Byeongho Jo

With few snapshots, minimum-variance distortionless-response beamforming on spherical microphone arrays loses white noise gain (WNG) and cancels its target. A Kantorovich bound that sets diagonal loading for a target WNG, applied to the spherical-harmonic modal covariance, can miss the spatial WNG by up to the squared modal-transform condition number. A computable rule searches the identity-loaded modal family for the least loading meeting a spatial-WNG floor and reports infeasibility. The exact spatial-WNG-constrained solution loads the capsule covariance instead and meets the tested floors at every frequency. On a simulated Eigenmike at 4.5 kHz, the rule meets the floor, with equality when loading is needed, where the transferred bound realizes 0.44–0.80 for a requested 0.1, and at equal targets from 0.1 to 0.5 keeps 0.3–0.7 dB more signal-to-interference-plus-noise ratio and 0.6–1.4 dB more directivity than the exact loading. The lead grows at lower frequencies, vanishes from about 5.5 kHz, and ends at an interferer-dependent target where the loading collapses. On measured room responses, the rule meets the target at every snapshot count while the unloaded weight self-cancels under deficiency and residual steering mismatch.

The increasing demand for high-quality audio content in applications such as media streaming and immersive audio services has driven the development of efficient audio coding techniques. Among these techniques, entropy coding is an essential component for the lossless compression of quantized spectral coefficients, which constitute the largest portion of the compressed audio bitstream. As the latest audio coding standard, Unified Speech and Audio Coding (USAC) has adopted ContextAdaptive Arithmetic Coding (CAAC) to employ its entropy coding method. Although CAAC can provide efficient lossless coding, it has several limitations, including bit overhead caused by variations in bit lengths between coefficients and considerable memory requirements for storing multiple context tables. To reduce excessive memory consumption, this study proposes Dynamic Bit-plane Arithmetic Coding (DBAC) as a new entropy coding method for quantized spectral coefficients. DBAC mitigates the bit overhead by using escape symbols for binarized coefficients and reduces memory usage while maintaining coding performance, even with marginal coding gains. In addition, the generation and selection methods of newly designed multiple contexts are also introduced to optimize arithmetic coding on the proposed dynamic bit-plane of DBAC. Experimental results demonstrate that the proposed method achieves 30% memory reduction with comparable coding performance compared to USAC.

Complex-valued frequency-domain linear predictive coding (CLPC) has been developed for audio coding. Recently, representations for efficiently quantizing CLPC coefficients have been proposed, including the complex immittance spectral frequency (CISF). The CISF has limitations in that it requires signalling the sequential information to eliminate ambiguity and the highest-order coefficient (HOC) for reconstructing the CLPC coefficients. This study developed a modified CISF-based method that eliminates the need for additional information by utilizing intermediate complex polynomial properties. Furthermore, a perceptual-metric-based codebook search was proposed to improve quantization efficiency. The experimental results show robust quantization performance, while listening tests demonstrate superior audio quality compared to MPEG-D USAC long TCX at 12 kbps.

For decades, linear predictive (LP) analysis of the real-valued time-domain signals has been developed for speech coding, analysis, and synthesis. Recently, the complex-valued frequency-domain LP coding was developed to enhance the estimation performance of the temporal envelope. To apply it for audio coding, a suitable representation for the complex-valued frequency-domain LP coefficients (CLPC) is required before quantization, but there was no efficient way to represent it. To address the problem, we propose efficient CLPC representations that retain some useful properties of conventional LPC representations. Through quantitative and qualitative evaluations, we demonstrate that our proposed representations increase quantization efficiency and improve audio coding performance.

