 Research
 Open Access
 Published:
Multisource localization by using offset residual weight
EURASIP Journal on Audio, Speech, and Music Processing volume 2021, Article number: 23 (2021)
Abstract
Multiple sound source localization is a hot issue of concern in recent years. The Single Source Zone (SSZ) based localization methods achieve good performance due to the detection and utilization of the TimeFrequency (TF) zone where only one source is dominant. However, some TF points consisting of components from multiple sources are also included in the detected SSZ sometimes. Once a TF point in SSZ is contributed by multiple components, this point is defined as an outlier. The existence of outliers within the detected SSZ is usually an unavoidable problem for SSZbased methods. To solve this problem, a multisource localization by using offset residual weight is proposed in this paper. In this method, an assumption is developed: the direction estimated by all the TF points within the detected SSZ has a difference along with the actual direction of sources. But this difference is much smaller than the difference between the directions estimated by the outliers along with the actual source localization. After verifying this assumption experimentally, Point Offset Residual Weight (PORW) and Source Offset Residual Weight (SORW) are proposed to reduce the influence of outliers on the localization results. Then, a composite weight is formed by combining PORW and SORW, which can effectively distinguish the outliers and desired points. After that, the outliers are removed by composite weight. Finally, a statistical histogram of DOA estimation with outliers removed is used for multisource localization. The objective evaluation of the proposed method is conducted in various simulated environments. The results show that the proposed method achieves a better performance compared with the reference methods in sources localization.
Introduction
Multiple sound source localization is a hot subject in audio signal processing and has gained extensive attention over decades for its vital role in various audio applications. An accurate estimation of sound source location can be applied in robotics [1], sound source separation [2, 3], hearing aids [4], humanmachine interaction [5], and so on. The main task of multiple sound source localization is to obtain the position of sources in the acoustic scene by using observed signals from several sensors without knowing the information of sound sources and the generation process of the received signals. Generally speaking, the existing localization methods can be roughly divided into four categories. The first ones are based on the time difference of arrival (TDOA) and its extensions [6,7,8]. Various TDOAbased methods, including CC (crosscorrelation), GCC (generalized crosscorrelation), and MCCC (multichannel crosscorrelation), were developed to solve the localization problem. However, these methods still suffer from the accuracy decline caused by ambient noise and reverberation. The robustness of the TDOAbased methods can be improved from three aspects [9]: incorporate priori knowledge to ameliorate the performance, increase the number of sensors to take advantage of the redundancy, and take the reverberation into account in signal model to improve the accuracy of TDOA. While limited by the practical application scenarios, priori knowledge is not always available [10] and the number of microphones set up in the space is usually restricted. Lots of the methods introduce reverberation in modeling while their performance still degrades inevitably when reverberation time increases.
The spectral estimation techniques are also used in multisource localization, some of the representative methods include multiple signal classification (MUSIC) [11,12,13] and estimation of signal parameters via rotational invariance (ESPRIT) [14] algorithms. MUSIC is one of the famous subspace based methods for multisource direction of arrival (DOA) estimation under superdeterministic conditions (i.e., the number of microphones is greater than the number of sound sources) [15, 16]. The MUSIC algorithm exhibits high resolution and is applicable for an array of arbitrary geometric shapes. A direct path dominance (DPD) test [17] based method is adopted to perform DOA estimation by crossspectrum matrices. In this method, a focusing process is added to localize multiple sources using array with arbitrary configuration. Nevertheless, since the entire array manifold needs to be searched to find the steering vectors which are orthogonal to the noise subspace, most of spectral estimation techniques has poor computational efficiency [18]. Besides, the MUSICbased methods need to know the number of sources in advance. Due to the sparseness of speech signals, the number of simultaneously active sources is not consistent in broadband, which make them not suitable to deal with broadband sources.
The third one is based on the independent component analysis (ICA), which is usually used to deal with the linear instantaneous mixtures. The convolutive blind source separation problem can be solved by transforming the problem into the frequency domain [19,20,21,22]. Recently, ICAbased methods have also been adopted to conduct multisource localization [23,24,25]. As mentioned in [26], the ICAbased methods can be used in the TimeFrequency (TF) domain to carry out the multisource localization task so long as the number of dominant sources does not exceed the number of microphones in each TF zone, which means that the requirement for the number of microphones becomes more relaxed.
For the last category, sparse components analysis (SCA) is applied to locate multiple sound sources. Most of the SCAbased methods rely on the wdisjoint orthogonal assumption [27]. In this assumption, there are usually some TF zones, where only one source is active or dominant, exist, even if multiple sources sound simultaneously. These specific TF zones are called single source zone (SSZ). According to [26], it has been proved that using TF points within the detected SSZs to conduct multisource localization can obtain high accuracy. Moreover, [28] proposed a DOA estimation method using a sound field microphone to realize highly accurate positioning. The SCAbased methods include not only the detection of “zone” level sparse components but also “point” level sparse components. These “point” level sparse components are the TF points, which are called the single source point (SSP), where only one source is active or dominant. The methods that use SSP to perform multiple source localization are called the SSPbased methods [29,30,31,32,33]. The SSP detection methods include but are not limited to the coherent test based method [29], the energy threshold based method [31], and the energy decomposition based method in [32]. Recently, methods base on the phase feature of real and imaginary parts of mixture TF vectors have also been proposed. The method proposed in [33] falls into the last category which detects the lowreverberant singlesource (LRSS) points to perform multisource localization and achieves localization results with high accuracy. Different from these methods which focus on the extraction of sparse components from recorded signals, other methods aim to find the model that fitting the observed distribution [34]. All the methods mentioned above can achieve a good localization performance in simple acoustic scenarios (i.e., an acoustic scenario with low reverberation time and a low number of sound