# Robust DOA Estimation Using Multi-Scale Fusion Network with Attention Mask
## 2 Related Work  
For indoor acoustic room impulse responses, there is the situation where an analysis with a short time frame contains multiple room reflections [26]. Acoustic reflections are highly correlated or even coherent, especially in a narrow frequency band. Subspace estimation algorithms and beamforming methods require that the source signals be independent, which is not true for highly correlated reflections. A number of smoothing methods have been developed to preprocess the array covariance matrix in frequency [27], time [28], or space [29] for spherical microphone array processing. Another major limitation of subspace algorithms is their sensitivity to noise. The orthogonality between the signal and noise subspaces is destroyed when the signal is perturbed by noise and subspace swapping occurs [30]. Maximum likelihood (ML) methods [31] handle coherent signals without smoothing operations, but they cannot easily localize multiple active sound sources simultaneously. To improve the estimation performance of the algorithm in highly reverberant environments, the direct-path dominance test (DPD-MUSIC) [32] was proposed. The algorithm identifies time-frequency bins (TF-bins) that contain the direct sound dominant of a target source, thus overcoming the problem of reverberant multipath distortion. However, most TF-bins that pass the test may carry false DOA information. The SHD-RMUSICalgorithm [33] improves robustness to noise by using noise-insensitive sound pressure values instead of original values. It does not work well at low signal-to-noise ratios (SNRs) and is not suitable for highly reverberant environments. Intensity vector-based DOA estimation methods [34] directly compute the direction of the energy flow rather than the spatial spectra, which can reduce the computational cost. But it is sensitive to phase mismatch and sensor noise. For spherical arrays, the acoustic field can be transformed into the spherical harmonic domain to generate pseudo intensity vectors (PIVs) [35], which are effective in estimating the direction of a single source in a noise-free environment. PIVs under non-ideal conditions were analyzed and subspace pseudo intensity vectors (SSPIVs) were proposed to reduce the effect of interference from the perspective of feature extraction. However, like array signal processing methods, the robustness is not sufficiently improved. With spherical harmonic decomposition, the magnitudes and phases of the spherical harmonic coefficients are fed into a CNN to obtain the classification of the DOA [36]. By converting the spherical harmonic function into the Kronecker product of two Vandermonde vectors, the uncoupled spherical harmonic azimuth feature (SHA) and spherical harmonic elevation (SHE) are extracted [37]. This results in a dimensional reduction of the input feature. Based on [36], an M-SVM was utilized to pre-classify the sound source into eight quadrants of 3D space. The azimuth and elevation are predicted between \(0^{\circ}\) and \(90^{\circ}\) by the CNN [38]. This approach offers superior noise and reverberation resistance through the use of spherical harmonic coefficients, while simultaneously reducing the complexity of the neural network by reducing the dimensionality of the input features. However, the use of RVNN limits the performance by overlooking the complex-valued property of the spherical harmonic coefficients. In recent years, CVNN has been widely used in signal processing and other fields [39; 40; 41; 42], such as speech enhancement and image segmentation. A speech signal, as a wave signal, has a phase component that represents the time difference and an amplitude that represents the energy of the wave [43]. CVNN can deal directly with the phase and amplitude components and is able to accurately model the phase rotation and amplitude decay features implicit in the signal on neural networks.