A 3D sound source localization (SSL) technique for multiple spherical arrays is proposed. Spherical arrays have been popularly used for direction of arrival (DoA) estimation, which serves as important prior knowledge for source separation, dereverberation, and binaural rendering for VR applications. In this work, a parametric 3D SSL technique is proposed that can detect multiple sources using multiple spherical array recordings. From the enriched information by using multiple arrays, we show that the parametric estimation of 3D positions is possible without scanning candidate positions in 3D space. To mitigate the problem of the ambiguous association between DoAs estimated from different arrays, a total covariance matrix including both auto- and cross-covariance matrices between arrays is utilized such that DoAs from arrays are automatically paired to uniquely localize 3D source positions. The joint parameter estimation is based on the generalized joint Schur decomposition of multiple matrices combined with a geometric projection robustly guiding the convergence of the proposed iteration algorithm. Simulations conducted in anechoic and reverberant room conditions reveal that the proposed technique can accurately determine multiple source positions in 3D space without ambiguities.

The direction-of-arrival (DoA) estimation from spherical microphone array data requires mode strength compensation for removing the dependency on the array surface impedance. However, in practice, mismatches exist in the actual and modeled surface impedances and degrade the DoA estimation performance. Herein, a non-parametric beamformer that can accurately estimate DoAs under the mode strength mismatch is proposed. The technique utilizes a block-diagonal covariance matrix and order-dependent normalization of spherical harmonic coefficients. The proposed method tested for various surface impedance mismatches and DoAs shows robust estimation performance with reduced dependency on the array surface impedance.

The estimation of direction of arrivals (DoAs) from spherical microphone array data is one of the key issues in extracting source information from all-around audio recordings. One such technique is the eigenbeam estimation of signal parameters via the rotational invariance technique (EB-ESPRIT), which separates the signal subspace related to the stationary sound field and then directly estimates DoAs of multiple sound sources. EB-ESPRIT has been evolved in many different ways by involving different types of recurrence relations of spherical harmonics, all of which are able to identify DoAs of a limited number of sources that are noticeably smaller than the number of finite-order spherical harmonic coefficients recorded. In this work, we report that it is possible to go beyond the known limits of detectable sources. The proposed formula is also based on conventional recurrence relations and probably permits to reach the ultimate limit by additional constraints of the signal parameters that can better exploit the highest-order coefficients. Monte-Carlo simulations conducted with various source positions and signal-to-noise ratios (SNRs) reveal that the proposed technique can detect more sources with insignificant loss in estimation performance and robustness.

The eigenbeam-ESPRIT (EB-ESPRIT) can directly identify the direction of arrivals (DoAs) of sound sources through the parametric estimation process. This parametric estimation is beneficial for identifying directions of early echoes in an enclosed space, because it does not need an extra peak searching step for finding dominant peak locations from a beamforming power map. Nevertheless, the use of EB-ESPRIT for echo localization has been hindered due to its ill-conditioning problem for sources positioned near the equator of the spherical coordinates. Room reflections in a reverberant room can impinge from any direction, and hence, the ill-conditioning is highly likely to occur. In this work, a nonsingular EB-ESPRIT technique is utilized for the echo localization without the ill-conditioning problem. The technique is based on new recurrence relations of spherical harmonics that express DoA parameters in form of sinusoidal functions. Therefore, the ill-conditioning due to the use of arctangent function can be avoided. The performance is demonstrated using the room impulse responses simulated and measured for a rigid spherical microphone array. Frequency smoothing is applied to localize echoes that are highly coherent to each other, and results demonstrate that accurate localization of echoes is possible irrespective of their incidence angles.

The eigenbeam-ESPRIT (EB-ESPRIT) is a parametric method that estimates the direction of arrival of source signals using a recurrence relation of spherical harmonics. In EB-ESPRIT, the sound-source elevation angle is estimated from an arctangent function, which diverges near the equator in the spherical coordinate system and inevitably induces an ill-conditioning problem. Here, a nonsingular spherical ESPRIT technique based on sine-based recurrence relations is proposed, in which the elevation angles are estimated from an arcsine function. It is shown that the proposed technique can estimate more sources than the conventional EB-ESPRIT technique while also avoiding ill-conditioning problems.