sources). However, as the number of sources or the reverberation time increases, the localization performance of both SSZbased methods and SSPbased methods declines. For SSPbased methods, there could be many outliers, which contain the wrong localization information, mixed in with the detected results. For example, the outliers can be TF points consist of multiple components with the same phase which cannot be discriminated by the phasebased SSP detection method. And the outliers can also be composed of a single reflection component that cannot be identified by both energybased and phasebased criteria. As for the SSZbased methods, in addition to the same problem as SSPbased methods, there are always some sources with relatively less DOA estimates in the histogram which are hard to be detected through peak searching. This kind of sources is called Statistically Weak Sources (SWS) and the other sources are called statistically dominant sources (SDS) [35]. A Statistically dominant source component removal (SDSCR) algorithm is proposed in the same paper to solve this problem. It has been proved that the SDSCR method can always obtain better localization results. However, the increasing number of outliers caused by multiple sources and high reverberation time is still unsolved and could lead to a significant decrease in localization accuracy.
In this paper, the problem of SSZbased methods mentioned above has been proved by verification experiments. This problem can be summarized as: there is an inevitable presence of outliers in detected SSZs which leads to the decreasing accuracy of localization. To solve to this problem, a new assumption has been proposed in this paper: since the distribution of outlier within SSZ is sparse and the DOA estimates of outliers are randomly distributed, the average direction calculated by the TF points inside SSZ does not far away from the location of sound source. Referring to this assumption, a residual based weighting idea has been proposed and applied in the SSZbased method to realize a robust multisource localization method for the reverberation environment. In the proposed method, two residual measurements are introduced to weaken the adverse effect brought by outliers. Among them, the residual measurement used in the outlier detection part aims to evaluate the angular difference, which is also called the offset, between the direction estimated by each TF point and the average direction estimated by their corresponding SSZ. This measurement is named as “point offset residual measurement.” The second proposed residual measurement, which is called “source offset residual measurement,” is mainly used to measure the offset between the DOA estimated by TF point and the coarse localization of their corresponding sound sources. Each of the residual measurement corresponds to a weight that is applied in the localization process. According to the residual measurement, they derivate from, they are named as “Point Offset Residual Weight” (PORW) and “Source Offset Residual Weight” (SORW), respectively. Both two weights have the characteristic that they give low values to outliers while high values for desired TF points. Based on this characteristic, these false detections of outliers would be weighted and suppressed. Different from the traditional methods which give both outliers and desired TF points a weight of 1 in the statistical histogram of DOA estimations, the proposed method combines two weights mentioned above to produce the histogram refer to the contribution of each TF point in the direction of their corresponding actual source. It should be mentioned that the proposed method can be applied to a variety of array setups. The PORW can be applied to all the SSZbased methods. The SORW can be applied to the methods where histograms are plotted to perform DOA estimation. At last, the advantages of the proposed method over traditional methods and its robustness in various environments are verified by several sets of objective and subjective experiments.
Modeling and angular calculation
In this section, the basic model of signals received by sound field microphone is introduced. Then, the SSZ detection criterion for TF zones and Bformat transformation, angular calculation for TF points is reviewed.
The SSZ detection using sound field microphone
In this paper, the sound field microphone [28], which is the array of directional microphones, is used to record the sound signals. A sound field microphone consists of four closely placed cardioid microphone capsules. Since the distance between source and microphone is much larger than that between different microphone capsules, these microphone capsules can be regarded spatially coincident with respect to a sound source. Under this condition, the recorded signals from different channels also have no delay in time domain. The recorded signals of four channels are represented as {s_{1}, s_{2}, s_{3}, s_{4}} which are corresponding to the microphone capsules pointing at front left up (FLU), front right down (FRD), back left down (BLD), and back right up (BRU), respectively. Assume an acoustic environment contains Q sources with reverberation and noise, the signals recorded by sound field microphone after short time Fourier transform (STFT) are modeled as the formula below:
where p ∈ {1, 2, 3, 4} is the index of the microphone capsule. The signals from ith source in the frame n and frequency number k are represented as x_{i}(n, k). h_{i, p}(k) is the transfer function between ith source to the pth microphone capsule. The signals received by pth microphone capsule from sound field microphone are represented as s_{p}(n, k). Since the reverberation components consist of signals from different sound sources in various of TF points, this part of received signals is simplified as r_{ev}(n, k). The noise components are represented as v_{n}(n, k). It should be mentioned that because of the spatially coincident characteristic of the different microphone capsules, the recorded signals from four capsules of the sound field microphone have closetoequal phase while different amplitude. Following equation should also be satisfied:
Meanwhile, if TF point (n, k) only consists of the direct component from a single source, the following equation should also be satisfied:
Where p, q ∈ {1, 2, 3, 4} (p ≠ q) represent the index of the sound field microphone channel. Since the signals from different channels of sound field microphone should have the same phase character, which means the signal waveform within SSZ in different channels should have high similarity. A SSZ detection criterion [26] is proposed based on this characteristic, which uses the normalized crosscorrelation (NCC) coefficient between signal between channels to detect SSZ. The NCC coefficient between channel p and q in the TF zone Z is defined as follow:
where p, q ∈ {1, 2, 3, 4} (p ≠ q) denotes the index of the channel and Z denotes the TF zone whose size is customized by user. R_{pq}(Z) is crosscorrelation coefficient given as follow:
When there are no reverberation components or noise components in the TF zone used for analysis, all the TF points within the TF zone consist of the signals from the same source through the direct path, the result of (4) should satisfy r_{pq}(Z) = 1, while it is hard to realize in the actual experimental environment so the criterion is relaxed as below:
where ε is an empirical threshold set by user according to the practical scenario. This threshold should guarantee that enough TF zones are detected to perform the localization; meanwhile, most of the TF zones contaminated by reverberation and interfere sources should be removed.
