicles with a Self-Rotating Bi-Microphone Array
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Ephraim, Y.; Malah, D. (Dec 1984). "Speech enhancement using a minimum-mean square error short-time spectral amplitude estimator".
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Two lines are judged as crossing. When two lines are crossing, we can compute the sound source location using the following:
1000:{\displaystyle {{\zeta }^{n}}_{i}\left(k\right)={\frac {{{\xi }^{n}}_{i}\left(k\right)}{{{\xi }^{n}}_{i}\left(k\right)+1}}}
118:
Localization cues are features that help localize sound. Cues for sound localization include binaural and monoaural cues.
105:
Applications of sound source localization include sound source separation, sound source tracking, and speech enhancement.
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is the delay of arrival for that microphone. The more specific procedure of this method is proposed by Valin and Michaud
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Binaural cues are generated by the difference in hearing between the left and right ears. These differences include the
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A DSP-based implementation of a realtime 3D sound localization approach with the use of an embedded DSP can reduce the
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Based on previous binaural sound localization methods, a hierarchical fuzzy artificial neural network system combines
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Three-dimensional sound source localization for unmanned ground vehicles with a self-rotational two-microphone array
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133:(ITD) and the interaural intensity difference (IID). Binaural cues are used mostly for horizontal localization.
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Nakashima, H.; Mukai, T. (2005). "3D Sound Source Localization System Based on Learning of Binaural Hearing".
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81:. Existing real-time passive sound localization systems are mainly based on the time-difference-of-arrival (
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3291:"Direct acoustic vector field mapping: new scanning tools for measuring 3D sound intensity in 3D space"
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ICECS 2001. 8th IEEE International Conference on Electronics, Circuits and Systems (Cat. No.01EX483)
3431:"Calibration Proposal for New Antenna Array Architectures and Technologies for Space Communications"
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Another way of saying it could be, that the two ears pick up different phases of the same signal.
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27:
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Salas Natera, M.A.; Martinez Rodriguez-Osorio, R.; de Haro Ariet, L.; Sierra Perez, M. (2012).
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Pérez Cabo, Daniel; de Bree, Hans Elias; Fernandez Comesaña, Daniel; Sobreira Seoane, Manuel.
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Evaluation of Two-Channel-Based Sound Source Localization using 3D Moving Sound Creation Tool
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reflect the reliability of each frequency component, and defined as the Wiener Filter gain
519:{\displaystyle {{R}^{\text{RWPHAT}}}_{i,j}\left({\tau }_{{m}_{1}}-{\tau }_{{m}_{2}}\right)}
8:
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2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings
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3412:"Real life harmonic source localization using a network of acoustic vector sensors"
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is the position where each direction intersect the line with minimum distance, and
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channels and the audio input data in time series are processed one after another.
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Saxena, A.; Ng, A.Y. (2009). "Learning sound location from a single microphone".
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2006 IEEE International Symposium on Signal Processing and Information Technology
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CPS method does not require the system impulse response data that HRTF needs. An
2286:
206:
3602:; Messer,H. (Jan 1996). "Three-Dimensional Source Localization in a Waveguide".
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2832:{\displaystyle {\theta }=\cos ^{-1}{\frac {v\cdot \tau }{d_{\max }\cdot F_{s}}}}
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1885:{\displaystyle dist(dir_{1},dir_{2})<abs(PU_{1}(r_{1}))+abs(PU_{2}(r_{2}))}
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Acoustic technology to locate the source of a sound in three-dimensional space
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Fernandez Comesana, D.; Steltenpool, S.; Korbasiewicz, M.; Tijs, E. (2015).
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1376:{\displaystyle PU\left(r\right)={\frac {\pm AU}{360}}\times 2\pi \times r}
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There are many different methods of 3D sound localization. For instance:
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2013 IEEE/RSJ International Conference on Intelligent Robots and Systems
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2007 IEEE Conference on Advanced Video and Signal Based Surveillance
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2005 IEEE International Conference on Systems, Man and Cybernetics
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A general way to implement 3d sound localization is to use the
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is the distance with maximum time delay between 2 microphones.
