## 1 Introduction  
In any enclosed acoustic space, the sound received by a listener is the superposition of the direct sound from the source and the reflected sounds from the surrounding surfaces. The numerous reflections termed reverberation cause persistence of sound even after the source ceases, until these reflected waves decay due to absorption by the surrounding surfaces. The intricate sound field generated by these reflected waves provides the sense of acoustic space to the perceived sound. However, severe reverberation can cause spectral distortions and reduce speech intelligibility. The study of reverberation is complicated since it is a product of many factors like sound frequency, room shape, room size, room geometry, source and receiver locations, source and receiver directivity, etc. A comprehensive understanding of the reflection sound field distribution, resonant frequencies, and modal decay rates is necessary to control audible artifacts and achieve desired sound perception quality in room acoustic applications. Initially, the objective parameters like reverberation time, percentage articulation (PA) [1], decay rates [2], and statistical measures of room impulse responses (RIR) [3] were the only measures of reverberation. However, later studies [4; 5] found that these measures vary with the sound frequency and wall surface properties. This necessitated the frequency-dependent spatio-temporal analysis of sound fields for accurate characterization of room acoustics. The existing 3D room acoustic parameter estimation methods either depend on predictions based on computational acoustics or derive the parameters directly from real sound field measurements. The room acoustic analysis using prominent computational models like ray/geometrical [6; 7], wave/element [8], statistical energy [9], or synthetic RIR [10; 11] methods are computationally complex and applicable to limited frequency ranges. The lack of proper consideration of the source and environment factors, frequency-dependent wave behavior, and precise reflection methods reduce the estimation accuracy of these computational approaches, especially in highly reverberant environments [12]. Furthermore, the analysis of intermediate frequencies using these computational models is complicated because of the dominant diffraction effects and the influence of both wave and ray acoustic behaviors. The characterization of real acoustic environments requires 3D acoustic scene analysis using spatial sound field measurements. This led to the development of several microphone arrays designs [13; 14; 15] and processing methods like sound intensity mapping [16], plane-wave decomposition (PWD) and steered beamforming [17; 18; 19], sound intensity vector analysis [20], and multi-channel correlation model [21]. [PERSON] et al. used PWD beamforming in [18] to estimate the angular distribution and anisotropy index of the spatial sound field from the RIRs recorded by a spherical microphone array. The recent works in [22; 23; 24] allow similar analysis in terms of isotropy measures and directional energy decays using Schroeder integration [25] and PWD of directional RIRs. However, these methods require a large number of RIR measurements for an accurate analysis of the room acoustic field. This problem was overcome with the introduction of higher-order spherical harmonic (eigenbeam)-based processing of spherical microphone array measurements [12; 26; 27; 28], which provided higher spatial resolution for analysis compared to the previous methods. Subsequently, more robust techniques [29; 30; 31] were developed to achieve efficient parameterization of the spatial sound field using modal decomposition. In [32], the eigenbeam rotational invariance technique (EB-SPRIT) was used to identify room modes and damping parameters from RIRs. In [33; 34], [PERSON] et al.