Angular calculation for TF points
The signals received by the sound field microphone directly are called the Aformat signals. In previous work [35], a simple and intuitive angular calculation method has been proposed where Aformat signals need to be changed into Bformat signals. The Bformat signals consist of four channels which are represented as {s_{w}, s_{x}, s_{y}, s_{z}}, and the transformation operation can be expressed as the formula below:
where s_{w} is the signal received by the omnidirectional channel, and {s_{x}, s_{y}, s_{z}} are the signal received by three channels correspond to the Cartesian coordinate.
As mentioned in [28], if a TF point only consists of the direct component from a single source i and no reverberation components are involved, the model of received Bformat signals from sound source i can be represented as:
Where x_{i}(n, k) represents the signals from source i in the frame n and frequency number k, μ_{i}, and γ_{i} are the azimuth and elevation of ith sound source, respectively. And thus, the localization of sound source i can be calculated. For simplicity, the formula below only calculates the azimuth of source i:
where Re{∙} represents the operation of taking the real part, and ∗ denotes conjugation. Since the azimuth calculation process depends on the ratio of Bformat signals from x, y channels, which means that for the TF points with a similar direction of the vector [s_{x,}s_{y}], the DOA estimations should have similar results. This particular trait is applied afterward to calculate the weight.
It should be noted that both (8) and (9) assume only one source is active in the TF point (n, k) for simplicity. However, there are many TF points contain reverberation components and\or multisource components. That means there are more parameters and the components from multiple source in (8) which cannot be eliminated by the division in (9), which causes the decline of the localization accuracy.
Problem statement and the proposed point offset residual weight
As mentioned in the last section, the degradation of localization performance is mainly caused by the reverberation components and\or multisource components. Once the estimated direction \( \hat{\mu}\left(n,k\right) \) for TF point (n, k) has a large difference to the directions of actual sources, this specific TF point is defined as an outlier. In this section, the problem caused by the outlier is verified by experiments. After analyzing the reason causes this problem, a relaxed assumption is proposed to solve the problem. In this assumption, the direction estimated by all the TF points in a detected SSZ is assumed to be much closer to the actual sources’ direction than that for the outliers. Refer to this assumption, the point offset residual measurement is introduced to measure the angular difference between the direction estimated by all the TF points within the detected SSZ and the direction estimated by each of them. Then, the point offset residual weight is calculated for the actual application.
Problem statement
In general, the SSZbased methods use the relevance feature of the TF zone to detect SSZ. Then, the TF points within detected SSZ are used to perform multisource localization. The SSZbased methods could usually obtain a good performance. However, with the increase of reverberation time and\or the increase of the source number, the localization accuracy of SSZbased methods is declining. This is mainly caused by the increasing ratio of outliers mixed in the detected SSZ. As this ratio increases, the wrongly counted number of sources could lead to the significant decrease in localization accuracy. To confirm the presence of outliers in the detected SSZ, a group of experiments is conducted.
In these experiments, the statistical analysis of DOA estimates for each TF point within detected SSZ is performed using 100 sound source segments with a length of 8s from the NTT [36] database. The angle between sources is settled as 60°. The experiments are divided into two cases: changing the number of sound sources and reverberation time. As for the experiments of changing the number of sources, the source number is selected as {2, 3, 4, 5} in an anechoic room. For the experiments of the changing of reverberation time, two sources are active simultaneously in the rooms with a reverberation time of 0 ms, 150 ms, 300 ms, and 450 ms, respectively. In these experiments, the SSZ is detected under four different thresholds of NCC, which means that the parameter from formula 6 mentioned above is chosen from {0.1, 0.25, 0.4, 0.55}. Among all the TF points in the selected SSZs, the outliers are defined as the TF points whose DOA estimations’ deviation from the direction of the actual source by more than 20°. On the contrary, the rest of the TF points within the detected SSZ are defined as the desired TF points. The threshold of 20° is an empirical threshold chosen according to the informal experiments. The outlier ratio η is calculated as follow:
where κ is the number of the detected SSZ, Z_{τ} is the τth detected SSZ. N_{o} is the outliers’ number in Z_{τ} and N_{a} is the size of Z_{τ}. The ratio of outliers in the different condition is given in Fig. 1a and b.
From Fig. 1, a visible trend can be found that the proportion of outlier is increasing with the number of source and the reverberation time. Besides, the increase in the outlier ratio caused by reverberation is much greater than that caused by the increase in source number. More specifically, we take the blue labeled (i.e., ε = 0.1) data in Fig. 1a as an example, when the room is anechoic with two sources, only 14.3% of outliers are mixed in the detected SSZ. When the number of sources rises to five, the ratio of the outlier is 28.6%, which means that despite the increase of the outlier ratio, most of the detected TF zones are still dominant by only one source. However, the situation is quite different in the presence of reverberation. We still take the blue labeled data for analysis, in Fig. 1b, compared with the outlier ratio of 14.3% in the anechoic environment, the outlier ratio increases to 44.1% with 150 ms of reverberation. When the reverberation is 450 ms, the outlier is an account for 63.6% of the total TF points within detected TF zone which means the components carry the directional information of actual sources are overwhelmed by other components with a high possibility.
On the other hand, it can be found that the change of threshold can hardly decrease the ratio of outliers significantly. From both Fig. 1a and b, the red and green labeled data (i.e., ε = 0.4, ε = 0.55, respectively) always obtained a higher ratio of outliers than blue and orange labeled data (i.e., ε = 0.1, ε = 0.25, respectively), which means that the increase of the threshold could increase the ratio of outliers in the detect TF zone. However, even lower the thresholds means fewer outliers are included in the detected TF zone, the strict threshold also makes fewer TF zones can be detected, which could lead to insufficient data for analysis. Above all, the change of the SSZ detection threshold could not reduce the number of outliers effectively, and the outliers are always existing in the detected SSZ.
The analysis for the cause of these outliers
From the description above, the outliers always exist in the detected SSZs, and the reasons for the presence of these outliers are analyzed as follows:

The SSZbased methods apply the zonelevel characteristics to discriminate TF zones dominated by only one sound source, and this leads to negligence in the performance of the individual TF points. Even though the outliers may carry multiple sound components, these redundant components could hardly pull down the NCC of the whole TF zone and that specific TF zone can still be recognized as SSZ.

In order to guarantee that enough SSZs are detected for the localization, the SSZ detection threshold ε should be relaxed. However, the relaxed threshold could also mean a large ratio of outliers mixed in with the detection result.
In conclusion, the existence of the outlier in the detected TF zone is an unavoidable problem for SSZbased methods and this problem gets worse as the reverberation time and source number increase.
The proposed point offset residual measurement
From the former section, we found that there are always outliers exist in the detected SSZs and the change of the SSZ detection threshold can hardly change the proportion of outliers. To solve this problem, a new assumption is proposed in this section: the DOA estimations using outliers are far away from the direction of the actual source. While the DOA estimations using all the TF points within detected SSZ are much closer to the direction of the actual source. A set of experiments are conducted to verify this assumption. In this experiment, the root mean squared error (RMSE) is used to measure the error between estimate direction and true direction, which is defined as:
where m represents the number of estimated directions. \( \hat{\mu}(i) \) is the ith estimated direction and the direction of its corresponding source is μ_{j}, j ∈ {1, 2, …, Q}, Q is an integer and presents the number of actual sources.
Three sets of directional estimation are used to calculate RMSE; the first set consists of the directions estimated by taking the average of all the TF points within each SSZ while the second set is the average of directions estimated by outliers. The third one is the directions estimated by each outlier. The same experimental data and simulation environments as the experiments performed above are used. The results are shown in Fig. 2a and b.
In Fig. 2, the lines with blue and square marks are the RMSE of directions estimated by each outlier, and the line with green circle representing the RMSE corresponding to the average direction of the outliers within the detected TF zone. The line with a label of orange and triangle is the RMSE of the direction estimated by all the TF points from each SSZ. In both figures, the direction estimated by each SSZ is the closest to the actual direction. While the direction estimated by each outlier obtain the biggest RMSE. The same result can be obtained in a variety of environments which proves the proposed assumption.
Besides, the reason causes this phenomenon could also be found in this figure. The reason for the low RMSE of SSZ is not only because of the introduction of desired points. From Fig. 2, it can be found that in the same situation, the green line is lower than the blue line which proves that taking average of outliers also contributes to the decrease of RMSE.
Refer to the assumption mentioned above, a conclusion can be obtained: once the offset between the direction estimated by a TF point and the direction of its corresponding SSZ is large; this specific TF point has little contribution to the localization. Based on this conclusion, a measurement called point offset residual measurement is introduced to evaluate the distance between the direction estimated by SSZ and every TF point. The point offset residual measurement is defined as:
where 〈∙〉 is the symbol of the inner product operator, and ‖∙‖ denotes Euclidean norm. V(n, k) is a vector formed by Bformat signal in (n, k), V(n, k) = [s_{y}(n, k), s_{x}(n, k)]^{′}, [∙]^{′} represents taking the transposition of the vector.\( \overline{\boldsymbol{V}}\left(\mathrm{Z}\right) \) is a vector formed by Bformat signals in Z, which is given as:
where K represents the number of TF points in the detected SSZ. According to the description below (9), the vector consists of Bformat signals is corresponding to the estimated direction. That is why these vectors are introduced to measure the angular difference between SSZ and the TF points.
The reason that the vector projection is used as the measurement is described as follows: In complex acoustic environments, most of the TF points contain multiple components which can be seen as the superposition of plane waves. From the perspective of spatial geometry, if the microphone is recognized as a point in the space, then each of the plane waves corresponds to a vector. Thus, the signals received in a TF point can be recognized as the composite vector, and formula 12 can be regarded as the projection of the vector. More specifically, the vector of each TF points is projected onto the direction estimated by their corresponding SSZ to measure the contribution of each point in the direction of their corresponding SSZ. On the other hand, outliers could also contain the contribution of components from the source directly which is overwhelmed by reverberation components, and the proposed PORW is used to measure this contribution.
The proposed point offset residual weight
This proposed measurement calculates the orthogonal projection of each TF point signal vector on SSZ average signal vector which aims to describe the offset between the direction of SSZ and the direction estimated by each TF point. However, this measurement only describes the offset between the direction of SSZ and the directions of the points within it, which can be used to measure the contribution but not represent it. Therefore, a normalization step is necessary to change the angular difference into weight, which could represent the contribution of a TF point and be directly assigned to a point. The point offset residual weight (PORW) is defined as:
It should be noted that the higher PORW is given to the TF points who have a higher contribution to the direction estimated by SSZ.
Since the direction estimated by SSZ is much closer to the direction of the actual source than the direction estimated by outliers. The proposed PORW should have a characteristic that a higher value for desired points, while a lower value for outliers. A set of experiments are conducted to verify this characteristic. The experimental data and simulation environment are the same as the experiment mentioned above. In this experiment, the average weights of four kinds of TF points are calculated. These four categories are named as: Desired points in the desired SSZ (DPDS), desired points in falsely detected SSZ (DPFS), outliers in falsely detected SSZ (OFDS), and outliers in desired SSZ (ODS). The specific name of points and SSZs in the categories is explained as follows, the desired point is the same as the desired TF point mentioned above. The desired SSZ represents a detected SSZ whose direction error is less than 20°, while the falsely detected SSZ does not meet this condition. More specifically, we take DPDS as an example: the DPDS collects all the desired TF points in the SSZ with a direction error less than 20° and then, the average of their given PORW is calculated. The results are given in Fig. 3.
In Fig. 3, it can be found that desired points obtain higher weights than the outliers in all the situations. As the reverberation time increase, the difference of PORW for outliers and desired points is decreased. More specifically, in the environment with a reverberation time of 150 ms, the average PORWs for DPDS and DPFS are 0.96 and 0.87, respectively. While for the ODS and OFDS, the average PORWs in the same situation are just 0.81 and 0.80, respectively. As the reverberation time increase, it can be found that the PORW of DPDS holds steady and the PORW of DPFS has declined while the average PORW of the desired TF point is still higher than that of outliers, which shows the robustness of the proposed PORW.
It should be mentioned that the DPFS represents the desired TF point in falsely detected SSZ. The reason DPFS gets a higher weight than outlier is that the falsely detected SSZ contains outliers with different directions which could also far away from their corresponding SSZ’s direction. On the other perspective, the SSZ’s direction has a feature that it does not far away from the actual source’s azimuth. Even the falsely detected SSZ has a direction whose offset larger than the threshold, it still relatively closes to the true direction which brings a relatively high weight to the desired TF points.
In summary, the proposed PORW can be used to attenuate the undesirable effects of outliers in SSZbased methods. However, even the outliers’ effect can be weakened; the outliers themselves are hard to be removed by using a single weight. To remove the outliers, the second kind of weight is proposed in the following process and more details are described in the next section.
Proposed method
Based on the proposed PORW, a multisource localization method is proposed and the flowchart is presented in Fig. 4. The whole method is described below:
Firstly, the SSZ detection is operated for the received Aformat signals. Secondly, all TF points in detected SSZ are converted into Bformat to form the signal vector and calculate PORW for every TF points. Thirdly, the PORWs are used to weight the Bformat signal vectors to weaken the effect of outliers. A more accurate DOA estimation of SSZ after weighing can be obtained. Then, based on these DOA estimations, the statistical source component equalization (SSCE) [37] is conducted to obtain the rough localization of sources. Later, the SORW is proposed for further reducing the effect of outliers. Follow by that, the SORW and PORW are combined as a composite weight which is used to remove the outliers and draw the histogram of DOA estimation. Finally, postprocessing applied in [35] including KDE and peak searching is conducted to complete the multisource localization.
Since the directional analysis of Bformat signals can be performed relatively simply based on an energetic analysis of the sound field, the activity intensity [38] which shows the direction of the flow of energy is introduced to replace the vector formed by Bformat signals. The corresponding formula is shown as follow:
where ρ and c are represented as the density of the medium and the speed of sound respectively and they are both constant. The representation of active intensity vector in the horizontal plane is I(n, k) = [I_{x}(n, k), I_{y}(n, k)]. More details of the proposed method are described below:
The blind weighting of TF points
Based on the description of the active intensity mentioned above, the proposed assumption can be changed to:

There are always outliers in the detected SSZ whose directions far away from the actual source.

The SSZ’s direction is close to the direction calculated by the active intensity vector which contains the actual sources’ azimuth information while the directions calculated by using the outliers’ active intensity vectors have a big difference with the actual source’s direction.
Therefore, the direction estimated by SSZ and the activity intensity vector of all the TF points within SSZ is used to obtain the PORW. Then, the obtained PORWs are combined with the active intensity vector to weaken the impact of outliers. A more accurate direction estimated by taking the average of the weighted active intensity vector in each SSZ can be obtained by the following formula:
The estimated direction of the SSZ can be used as guidance for statistical source component equalization.
Statistical sound source component equalization
The statistical sound source component equalization [37] aims to deal with the masking phenomenon caused by the excessive disparity in the number of TF points each sound source belongs to. The sound sources are divided into two categories, the statistically weak sources (SWS) and the statistically dominant sources (SDS). The former is likely to be masked by the latter in the DOA statistical histogram because of their few corresponding TF points. Four thresholds are set to distinguish the TF points belong to the SDS and remove a part of them to make the SWS sufficiently obvious to be retrieved by postprocessing. Although this step solves the problem of the masked SWS, the effect of outliers on the localization is instead amplified after this step because the removal of components from sound sources is equal to the enhancement of the outliers’ components.
The weighting of outliers under rough localization guidance
To solve the problem caused by the removal of SDS components, the source offset residual measurement is proposed. The peaks formed by outliers, which are called the pseudopeaks, have some characteristics different from the peaks formed by actual sources’ components, which are called the true peaks. These characteristics can be summarized in two aspects: Firstly, the pseudopeaks usually lower than the true peaks. Secondly, the pseudopeaks locate differently in the histogram before and after smoothing due to the randomly distributed outliers. While the true peaks always have the same location in the histogram before and after smoothing. Based on these characteristics, the SORW is introduced to deal with the problem caused by pseudopeaks, the formula is shown below:
where \( i\in \left[1,\overset{\sim }{Q}\right] \) is the index of the estimated source, \( \overset{\sim }{Q} \) is the estimated number of sources (i.e., the pseudopeaks are included). \( {\overset{\sim }{\boldsymbol{I}}}_i \) is the active intensity vector of the TF point that carries the azimuth information closest to the ith estimated azimuth.
Like the PORW, a normalization step is also necessary to the source offset residual measurement. Therefore, the source offset residual weight (SORW) is proposed to lower the pseudopeaks in the statistical histogram which is defined as:
Since the pseudopeaks in smoothed histogram hardly correspond to local maximums formed by outliers in the DOA statistical histogram, the outliers who form the local maximum in DOA statistical histogram can hardly be given the highest SORW. While the TF points with the accurate azimuth information could obtain a higher SORW due to the peaks formed by them have the same localization before and after the smoothing. Like the previous description, an experiment is conducted to verify the proposed SORW in different reverberation time using the same group of data, the average of SORW calculated by outliers and desired points is shown in Fig. 5:
It can be found that the SORWs which are given to the desired points are higher than that given to the outliers in different reverberation time. With these experimental results, SORW can be proven effective to distinguish the outliers and desired TF points. Although the SORW can be easily integrated into other localization frameworks, it should be noted that the SORW is based on the rough localization using PORW. That means the calculation of PORW is a necessary step for the obtaining of SORW. A detailed explanation would be given in the next section combining with the experiments.
The composite weight based postprocessing
In this section, the proposed PORW and SORW are combined into one composite weight, the formula is shown below:
The experiment is also conducted to evaluate the composite weight and Fig. 6 shows the results:
Experimental results show that both outliers and desired points have lower composite weights compared with the results of SORW but the outliers still get weights much lower than desired points. According to the analysis above, both PORW and SORW can distinguish the desired points and outliers by giving a lower weight to outliers which means that the composite weight should have a better performance. To verify this conclusion, the difference between the weights given to outliers and desired points are calculated by using PORW, SORW, and composite weight. The results are shown in Fig. 7.
It can be found that the composite weight has the highest difference between outliers and desired points in all the simulation environments and the conclusion mentioned above is proved. Due to its high efficiency to discriminate outliers, the composite weight is used to remove the outliers and draw the histogram for the final localization. The desired points should satisfy the equation given as follow:
where δ is an empirical threshold set by uses according to the application scenario and the TF points that do not satisfy this equation are recognized as outliers and their weights are assigned a value of 0. The histogram is drawn using the following formula:
where μ ∈ [1,360], (n, k) ∈ ℤ is the angle in the histogram and ℤ represents the set of all the detected SSZ after equalization. The composite weight can be integrated into the histograms of SSZbased methods. After that, the postprocessing applied in [35] is used to perform the localization by KDE and peak searching.
Results and discussion
In this section, experiments are conducted to evaluate the performance of the proposed method. The experimental validation includes two aspects: the comparison between the localization results using different proposed weights and the comparison between different methods. The experiments are conducted in both simulation and actual environments, the experimental environments and reference methods is introduced as follows:
Experimental environments and reference methods
The simulation experiments were realized by using ROOMSIM package [39]. The simulation room used for experiments is uniformly set as a cube with a length of 6 m, a width of 3 m, and a height of 2 m. The sound field microphone is set in the center of the simulated room and the sources are set around the microphone with a distance of 1 m. It is noted that the elevation angle and the azimuth have the same calculation process except for the different channels of B format signals are selected. For the convenience of explanation and calculation, the elevation is set as 0° and not involved in the localization. It also means that the height of the sources is the same as that of the microphone. The original speech signals are chosen from the Chinese subdatabase from the NTT database [36]. The other parameters include but are not limited to the thresholds for STFT or SDSCR algorithm is shown in Table 1.
It should be noted that ε_{p} is the threshold for the peak searching on postprocessing. The different values for the minimum difference threshold ε_{a} are set based on the prediction of the minimum possible distance between sound sources, in the experiments of changing separation of sources, the minimum difference threshold is changing with the separation of sources. As for the outliers’ removal threshold (δ), which is introduced in this paper, is set according to the results of statistical experiments. Combing with the results in Fig. 6, it can be found that the threshold of 0.6 can distinguish the outliers and the desired points in most of the experiment settings.
As for the experiments using actual recorded signals, the signals are recorded in an acoustic chamber with a dimension of 4.5 m × 3.5 m × 2.8 m. The reverberation time and background noise are estimated to be 400 ms and 28 dB, respectively. A Sennheiser Ambeo VR Mic was selected to record the signals. During the recording process, the microphone was set at the center of the room with a height of 1.8 m. Two male speakers were located 1.6 m away from the microphone.
The reference methods are selected from the representative methods of SSZbased method [28], SDSCR algorithm [35], DPD [17] test based method and SSPbased method [33].
The statistical analysis for outliers’ ratio with different source number
Before calculating the accuracy of the localization under different experimental environments, a group of experiments is performed to verify the effectiveness of the proposed method in terms of outlier removal. In these experiments, the reverberation time is set as 150 ms and the separation between sources is set as 60°, and the minimum difference threshold is set as 50°. The number of sources is chosen from {2, 3, 4, 5}. The results are shown in Fig. 8:
It should be noted that the way to select outliers and calculate outlier’s ratio are the same as experiment shown by Fig. 1, which means that the DOA estimation is conducted for the points selected by the methods, once the DOA of a selected point has a deviation from the direction of the actual source by more than 20°, this point is recognized as an outlier. The outlier’s ratio is obtained by calculating the proportion of outlier within selected points. From Fig. 8, it can be found that as the number of source increases, the outlier’s ratio of all the methods increases. Among the points selected by all the methods, the points selected by proposed methods have the lowest outliers’ ratio, which proves the effectiveness of the proposed method in terms of outlier removal.
The evaluation of the proposed method in different reverberation time
In this section, the proposed method is evaluated in the environment with different reverberation times. Four kinds of room with different reverberation times are set by the adjustment of the absorption of the walls inside the room and the experiments are conducted in these room with the reverberation time of {150 ms, 300 ms, 450 ms, 600 ms}. Three sound sources with a separation of 60° are active simultaneously and the RMSE of the estimated localization of sources are calculated. The minimum difference threshold is set as 50° in this group of experiments. The results are shown in Fig. 9:
In addition to the obvious trends of increasing RMSE with increasing reverberation time, the proposed method using composite weight can usually obtain the lowest RMSE. As Fig. 9a shows, when the reverberation time is set as 150 ms, the difference of RMSE between the proposed method and SSP is only 1.1 while this difference widens to 6.58 when the reverberation time increases to 600 ms which demonstrates the advantage of the proposed method against reverberation environments. Comparing to that, the difference in localization accuracy between the proposed method and SSZ is even larger, especially in the environment with high reverberation time. In the room with a reverberation time of 600 ms, the RMSE of SSZ is up to 34.1, which is 20.2 higher than the RMSE of the proposed method. This severe decline in the localization accuracy is mainly due to the incorrect counting of source number. It should be noted that the data used to evaluate the localization performance is selected only if at least one method has its sound source counting correct. More specifically, the correct counting of sources’ number means that the number of detected sources is equal to the number of actual sources. But in the complex acoustic environments, this criterion is too hard to realize for some methods, so this criterion has been relaxed. As long as all of the actual sources in the scenario have a corresponding localization estimation, and the error is not higher than the minimum difference threshold set above, the source counting is considered as a correct source counting result.
Since the traditional SSZbased methods have poor robustness to the increase of reverberation time compared with SSPbased methods and the proposed method. In the scenario with severe reverberation interference, the SSZbased method usually has the source counting problem more often than the proposed method which brings a huge difference in the localization accuracy.
Figure 9b aims to indicate that each step of the proposed method is necessary. It can be found that in this figure, the data with red mark always has a higher RMSE than the data with orange mark which means that the removal of PORW from the proposed method leads to a greater negative influence than removing SORW from the proposed method. As previously mentioned, the SORW is used to remove the pseudopeaks consist of outliers based on the guidance of PORW. Once the PORW is not involved in the rough localization, the difference between pseudopeaks and peaks consist of desired TF points is shortened and it is possible that the peaks are wrongly removed. While the PORW is used in the blind weighing process which means that it can be used without the SORW. When the reverberation time is set as 150 ms and 300 ms, it only has little difference between the data marked yellow and the data marked orange because of the rare occurrence of pseudopeaks in these scenarios. However, the absence of PORW does cause the localization accuracy decline in high reverberation scenarios. In the scenario with a reverberation time of 450 ms, the removal of SORW in the proposed method leads to the RMSE increase by 9.22, which means that the false detection of pseudopeaks can cause a sharp decline of localization performance. It should be noted that when the reverberation time is 600 ms, this difference caused by removing SORW is 5.32, which is lower than that in the reverberation time of 450 ms. This phenomenon is caused by the increase in the error of the estimated directions when the reverberation time is 600 ms. The offset increase in the results with wrongly counted source number is not significant compare to the offset increase in the results with the source number counting correctly. In conclusion, the SORW is based on the PORW which means that without PORW, the proposed method is hard to be established. Even the PORW can exist only by itself, the problem of pseudopeaks still leads to a dramatic increase of error in high reverberation situations. Both SORW and PORW play necessary roles in the proposed method.
The evaluation of proposed method with different separation of sources
In order to validate the proposed method under multiple conditions, experiments are conducted in the scenario of changing sources’ separation. The reverberation time is set as 150 ms and the number of sources is three. The separations between sources are selected as {30°, 40°, 50°, 60°}. It should be noted that in this section, the minimum difference threshold used in SDSCR and postprocessing changes with the separation of sources to simulate the changing of the preestimated minimum angle spacing between sources. The minimum difference threshold is set 10° lower than the separation between sources which means that the minimum difference threshold is select from {20°, 30°, 40°, 50°} corresponding to the separation between sources. The results are shown in Fig. 10:
From both figures in Fig. 10, the proposed method still has the lowest RMSE in all the scenarios. Apart from that, another trend in Fig. 10 is that in complex acoustic environments, the difference of RMSE for the proposed method and the reference methods is lower than that in Fig. 9. For example, in the room with a reverberation time of 600 ms, the difference of RMSE for the proposed method and the SSP is 6.58 while in the scenario that the separation between sources is 30°, the difference between the proposed method and the SSP is just 0.25. It should be noted that the reduction of separation between sources does not boost the reverberation components as the operation of increasing reverberation time does. The problem caused by the reduction of separation mainly lies in the difficultly in the peak searching part where the distance between adjacent peaks is too close and hard to be detected. That means a reasonable preestimated minimum angle spacing can help with this problem. Since peak searching is a necessary part of the postprocessing, both the proposed method and reference method could benefit from the suitable minimum difference threshold setting. That lowers the RMSE difference between the proposed method and reference methods. Even so, the proposed method still has the lowest RMSE compared with both reference methods and the proposed method with one proposed weight removed, which demonstrates the validity of the proposed method in a variety of situations.
The evaluation of proposed method using noisy signals and actual recorded signals
The proposed method is further verified by using noisy signals and signals recorded in real environments.
For the experiments using noisy signals, the reverberation time is set as 450 ms. Three sources with a separation of 90° sound simultaneously in the simulated room and the minimum difference threshold is set as 70°. According to the setup of experiments, white Gaussian noise is added to all the channels of the sound field microphone to create signals with different signal to noise ratio (SNR). In this set of experiments, the SNR is chosen from {15dB, 25 dB, 35 dB} to evaluate the performance of proposed method for different recorded signals. The results are shown in Fig. 11 below:
It can be found from Fig. 11 that with the increase of SNR, the RMSE of all the methods decline, while their decline rate varies. Figure 11a shows that when the SNR increase from 15 to 25 dB, the RMSE of the proposed method declines sharply from 10.9 to 6.8. Then the RMSE remains relatively stable when the SNR increases from 25 to 35 dB, which means that when the SNR is equal or lower than 25 dB, noise has little effect on the localization results. While for the other methods, it can be observed that their RMSE decreases constantly with the increase of the SNR, which indicates that among all the experimental situations, noise always has a great impact on the localization accuracy of the reference methods. A similar conclusion can be also obtained from Fig. 11b. In summary, the proposed method can achieve a better performance in processing noisy signals. More specifically, when the noise is moderately added into the signals (i.e., when SNR = 25 dB or 35 dB), the proposed method can effectively reduce the impact of noise, even the localization accuracy of the proposed method is degraded by the harsh effects of lower SNR (i.e., SNR = 15 dB), it still achieves better performance than the reference methods.
To further evaluate the performance of the proposed method, a series of experiments using actual recorded signals are conducted. The information about the acoustic chamber used in these experiments is given in the first part of the section. The locations of the speakers are set in advance, and the angle difference between sources is chosen from {60°, 90°, 120°, 150°, 180°}. The results are shown in Fig. 12:
Similar to the experiments conducted using simulation signals, the RMSE of all the methods increases with the decline of separation between sources. The proposed method also achieves better performance in all the experimental situations compared with reference methods and incomplete versions of the proposed method. It should be noted that the RMSE of all the methods using actual recorded signals is much larger than that using simulation signals. This is not only because both noise components and reverberation components exist in the actual recorded signals but also due to the unpredictable noise caused by the incomplete sound insulation of acoustic chamber or the stronger reflections from the objects such as devices in the room. Even so, the proposed method still has the lowest RMSE, which demonstrates the validity of the proposed method in actual environments.
Conclusions
In this paper, the unavoidable problem of outliers’ existence in the detected SSZs has been explained and verified. To solve this problem, an assumption based on the characteristic of the whole SSZ has been proposed. Refer to this assumption, each TF point within SSZ is weighted by the PORW and SORW proposed in this paper to weaken the effect of outliers. Finally, the proposed PORW and SORW are combined as a composite weight to maximize the difference between the weight applied to the outliers and desired points, then the outliers are removed by an empirical threshold. The proposed method has been proved to achieve better performance over various experimental environments compared with the reference methods. Besides, the proposed method can be integrated into other localization frameworks making use of DOA histograms plotted by SSZbased methods. The future work lies in the scientific selection of thresholds instead of using empirical thresholds.
Availability of data and materials
Not applicable.
Abbreviations
 SSZ:

Single source zone
 TF:

Timefrequency
 PORW:

Point offset residual weight
 SORW:

Source offset residual weight
 TDOA:

Time difference of arrival
 MUSIC:

Multiple signal classification
 ESPRIT:

Estimation of signal parameters via rotational invariance
 DOA:

Direction of arrival
 DPD:

Direct path dominance
 ICA:

Independent component analysis
 SCA:

Sparse components analysis
 SSP:

Single source point
 LRSS:

Lowreverberant singlesource
 SWS:

Statistically weak source
 SDS:

Statistically dominant sources
 SDSCR:

Statistically dominant source component removal
 FLU:

Front left up
 FRD:

Front right down
 BLD:

Back left down
 BRU:

Back right up
 STFT:

Short time Fourier transform
 NCC:

Normalized crosscorrelation
 RMSE:

Root mean squared error
 DPDS:

Desired points in the desired SSZ
 DPFS:

Desired points in falsely detected SSZ
 ODS:

Outliers in desired SSZ
 OFDS:

Falsely detected SSZ
 SSCE:

Statistical source component equalization
References
 1.
J.M. Valin, F. Michaud, B. Hadjou, J. Rouat, Localization of simultaneous moving sound sources for mobile robot using a frequencydomain steered beamformer approach. Proc. IEEE. Int. Conf. Robot. Automat. 1, 1033–1038 (2004)
 2.
X. Chen, W. Wang, Y. Wang, X. Zhong, A. Alinaghi, Reverberant speech separation with probabilistic timefrequency masking for bformat recordings. Speech Comm. 68, 41–54 (2015)
 3.
Y. Yu, W. Wang, P. Han, Localization based stereo speech source separation using probabilistic timefrequency masking and deep neural networks. EURASIP J. Audio, Speech, Music Process 2016(1), 1–18 (2016)
 4.
T.V. Bogaert, E. Carette, J. Wouters, Sound source localization using hearing aids with microphones placed behindtheear, inthecanal, and inthepinna. Int. J. Audiol. 50(3), 164–176 (2011)
 5.
T. Latif, E. Whitmire, T. Novak, A. Bozkurt, Sound localization sensors for search and rescue biobots. IEEE Sensors J. 16(10), 3444–3453 (2016)
 6.
C. Knapp, G. Carter, The generalized correlation method for estimation of time delay. IEEE Trans. Acoust. Speech Signal Process. 24(4), 320–327 (1976)
 7.
J. Benesty, J. Chen, Y. Huang, Timedelay estimation via linear interpolation and cross correlation. IEEE Trans. Speech Audio Process 12(5), 509–519 (2004)
 8.
F. Nesta, M. Omologo, Generalized state coherence transform for multidimensional TDOA estimation of multiple sources. IEEE Trans. Audio Speech Lang. Process. 20(1), 246–260 (2012)
 9.
J. Chen, J. Benesty, Y. Huang, Time delay estimation in room acoustic environments: an overview. EURASIP J. Adv. Signal Process, 26503, 119 (2006)
 10.
P. Annibale, R. Rabenstein, Accuracy of Timedifferenceofarrival based source localization algorithms under temperature variations, 2010 4th International Symposium on Communications, Control and Signal Processing (ISCCSP) (Limassol, 2010), pp. 1–4. https://doi.org/10.1109/ISCCSP.2010.5463320
 11.
R. Schmidt, Multiple emitter location and signal parameter estimation. IEEE Trans. Antennas Propag. 34(3), 276–280 (1986)
 12.
J.P. Dmochowski, J. Benesty, S. Affes, in Applications of signal processing to audio and acoustics, 2007 IEEE workshop on. Broadband music: opportunities and challenges for multiple source localization (2007), pp. 18–21
 13.
C.T. Ishi, O. Chatot, H. Ishiguro, N. Hagita, in Intelligent robots and systems, 2009. IROS 2009. IEEE/RSJ international conference on. Evaluation of a musicbased realtime sound localization of multiple sound sources in real noisy environments (2009), pp. 2027–2032
 14.
R. Roy, T. Kailath, Espritestimation of signal parameters via rotational invariance techniques. IEEE Trans. Acoust. Speech Signal Process. 37(7), 984–995 (1989)
 15.
Argentieri, S.; Danes, P. Broadband variations of the MUSIC highresolution method for sound source localization in robotics. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, San Diego, CA, USA, 29 October–2 November 2007; Volume 11, pp. 2009–2014.
 16.
Dmochowski, J.P.; Benesty, J.; Affes, S. Broadband MUSIC: opportunities and challenges for multiple source localization. In Proceedings of the IEEE Workshop on Applications of Signals Processing to Audio and Acoustics, New York, NY, USA, 21–24 October 2007; pp. 18–21.
 17.
B. Rafaely, K. Alhaiany, Speaker localization using direct path dominance test based on sound field directivity. Signal Process. 143, 42–47 (2018)
 18.
Z. I. Khan, M. M. Kamal, N. Hamzah, K. Othman and N. I. Khan, “Analysis of performance for multiple signal classification (MUSIC) in estimating direction of arrival,” 2008 IEEE International RF and Microwave Conference, Kuala Lumpur, 2008, pp. 524529, doi: 10.1109/RFM.2008.4897465.
 19.