2745:{\displaystyle {\tau }=\operatorname {arg} \max\{csp_{ij}(k)\}}
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42:
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Hyun-Don Kim; Komatani, K.; Ogata, T.; Okuno,H.G. (Jan 2008).
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Real-time methods using an Acoustic Vector Sensor (AVS) array
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2009 IEEE International Conference on Robotics and Automation
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IEEE Transactions on Acoustics, Speech, and Signal Processing
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method. The method can be used to localize a distant source.
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26:
technology that is used to locate the source of a sound in a
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Gala, Deepak; Lindsay, Nathan; Sun, Liang (June 2018).
3518:"Reducing noise emissions from Lontra's LP2 compressor"
3268:
Automation and Test in Europe Conference and Exhibition
3182:(Eighth ed.). Cengage Learning. pp. 293–297.
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1240:
3837:
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Gala, Deepak; Lindsay, Nathan; Sun, Liang (Oct 2021).
3533:"An Enhanced Binaural 3D Sound Localization Algorithm"
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Hierarchical Fuzzy Artificial Neural Networks Approach
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2256:Intensity Probe, in combination with a 3D tracker.
427:Where E indicates the energy, and K is a constant,
209:that tracks sources and prevents false directions.
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2239:from the array to the line with minimum distance.
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172:Different techniques for optimal results, such as
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2975:Structure of How to derive the Azimuth Estimation
2169:is the weighted factors. As the weighting factor
1126:, computed using the decision-directed approach.
530:defined by Reliability Weighted Phase Transform:
221:To maximize the output energy of a delay-and-sum
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2808:
2705:
1695:are vectors parallel to detected direction, and
870:{\displaystyle {{\zeta }^{n}}_{i}\left(k\right)}
126:and are generally used in vertical localization.
2999:3D sound localization for monaural sound source
1306:Learning how to apply Multiple Microphone Array
137:
3743:
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3435:IEEE Antennas and Wireless Propagation Letters
3224:
1049:{\displaystyle {{\xi }^{n}}_{i}\left(k\right)}
200:
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65:Sound localization technology is used in some
3949:
3531:Keyrouz, Fakheredine; Diepold, Klaus (2006).
3051:
3011:
1270:• Commonly used both in air and underwater.
165:Different types of sensor structure, such as
89:, and are not practical in noisy conditions.
3789:Journal of the Acoustical Society of America
3713:Journal of Intelligent & Robotic Systems
3639:Journal of Intelligent & Robotic Systems
3315:
2739:
2708:
2107:is the estimation of sound source position,
1263:Picture of a 3D Acoustic Vector Sensor (air)
3036:dsp implementation of 3d sound localization
3028:DSP implementation of 3D sound localization
2251:3D sound localization of a large compressor
1297:Motivation of the Advanced Microphone array
110:military applications, are being explored.
85:) approach, limiting sound localization to
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2276:Structure of the binaural robot dummy head
3924:3-D Localization of Virtual Sound Sources
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2310:Cross-power spectrum phase (CSP) analysis
193:Offline methods (according to timeliness)
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96:
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2100:{\displaystyle \mathrm {POS} _{source}}
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3604:IEEE Transactions on Signal Processing
3087:Perceptual-based 3D sound localization
2242:
3986:Computational auditory scene analysis
3963:
3937:
2941:Demonstration of 2d line sensor array
2294:Head-related Transfer Function (HRTF)
2618:are signals entering the microphone
2268:Learning method for binaural hearing
1241:Collocated Microphone Array Approach
3842:. Vol. 2. pp. 1061–1064.
3229:. Vol. 4. pp. 3534–3539.
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217:Beamformer-based Sound Localization
122:Monoaural cues can be obtained via
13:
2927:expectation-maximization algorithm
2135:{\displaystyle \mathrm {POS} _{n}}
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3178:Goldstein, E.Bruce (2009-02-13).
2958:Self-rotating Bi-Microphone Array
1226:{\displaystyle {\tau }_{{m}_{n}}}
1056:is an estimate of a prior SNR at
3929:3-D Acoustic Vector Sensor (air)
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2862:is the sound propagation speed,
169:and binaural hearing robot head.
