Ambisonics Networks -- The Effect Of Radial Functions Regularization (2402.18968v1)

Abstract: Ambisonics, a popular format of spatial audio, is the spherical harmonic (SH) representation of the plane wave density function of a sound field. Many algorithms operate in the SH domain and utilize the Ambisonics as their input signal. The process of encoding Ambisonics from a spherical microphone array involves dividing by the radial functions, which may amplify noise at low frequencies. This can be overcome by regularization, with the downside of introducing errors to the Ambisonics encoding. This paper aims to investigate the impact of different ways of regularization on Deep Neural Network (DNN) training and performance. Ideally, these networks should be robust to the way of regularization. Simulated data of a single speaker in a room and experimental data from the LOCATA challenge were used to evaluate this robustness on an example algorithm of speaker localization based on the direct-path dominance (DPD) test. Results show that performance may be sensitive to the way of regularization, and an informed approach is proposed and investigated, highlighting the importance of regularization information.

• The paper demonstrates that regularization in Ambisonics encoding directly influences DNN-based speaker localization, revealing a trade-off between noise suppression and signal distortion.
• It details the use of Tikhonov regularization, emphasizing that optimal parameterization is crucial for reducing low-frequency noise while maintaining signal integrity.
• Informed algorithms that integrate regularization data significantly enhance DOA estimation accuracy, suggesting robust strategies for spatial audio applications in complex environments.

Exploring Regularization Techniques in Ambisonics for Speaker Localization Algorithms

Introduction to Ambisonics and Regularization Techniques

Ambisonics has become an increasingly popular method for capturing and reproducing spatial audio, especially with the advent of virtual reality (VR) and augmented reality (AR) technologies. One critical challenge in the Ambisonics encoding process, particularly when using spherical microphone arrays, involves the amplification of noise at low frequencies due to division by small-magnitude radial functions. Regularization techniques have been proposed to mitigate this issue, albeit with the trade-off of introducing errors in the encoded Ambisonics signals. This paper investigates the influence of different regularization techniques on the training and performance of Deep Neural Networks (DNNs) utilized for speaker localization, aiming to understand the robustness of these networks to varying levels of regularization.

Ambisonics Signal Encoding and Regularization

The process of encoding Ambisonics signals from spherical array data using regularized Plane-Wave Decomposition (PWD) is discussed, emphasizing the compromise between minimizing noise and preserving signal integrity. Specifically, the paper explains how regularization affects Ambisonics signals in terms of noise amplification and distortion, utilizing the Tikhonov regularization method as a focal example due to its effective parameterization. The paper underscores the impact of different regularization choices, showing that while noise can be suppressed with an appropriate selection of the regularization matrix, the accuracy in estimating Ambisonics signals may be compromised.

Speaker Localization Using DNN-DPD Algorithm

An illustrative application involving a speaker localization algorithm based on DNN, termed DNN-DPD, is evaluated to demonstrate the sensitivity of Ambisonics algorithms to varying regularization levels. This algorithm extends the Direct Path Dominance (DPD) test method by incorporating neural networks to classify input features based on Ambisonics signals as containing direct sound, which is then utilized for Direction Of Arrival (DOA) estimation. The paper's experimental investigation explores the effect of Ambisonics regularization on the DNN-DPD algorithm, contrasting the performance of informed and uninformed algorithms in simulations and real data from the LOCATA challenge.

Experimental Findings and Implications

The research findings reveal a direct correlation between the level of regularization employed during the Ambisonics encoding process and the performance of the DNN-DPD algorithm in speaker localization tasks. Notably, the paper illustrates how stronger regularization adversely affects the algorithm's accuracy, particularly at lower frequencies where distortion is more prevalent. Furthermore, incorporating regularization information into the model (the informed algorithm) demonstrates significant improvements in performance, underlining the value of leveraging regularization knowledge in Ambisonics networks.

Concluding Remarks

The implications of this research are twofold: firstly, it highlights the critical sensitivity of DNN-based Ambisonics algorithms to the specifics of regularization techniques employed in spatial audio encoding. Secondly, it posits that an informed approach, where the regularization information is integrated into the network, can enhance the robustness and performance of Ambisonics networks. These insights pave the way for further exploration into regularization-informed approaches for a broader range of applications within Ambisonics networks, potentially advancing the domain of spatial audio processing in complexity-ridden environments like reverberant rooms. Future investigations could extend these findings to other Ambisonics-based tasks beyond speaker localization, further refining the synergy between spatial audio encoding and machine learning algorithms.