3D sound localization

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 particle filter that tracks sources and prevents false directions.

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.

To maximize the output energy of a delay-and-sum beamformer 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

${\displaystyle E=K+2\sum _{{m}_{1}=1}^{M-1}\sum _{{m}_{2}=0}^{{m}_{1}-1}{{R}^{\text{RWPHAT}}}_{i,j}\left({\tau }_{{m}_{1}}-{\tau }_{{m}_{2}}\right)}$

Where E indicates the energy, and K is a constant, ${\displaystyle {{R}^{\text{RWPHAT}}}_{i,j}\left({\tau }_{{m}_{1}}-{\tau }_{{m}_{2}}\right)}$ is the microphone pairs cross-correlation defined by Reliability Weighted Phase Transform:

${\displaystyle {{R}^{\text{RWPHAT}}}_{i,j}\left(\tau \right)=\sum _{k=0}^{L-1}{\frac {{\zeta }_{i}\left(k\right){X}_{i}\left(k\right){\zeta }_{j}\left(k\right){{X}_{j}}^{*}\left(k\right)}{\left|{X}_{i}\left(k\right)\right|\left|{X}_{j}\left(k\right)\right|}}{e}^{j2\pi k\tau /L}}$

the weighted factor ${\displaystyle {{\zeta }^{n}}_{i}\left(k\right)}$reflect the reliability of each frequency component, and defined as the Wiener Filter gain ${\displaystyle {{\zeta }^{n}}_{i}\left(k\right)={\frac {{{\xi }^{n}}_{i}\left(k\right)}{{{\xi }^{n}}_{i}\left(k\right)+1}}}$, where ${\displaystyle {{\xi }^{n}}_{i}\left(k\right)}$ is an estimate of a prior SNR at ${\displaystyle i^{th}}$ microphone, at time frame ${\displaystyle n}$, for frequency ${\displaystyle k}$, computed using the decision-directed approach.[8]

The ${\displaystyle x_{m_{n}}}$ is the signal from ${\displaystyle {m}^{th}}$ microphone and ${\displaystyle {\tau }_{{m}_{n}}}$ is the delay of arrival for that microphone. The more specific procedure of this method is proposed by Valin and Michaud[9]

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.

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 sensor arrays 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.

Picture of a 3D Acoustic Vector Sensor (air)

• Contains three orthogonally placed acoustic particle velocity sensors (shown as X, Y and Z array) and one omnidirectional acoustic microphone (O).

• Commonly used both in air [10] and underwater.

• Can be used in combination with the Offline Calibration Process[11] to measure and interpolate the impulse response of X, Y, Z and O arrays, to obtain their steering vector.

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 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.

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 low frequency and high frequency 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.

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.

Sound reflections always occur in an actual environment and microphone arrays[12] 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.

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:

${\displaystyle PU\left(r\right)={\frac {\pm AU}{360}}\times 2\pi \times r}$

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:

${\displaystyle dist\left(dir_{1},dir_{2}\right)={\frac {\left({\overrightarrow {v_{1}}}\times {\overrightarrow {v_{2}}}\right)\times {\overrightarrow {p_{1}p_{2}}}}{\left|{\overrightarrow {v_{1}}}\times {\overrightarrow {v_{2}}}\right|}}}$

where${\displaystyle dir_{1}}$and ${\displaystyle dir_{2}}$ are two directions, ${\displaystyle v_{i}}$are vectors parallel to detected direction, and ${\displaystyle p_{i}}$are the position of arrays.

If

${\displaystyle dist(dir_{1},dir_{2})<abs(PU_{1}(r_{1}))+abs(PU_{2}(r_{2}))}$

Two lines are judged as crossing. When two lines are crossing, we can compute the sound source location using the following:

${\displaystyle \mathrm {POS} _{source}={\frac {\left(\mathrm {POS} _{1}\times w_{1}+\mathrm {POS} _{2}\times w_{2}\right)}{w_{1}+w_{2}}}}$

${\displaystyle \mathrm {POS} _{source}}$is the estimation of sound source position, ${\displaystyle \mathrm {POS} _{n}}$ is the position where each direction intersect the line with minimum distance, and ${\displaystyle w_{n}}$ is the weighted factors. As the weighting factor ${\displaystyle w_{n}}$, we determined use ${\displaystyle PU}$ or ${\displaystyle r}$ from the array to the line with minimum distance.

3D sound localization of a large compressor[13]

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 Intensity Probe, in combination with a 3D tracker.

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 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.

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, noise control, and audio engineering, as it allows for a detailed understanding of the sound distribution and its interactions with the surrounding environment.

Structure of the binaural robot dummy head

Binaural hearing learning[5] is a bionic 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 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 neural networks 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 of the reflector. Binaural hearing use only 2 microphones and is capable of concentrating on one source among multiple sources of noises.

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).[14] HRTF also uses the robot head sensor, which is the 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.

See more: Head-related transfer function.

CSP method[15] 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 by:

${\displaystyle csp_{ij}(k)={\text{IFFT}}\left\{{\frac {{\text{FFT}}[s_{i}(n)]\cdot {\text{FFT}}[s_{j}(n)]^{*}}{\left|{\text{FFT}}[s_{i}(n)]\right\vert \cdot \left|{\text{FFT}}[s_{j}(n)]\right\vert \quad }}\right\}\quad }$

Where ${\displaystyle s_{i}(n)}$ and ${\displaystyle s_{j}(n)}$ are signals entering the microphone ${\displaystyle i}$ and ${\displaystyle j}$ respectively
Time delay of arrival(${\displaystyle \tau }$) then can be estimated by:

${\displaystyle {\tau }=\operatorname {arg} \max\{csp_{ij}(k)\}}$

Sound source direction is

${\displaystyle {\theta }=\cos ^{-1}{\frac {v\cdot \tau }{d_{\max }\cdot F_{s}}}}$

Where ${\displaystyle v}$ is the sound propagation speed, ${\displaystyle F_{s}}$ is the sampling frequency and ${\displaystyle d_{max}}$ is the distance with maximum time delay between 2 microphones.

CPS method does not require the system impulse response data that HRTF needs. An expectation-maximization algorithm 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.

Demonstration of 2d line sensor array

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.[16] By processing the data from two arrays using the maximum likelihood method, the direction, range and depth of the source can be identified simultaneously. Unlike the binaural hearing model, this method is similar to the spectral analysis method. The method can be used to localize a distant source.

The rotation of the two-microphone array (also referred as bi-microphone array [17]) 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 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.[18] 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.[19]

Structure of How to derive the Azimuth Estimation

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 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 frogs.

Based on previous binaural sound localization methods, a hierarchical fuzzy artificial neural network system combines interaural time difference(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[20] were used with the goal of the same sound localization accuracy as human ears.

IID-based or ITD-based sound localization methods have a main problem called Front-back confusion.[21] 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 non-stationary scenarios.

A general way to implement 3d sound localization is to use the HRTF(Head-related transfer function). 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.

dsp implementation of 3d sound localization

A DSP-based implementation of a realtime 3D sound localization approach with the use of an embedded DSP can reduce the computational complexity 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 channels and the audio input data in time series are processed one after another.[22]

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.

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 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.[23] 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.