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2280:Binaural hearing learning is a
101:Collecting Multibeam Sonar Data
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3109:
3082:Head-related transfer function
3022:Head-related transfer function
2889:is the sampling frequency and
2736:
2730:
2605:
2599:
2569:
2563:
2516:
2513:
2507:
2494:
2473:
2470:
2464:
2451:
2430:
2426:
2420:
2407:
2396:
2393:
2387:
2374:
2351:
2345:
2304:Head-related transfer function
1879:
1876:
1863:
1847:
1832:
1829:
1816:
1800:
1785:
1747:
182:Multiple signal classification
1:
3715:. Vol. 103, no. 3.
3161:"Noise Source Identification"
3102:
60:
3072:Acoustic source localization
1722:are the position of arrays.
138:How does one localize sound?
7:
3991:Music information retrieval
3371:10.1109/ICASSP.2006.1661100
3355:. Vol. 4. p. IV.
3097:Vertical sound localization
3060:
2680:) then can be estimated by:
201:Steered Beamformer Approach
114:Cues for sound localization
10:
4075:
3889:10.1109/ROBOT.2009.5152861
3731:10.1007/s10846-021-01481-4
3545:10.1109/ISSPIT.2006.270883
3330:10.1109/TASSP.1984.1164453
3235:10.1109/ICSMC.2005.1571695
3052:Single microphone approach
3012:Conventional HRTF approach
3006:Interaural time difference
2995:non-stationary scenarios.
2989:interaural time difference
2755:Sound source direction is
1086:microphone, at time frame
156:
148:Interaural time difference
141:
131:interaural time difference
3971:
3848:10.1109/ICECS.2001.957673
3758:10.1007/s00500-007-0249-9
3661:10.1007/s10846-018-0908-3
3486:10.1109/IROS.2013.6696919
3455:10.1109/LAWP.2012.2215952
3128:10.1109/AVSS.2007.4425372
1156:{\displaystyle x_{m_{n}}}
196:Microphone Array Approach
3295:Proceedings of Euronoise
3180:Sensation and Perception
3042:computational complexity
2611:{\displaystyle s_{j}(n)}
2575:{\displaystyle s_{i}(n)}
1188:{\displaystyle {m}^{th}}
526:is the microphone pairs
4033:3D sound reconstruction
3067:3D sound reconstruction
2915:{\displaystyle d_{max}}
1661:{\displaystyle dir_{2}}
1628:{\displaystyle dir_{1}}
53:hearing, especially in
45:(including humans) use
28:three-dimensional space
3883:. pp. 1737–1742.
3037:
2976:
2942:
2916:
2883:
2856:
2833:
2746:
2674:
2660:Time delay of arrival(
2652:
2632:
2612:
2576:
2537:
2277:
2252:
2233:
2213:
2190:
2163:
2136:
2101:
2049:
1886:
1716:
1689:
1662:
1629:
1593:
1377:
1264:
1227:
1189:
1157:
1120:
1100:
1080:
1079:{\displaystyle i^{th}}
1050:
1001:
871:
819:
607:
520:
418:
331:
286:
102:
4028:3D sound localization
3035:
2974:
2940:
2917:
2884:
2882:{\displaystyle F_{s}}
2857:
2834:
2747:
2675:
2673:{\displaystyle \tau }
2653:
2633:
2613:
2577:
2538:
2275:
2250:
2234:
2214:
2191:
2189:{\displaystyle w_{n}}
2164:
2162:{\displaystyle w_{n}}
2137:
2102:
2050:
1887:
1717:
1715:{\displaystyle p_{i}}
1690:
1688:{\displaystyle v_{i}}
1663:
1630:
1594:
1378:
1262:
1255:Acoustic Vector Array
1228:
1190:
1158:
1121:
1101:
1081:
1051:
1002:
872:
820:
581:
521:
419:
287:
251:
100:
87:two-dimensional space
20:3D sound localization
3976:Acoustic fingerprint
3586:10.1109/ICKS.2008.25
3539:. pp. 662–665.
3480:. pp. 3937–42.