S. Haykin, Unsupervised Adaptive Filtering: Volume I Blind Source Separation (Wiley Interscience, New York, 2000)
 20.
A. Hyvarinen, J. Karhunen, E. Oja, Independent component analysis (Wiley, New York, 2001)
 21.
T.W. Lee, Indepndent component analysis: theory and applications (Kluwer Academic, Boston, 1998)
 22.
P. Comon, C. Jutten, Handbook of blind source separation (Academic Press, Salt Lake City, 2010)
 23.
N. Epain, C.T. Jin, Independent component analysis using spherical microphone arrays. Acta. Acust. United Acust. 98, 91–102 (2012)
 24.
Noohi, T.; Epain, N.; Jin, C.T. Direction of arrival estimation for spherical microphone arrays by combination of independent component analysis and sparse recovery. In Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, Vancouver, BC, Canada, 26–30 May 2013; Volume 32, pp. 346–349.
 25.
Noohi, T.; Epain, N.; Jin, C.T. Superresolution acoustic imaging using sparse recovery with spatial priming. In Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, South Brisbane, Australia, 19–24 April 2015; pp. 2414–2418.
 26.
D. Pavlidi, A. Griffin, M. Puigt, A. Mouchtaris, Realtime multiple sound source localization and counting using a circular microphone array. IEEE Trans. Audio Speech Lang. Process. 21(10), 2193–2206 (2013)
 27.
O. Yilmaz, S. Rickard, Blind separation of speech mixtures via timefrequency masking. IEEE Transact. signal Process 52(7), 1830–1847 (2004)
 28.
M. Jia, J. Sun, C. Bao, Realtime multiple sound source localization and counting using a soundfield microphone. J. Ambient. Intell. Humaniz. Comput. 8(6), 829–844 (2017)
 29.
S. Mohan, M.E. Lockwood, M.L. Kramer, D.L. Jones, Localization of multiple acoustic sources with small arrays using a coherence test. J. Acoust. Soc. Am. 123(4), 2136–2147 (2008)
 30.
C. Avendano and J. M. Jot, “Ambience extraction and synthesis from stereo signals for multichannel audio upmix,” in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP'02), Orlando, Florida, USA, May 2002.
 31.
A. AissaElBey, N. LinhTrung, K. AbedMeraim, A. Belouchrani, Y. Grenier, Underdetermined blind separation of nondisjoint sources in the timefrequency domain. IEEE Transact. Signal Process 55(3), 897–907 (2007). https://doi.org/10.1109/TSP.2006.888877
 32.
H. Zhang, G. Hua, L. Yu, Y. Cai, G. Bi, Underdetermined blind separation of overlapped speech mixtures in timefrequency domain with estimated number of sources. Speech Comm. 89, 1–16 (2017)
 33.
K. Wu, V. G. Reju and A. W. H. Khong, “Multisource DOA estimation in a reverberant environment using a single acoustic vector sensor,” in IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 26, no. 10, pp. 18481859, 2018, doi: https://doi.org/10.1109/TASLP.2018.2845121.
 34.
M. Cobos, J.J. Lopez, D. Martinez, Twomicrophone multispeaker localization based on a Laplacian mixture model. Digital Signal Process 21(1), 66–76 (2011)
 35.
M. Jia, Y. Wu, C. Bao, J. Wang, Multiple sound sources localization with framebyframe component removal of statistically dominant source. Sensors 18(11), 3613 (2018) 121
 36.
NTT database, 2008. More information see as http://www.nttat.com/product. Accessed 2009
 37.
Gao S, Jia M, Wu Y, et al. Multiple sound sources localization by using statistical source component equalization[C]// ICCPR ‘19: 2019 8th International Conference on Computing and Pattern Recognition. 2019.
 38.
B. Gunel, H. Hacihabiboglu, A.M. Kondoz, Acoustic source separation of convolutive mixtures based on intensity vector statistics. IEEE Trans. Audio Speech Lang. Process. 16(4), 748–756 (2008)
 39.
D.R. Campbell, K.J. Palomki, G.J. Brown, A matlab simulation of “shoebox” room acoustics for use in research and teaching. Comput Inf Syst J 9, 48–51 (2005)
Acknowledgements
Not applicable.
Funding
This work was supported by the National Natural Science Foundation of China under Grants (No. 61971015, 61831019) and the Cooperative Research Project of BJUTNTUT (No. NTUTBJUT11005).
Author information
Affiliations
Contributions
Gao S performed the whole research and wrote the paper. Jia M provided support to the writing and experiments. Bao C supervised the research. The authors read and approved the final manuscript.
Corresponding author
Ethics declarations
Competing interests
The authors declare that they have no competing interests.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Jia, M., Gao, S. & Bao, C. Multisource localization by using offset residual weight. J AUDIO SPEECH MUSIC PROC. 2021, 23 (2021). https://doi.org/10.1186/s1363602100211w
Received:
Accepted:
Published:
DOI: https://doi.org/10.1186/s1363602100211w
Keywords
 Multiple sound sources localization
 Direction of arrival estimation
 Reverberation
 Soundfield microphone