2933:2D sensor line array
2893:
2866:
2846:
2762:
2688:
2664:
2642:
2622:
2586:
2550:
2323:
2223:
2200:
2196:, we determined use
2173:
2146:
2111:
2061:
1902:
1732:
1699:
1672:
1668:are two directions,
1639:
1606:
1394:
1317:
1199:
1167:
1133:
1110:
1090:
1060:
1011:
881:
832:
828:the weighted factor
537:
431:
233:
4011:Speaker recognition
3801:2000ASAJ..108.2901H
3693:10.11159/cdsr18.104
3616:1996ITSP...44....1T
3447:2012IAWPL..11.1129S
3167:. Brüel & Kjær.
2243:Scanning Techniques
1163:is the signal from
190:Scanning techniques
77:, surveillance and
4016:Speech recognition
3122:. pp. 563–6.
3092:Sound localization
3077:Binaural recording
3038:
2977:
2948:maximum likelihood
2943:
2912:
2879:
2852:
2829:
2742:
2670:
2648:
2628:
2608:
2572:
2533:
2278:
2253:
2229:
2212:{\displaystyle PU}
2209:
2186:
2159:
2132:
2097:
2045:
1882:
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1685:
1658:
1625:
1589:
1373:
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1223:
1185:
1153:
1116:
1096:
1076:
1046:
997:
867:
815:
516:
414:
178:maximum likelihood
144:Sound localization
103:
4041:
4040:
4023:Sound recognition
4001:Speech processing
3965:Computer audition
3898:978-1-4244-2788-8
3857:978-0-7803-7057-9
3809:10.1121/1.1323235
3624:10.1109/78.482007
3495:978-1-4673-6358-7
3380:978-1-4244-0469-8
3205:"Listening in 3D"
3189:978-0-495-60149-4
3137:978-1-4244-1695-0
2952:spectral analysis
2855:{\displaystyle v}
2827:
2651:{\displaystyle j}
2631:{\displaystyle i}
2526:
2492:
2449:
2405:
2372:
2360:
2232:{\displaystyle r}
2043:
1587:
1580:
1560:
1537:
1500:
1480:
1356:
1119:{\displaystyle k}
1099:{\displaystyle n}
995:
781:
551:
528:cross-correlation
445:
343:
124:spectral analysis
36:signal processing
4066:
4006:Speech analytics
3958:
3951:
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3307:
3306:
3286:
3280:
3279:
3263:
3257:
3256:
3222:
3213:
3212:
3209:Brüel & Kjær
3200:
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2017:
2013:
2012:
2011:
1999:
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1195:microphone and
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167:microphone array
73:fields, such as
47:binaural hearing
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2287:neural networks
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3996:Semantic audio
3993:
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3918:External links
3916:
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3897:
3871:
3856:
3830:
3795:(6): 2901–10.
3779:
3746:Soft Computing
3736:
3699:
3674:
3645:(3): 935–954.
3629:
3591:
3580:. ICERI 2008.
3568:
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3523:
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3494:
3468:
3421:
3416:EuroNoise 2015
3402:
3379:
3343:
3324:(6): 1109–21.
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2461:
2457:
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2432:
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2129:
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2079:
2074:
2071:
2068:
2056:
2055:
2039:
2035:
2031:
2026:
2022:
2016:
2010:
2006:
2002:
1997:
1992:
1989:
1986:
1981:
1976:
1972:
1968:
1963:
1958:
1955:
1952:
1946:
1940:
1935:
1932:
1929:
1926:
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1920:
1915:
1912:
1909:
1893:
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1399:
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1363:
1360:
1355:
1351:
1348:
1345:
1339:
1335:
1332:
1329:
1325:
1322:
1307:
1304:
1298:
1295:
1286:high frequency
1256:
1253:
1242:
1239:
1218:
1213:
1206:
1182:
1179:
1174:
1148:
1144:
1139:
1115:
1095:
1073:
1070:
1066:
1044:
1041:
1038:
1032:
1025:
1020:
993:
990:
986:
983:
980:
974:
967:
962:
952:
949:
946:
940:
933:
928:
918:
914:
911:
908:
902:
895:
890:
865:
862:
859:
853:
846:
841:
826:
825:
812:
808:
804:
801:
798:
795:
792:
787:
778:
773:
770:
767:
761:
756:
750:
745:
740:
737:
734:
728:
723:
717:
710:
707:
704:
698:
691:
686:
678:
675:
672:
666:
661:
655:
652:
649:
643:
638:
632:
629:
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620:
615:
605:
602:
599:
594:
591:
588:
584:
580:
576:
573:
570:
564:
561:
558:
546:
514:
506:
501:
494:
489:
482:
477:
470:
464:
458:
455:
452:
440:
425:
424:
412:
404:
399:
392:
387:
380:
375:
368:
362:
356:
353:
350:
338:
329:
326:
321:
316:
309:
306:
301:
296:
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284:
281:
278:
273:
270:
265:
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254:
250:
247:
244:
241:
238:
218:
215:
202:
199:
198:
197:
194:
191:
188:
185:
174:neural network
170:
158:
155:
139:
136:
135:
134:
127:
115:
112:
94:
91:
62:
59:
15:
9:
6:
4:
3:
2:
4071:
4060:
4057:
4055:
4052:
4051:
4049:
4034:
4031:
4029:
4026:
4024:
4021:
4017:
4014:
4012:
4009:
4007:
4004:
4003:
4002:
3999:
3997:
3994:
3992:
3989:
3987:
3984:
3982:
3979:
3977:
3974:
3973:
3970:
3966:
3959:
3954:
3952:
3947:
3945:
3940:
3939:
3936:
3930:
3927:
3925:
3922:
3921:
3908:
3904:
3900:
3894:
3890:
3886:
3882:
3875:
3867:
3863:
3859:
3853:
3849:
3845:
3841:
3834:
3826:
3822:
3818:
3814:
3810:
3806:
3802:
3798:
3794:
3790:
3783:
3775:
3771:
3767:
3763:
3759:
3755:
3751:
3747:
3740:
3732:
3728:
3723:
3718:
3714:
3710:
3703:
3694:
3689:
3686:. CDSR 2018.
3685:
3678:
3670:
3666:
3662:
3658:
3653:
3648:
3644:
3640:
3633:
3625:
3621:
3617:
3613:
3609:
3605:
3601:
3595:
3587:
3583:
3579:
3572:
3564:
3560:
3556:
3554:0-7803-9754-1
3550:
3546:
3542:
3538:
3534:
3527:
3519:
3513:
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3501:
3497:
3491:
3487:
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3440:
3436:
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3386:
3382:
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3372:
3368:
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3331:
3327:
3323:
3319:
3312:
3304:
3300:
3296:
3292:
3285:
3277:
3273:
3269:
3262:
3254:
3250:
3246:
3244:0-7803-9298-1
3240:
3236:
3232:
3228:
3221:
3219:
3210:
3206:
3203:Kjær, Brüel.
3199:
3191:
3185:
3181:
3174:
3166:
3162:
3159:Kjær, Brüel.
3155:
3147:
3143:
3139:
3133:
3129:
3125:
3121:
3120:
3112:
3108:
3098:
3095:
3093:
3090:
3088:
3085:
3083:
3080:
3078:
3075:
3073:
3070:
3068:
3065:
3064:
3058:
3049:
3046:
3043:
3034:
3025:
3023:
3019:
3009:
3007:
2996:
2992:
2990:
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2964:
2955:
2953:
2949:
2939:
2930:
2928:
2923:
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2904:
2901:
2897:
2874:
2870:
2849:
2821:
2817:
2813:
2804:
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2758:
2757:
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2733:
2725:
2722:
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2714:
2711:
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2699:
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2590:
2566:
2558:
2554:
2528:
2520:
2510:
2502:
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2481:
2477:
2467:
2459:
2455:
2442:
2434:
2423:
2415:
2411:
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2378:
2363:
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2317:
2307:
2305:
2300:
2291:
2288:
2283:
2274:
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2261:
2257:
2249:
2240:
2226:
2206:
2203:
2181:
2177:
2154:
2150:
2127:
2092:
2089:
2086:
2083:
2080:
2077:
2037:
2033:
2029:
2024:
2020:
2014:
2008:
2004:
2000:
1995:
1979:
1974:
1970:
1966:
1961:
1944:
1938:
1933:
1930:
1927:
1924:
1921:
1918:
1898:
1897:
1896:
1871:
1867:
1858:
1854:
1850:
1844:
1841:
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1811:
1807:
1803:
1797:
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1776:
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1769:
1766:
1761:
1757:
1753:
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1744:
1741:
1738:
1735:
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1723:
1707:
1703:
1680:
1676:
1653:
1649:
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1609:
1583:
1577:
1572:
1568:
1562:
1557:
1552:
1548:
1541:
1534:
1528:
1524:
1518:
1514:
1507:
1503:
1497:
1492:
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1477:
1472:
1468:
1461:
1454:
1450:
1444:
1440:
1436:
1433:
1430:
1425:
1421:
1417:
1414:
1410:
1406:
1403:
1400:
1397:
1390:
1389:
1388:
1370:
1367:
1364:
1361:
1358:
1353:
1349:
1346:
1343:
1337:
1333:
1330:
1327:
1323:
1320:
1313:
1312:
1311:
1303:
1294:
1290:
1287:
1283:
1282:low frequency
1278:
1274:
1271:
1268:
1261:
1252:
1249:
1248:sensor arrays
1238:
1234:
1216:
1211:
1204:
1180:
1177:
1172:
1146:
1142:
1137:
1127:
1113:
1093:
1071:
1068:
1064:
1042:
1039:
1036:
1030:
1023:
1018:
991:
988:
984:
981:
978:
972:
965:
960:
950:
947:
944:
938:
931:
926:
916:
912:
909:
906:
900:
893:
888:
863:
860:
857:
851:
844:
839:
810:
806:
802:
799:
796:
793:
790:
785:
776:
771:
768:
765:
759:
754:
748:
743:
738:
735:
732:
726:
721:
715:
708:
705:
702:
696:
689:
684:
676:
673:
670:
664:
659:
653:
650:
647:
641:
636:
630:
627:
624:
618:
613:
603:
600:
597:
592:
589:
586:
582:
578:
574:
571:
568:
562:
559:
556:
544:
533:
532:
531:
529:
512:
504:
499:
492:
487:
480:
475:
468:
462:
456:
453:
450:
438:
410:
402:
397:
390:
385:
378:
373:
366:
360:
354:
351:
348:
336:
327:
324:
319:
314:
307:
304:
299:
294:
288:
282:
279:
276:
271:
268:
263:
258:
252:
248:
245:
242:
239:
236:
229:
228:
227:
224:
214:
210:
208:
195:
192:
189:
186:
183:
179:
175:
171:
168:
164:
163:
162:
154:
149:
145:
132:
128:
125:
121:
120:
119:
111:
108:
99:
90:
88:
84:
80:
76:
72:
68:
58:
56:
52:
48:
44:
39:
37:
33:
29:
25:
22:refers to an
21:
4027:
3981:Audio mining
3880:
3874:
3839:
3833:
3792:
3788:
3782:
3752:(7): 721–9.
3749:
3745:
3739:
3712:
3708:
3702:
3683:
3677:
3642:
3638:
3632:
3607:
3603:
3600:Tabrikian,J.
3594:
3577:
3571:
3536:
3526:
3512:
3477:
3471:
3438:
3434:
3424:
3415:
3405:
3352:
3346:
3321:
3317:
3311:
3294:
3284:
3267:
3261:
3226:
3208:
3198:
3179:
3173:
3164:
3154:
3118:
3111:
3055:
3047:
3039:
3015:
3002:
2993:
2986:
2978:
2961:
2944:
2924:
2841:
2754:
2658:respectively
2545:
2313:
2301:
2297:
2279:
2262:
2258:
2254:
2057:
1894:
1724:
1601:
1385:
1309:
1300:
1291:
1279:
1275:
1272:
1269:
1266:
1244:
1235:
1128:
827:
426:
220:
211:
204:
160:
151:
117:
104:
93:Applications
75:hearing aids
64:
40:
38:techniques.
19:
18:
3610:(1): 1–13.
3441:: 1129–32.
3297:: 891–895.
4048:Categories
3722:1804.05111
3652:1804.03372
3362:1604.01642
3103:References
2302:See more:
223:beamformer
142:See also:
79:navigation
61:Technology
4054:Acoustics
3817:0001-4966
3766:1432-7643
3463:1536-1225
3389:1520-6149
3338:0096-3518
3303:2226-5147
3276:1530-1591
3270:: 832–5.
2814:⋅
2799:τ
2796:⋅
2787:
2779:−
2767:θ
2703:
2693:τ
2668:τ
2482:⋅
2435:∗
2400:⋅
2001:×
1967:×
1578:→
1563:×
1558:→
1535:→
1508:×
1498:→
1483:×
1478:→
1368:×
1365:π
1359:×
1344:±
1205:τ
1019:ξ
961:ξ
927:ξ
889:ζ
840:ζ
803:τ
797:π
697:∗
660:ζ
614:ζ
601:−
583:∑
572:τ
493:τ
488:−
469:τ
391:τ
386:−
367:τ
325:−
289:∑
280:−
253:∑
71:acoustics
3907:14665341
3866:60528168
3825:11144583
3774:30037380
3563:14042947
3504:16043629
3165:bksv.com
3146:11238184
3061:See also
1007:, where
184:(MUSIC).
55:3D space
51:monaural
24:acoustic
4059:Hearing
3797:Bibcode
3669:4745823
3612:Bibcode
3443:Bibcode
3253:7446711
3004:(using
157:Methods
43:mammals
32:sensors
3905:
3895:
3864:
3854:
3823:
3815:
3772:
3764:
3667:
3561:
3551:
3502:
3492:
3461:
3397:557491
3395:
3387:
3377:
3336:
3301:
3274:
3251:
3241:
3186:
3144:
3134:
2842:Where
2546:Where
2282:bionic
550:RWPHAT
444:RWPHAT
342:RWPHAT
3903:S2CID
3862:S2CID
3770:S2CID
3717:arXiv
3665:S2CID
3647:arXiv
3559:S2CID
3500:S2CID
3393:S2CID
3357:arXiv
3249:S2CID
3142:S2CID
2982:frogs
1602:where
107:Sonar
67:audio
41:Most
3893:ISBN
3852:ISBN
3821:PMID
3813:ISSN
3762:ISSN
3549:ISBN
3490:ISBN
3459:ISSN
3385:ISSN
3375:ISBN
3334:ISSN
3299:ISSN
3272:ISSN
3239:ISBN
3184:ISBN
3132:ISBN
3018:HRTF
2638:and
2582:and
2359:IFFT
1789:<
1635:and
1284:and
1129:The
180:and
146:and
83:TDOA
69:and
34:and
3885:doi
3844:doi
3805:doi
3793:108
3754:doi
3727:doi
3688:doi
3657:doi
3620:doi
3582:doi
3541:doi
3482:doi
3451:doi
3367:doi
3326:doi
3231:doi
3124:doi
2809:max
2775:cos
2706:max
2700:arg
2491:FFT
2448:FFT
2404:FFT
2371:FFT
2315:by:
2219:or
1725:If
1354:360
4050::
3901:.
3891:.
3860:.
3850:.
3819:.
3811:.
3803:.
3791:.
3768:.
3760:.
3750:12
3748:.
3725:.
3711:.
3663:.
3655:.
3643:95
3641:.
3618:.
3608:44
3606:.
3557:.
3547:.
3535:.
3498:.
3488:.
3457:.
3449:.
3439:11
3437:.
3433:.
3414:.
3391:.
3383:.
3373:.
3365:.
3332:.
3322:32
3320:.
3293:.
3247:.
3237:.
3217:^
3207:.
3163:.
3140:.
3130:.
2984:.
2306:.
176:,
57:.
3957:e
3950:t
3943:v
3909:.
3887::
3868:.
3846::
3827:.
3807::
3799::
3776:.
3756::
3733:.
3729::
3719::
3696:.
3690::
3671:.
3659::
3649::
3626:.
3622::
3614::
3588:.
3584::
3565:.
3543::
3520:.
3506:.
3484::
3465:.
3453::
3445::
3418:.
3399:.
3369::
3359::
3340:.
3328::
3305:.
3278:.
3255:.
3233::
3211:.
3192:.
3148:.
3126::
3020:(
2908:x
2905:a
2902:m
2898:d
2875:s
2871:F
2850:v
2822:s
2818:F
2805:d
2793:v
2782:1
2771:=
2740:}
2737:)
2734:k
2731:(
2726:j
2723:i
2719:p
2715:s
2712:c
2709:{
2697:=
2646:j
2626:i
2606:)
2603:n
2600:(
2595:j
2591:s
2570:)
2567:n
2564:(
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2555:s
2529:}
2521:|
2517:]
2514:)
2511:n
2508:(
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2499:s
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2468:n
2465:(
2460:i
2456:s
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2427:)
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2408:[
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2388:(
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2379:s
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2364:{
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2349:k
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2207:U
2204:P
2182:n
2178:w
2155:n
2151:w
2128:n
2123:S
2120:O
2117:P
2093:e
2090:c
2087:r
2084:u
2081:o
2078:s
2073:S
2070:O
2067:P
2038:2
2034:w
2030:+
2025:1
2021:w
2015:)
2009:2
2005:w
1996:2
1991:S
1988:O
1985:P
1980:+
1975:1
1971:w
1962:1
1957:S
1954:O
1951:P
1945:(
1939:=
1934:e
1931:c
1928:r
1925:u
1922:o
1919:s
1914:S
1911:O
1908:P
1880:)
1877:)
1872:2
1868:r
1864:(
1859:2
1855:U
1851:P
1848:(
1845:s
1842:b
1839:a
1836:+
1833:)
1830:)
1825:1
1821:r
1817:(
1812:1
1808:U
1804:P
1801:(
1798:s
1795:b
1792:a
1786:)
1781:2
1777:r
1773:i
1770:d
1767:,
1762:1
1758:r
1754:i
1751:d
1748:(
1745:t
1742:s
1739:i
1736:d
1708:i
1704:p
1681:i
1677:v
1654:2
1650:r
1646:i
1643:d
1621:1
1617:r
1613:i
1610:d
1584:|
1573:2
1569:v
1553:1
1549:v
1542:|
1529:2
1525:p
1519:1
1515:p
1504:)
1493:2
1489:v
1473:1
1469:v
1462:(
1455:=
1451:)
1445:2
1441:r
1437:i
1434:d
1431:,
1426:1
1422:r
1418:i
1415:d
1411:(
1407:t
1404:s
1401:i
1398:d
1371:r
1362:2
1350:U
1347:A
1338:=
1334:)
1331:r
1328:(
1324:U
1321:P
1217:n
1212:m
1181:h
1178:t
1173:m
1147:n
1143:m
1138:x
1114:k
1094:n
1072:h
1069:t
1065:i
1043:)
1040:k
1037:(
1031:i
1024:n
992:1
989:+
985:)
982:k
979:(
973:i
966:n
951:)
948:k
945:(
939:i
932:n
917:=
913:)
910:k
907:(
901:i
894:n
864:)
861:k
858:(
852:i
845:n
811:L
807:/
800:k
794:2
791:j
786:e
777:|
772:)
769:k
766:(
760:j
755:X
749:|
744:|
739:)
736:k
733:(
727:i
722:X
716:|
709:)
706:k
703:(
690:j
685:X
677:)
674:k
671:(
665:j
654:)
651:k
648:(
642:i
637:X
631:)
628:k
625:(
619:i
604:1
598:L
593:0
590:=
587:k
579:=
575:)
569:(
563:j
560:,
557:i
545:R
513:)
505:2
500:m
481:1
476:m
463:(
457:j
454:,
451:i
439:R
411:)
403:2
398:m
379:1
374:m
361:(
355:j
352:,
349:i
337:R
328:1
320:1
315:m
308:0
305:=
300:2
295:m
283:1
277:M
272:1
269:=
264:1
259:m
249:2
246:+
243:K
240:=
237:E
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