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Residual Learning for Neural Ambisonics Encoders

Thomas Deppisch, Yang Gao, Manan Mittal, Benjamin Stahl, Christoph Hold, David Alon, Zamir Ben-Hur

TL;DR

This paper tackles the challenge of accurate Ambisonics encoding for compact head-worn microphone arrays by proposing a residual-learning framework that refines a linear encoder with neural corrections. It compares a UNet-based encoder and a novel attention–RNN model (AmbiNet), both integrated in a residual setup, showing consistent in-domain gains and moderate out-of-domain improvements while high-frequency directional accuracy remains difficult. The study highlights the value of combining the robustness of linear encoding with neural refinements, especially when trained on realistic smartglasses ATFs and diverse in-domain/out-of-domain data, though it also notes persistent limitations due to sparse spatial sampling. Overall, residual learning emerges as a practical path to more reliable Ambisonic encoding in wearable devices, with potential applicability to future directional super-resolution and parametric encoding approaches.

Abstract

Emerging wearable devices such as smartglasses and extended reality headsets demand high-quality spatial audio capture from compact, head-worn microphone arrays. Ambisonics provides a device-agnostic spatial audio representation by mapping array signals to spherical harmonic (SH) coefficients. In practice, however, accurate encoding remains challenging. While traditional linear encoders are signal-independent and robust, they amplify low-frequency noise and suffer from high-frequency spatial aliasing. On the other hand, neural network approaches can outperform linear encoders but they often assume idealized microphones and may perform inconsistently in real-world scenarios. To leverage their complementary strengths, we introduce a residual-learning framework that refines a linear encoder with corrections from a neural network. Using measured array transfer functions from smartglasses, we compare a UNet-based encoder from the literature with a new recurrent attention model. Our analysis reveals that both neural encoders only consistently outperform the linear baseline when integrated within the residual learning framework. In the residual configuration, both neural models achieve consistent and significant improvements across all tested metrics for in-domain data and moderate gains for out-of-domain data. Yet, coherence analysis indicates that all neural encoder configurations continue to struggle with directionally accurate high-frequency encoding.

Residual Learning for Neural Ambisonics Encoders

TL;DR

This paper tackles the challenge of accurate Ambisonics encoding for compact head-worn microphone arrays by proposing a residual-learning framework that refines a linear encoder with neural corrections. It compares a UNet-based encoder and a novel attention–RNN model (AmbiNet), both integrated in a residual setup, showing consistent in-domain gains and moderate out-of-domain improvements while high-frequency directional accuracy remains difficult. The study highlights the value of combining the robustness of linear encoding with neural refinements, especially when trained on realistic smartglasses ATFs and diverse in-domain/out-of-domain data, though it also notes persistent limitations due to sparse spatial sampling. Overall, residual learning emerges as a practical path to more reliable Ambisonic encoding in wearable devices, with potential applicability to future directional super-resolution and parametric encoding approaches.

Abstract

Emerging wearable devices such as smartglasses and extended reality headsets demand high-quality spatial audio capture from compact, head-worn microphone arrays. Ambisonics provides a device-agnostic spatial audio representation by mapping array signals to spherical harmonic (SH) coefficients. In practice, however, accurate encoding remains challenging. While traditional linear encoders are signal-independent and robust, they amplify low-frequency noise and suffer from high-frequency spatial aliasing. On the other hand, neural network approaches can outperform linear encoders but they often assume idealized microphones and may perform inconsistently in real-world scenarios. To leverage their complementary strengths, we introduce a residual-learning framework that refines a linear encoder with corrections from a neural network. Using measured array transfer functions from smartglasses, we compare a UNet-based encoder from the literature with a new recurrent attention model. Our analysis reveals that both neural encoders only consistently outperform the linear baseline when integrated within the residual learning framework. In the residual configuration, both neural models achieve consistent and significant improvements across all tested metrics for in-domain data and moderate gains for out-of-domain data. Yet, coherence analysis indicates that all neural encoder configurations continue to struggle with directionally accurate high-frequency encoding.
Paper Structure (12 sections, 5 equations, 3 figures, 1 table)

This paper contains 12 sections, 5 equations, 3 figures, 1 table.

Table of Contents

  1. Introduction
  2. Problem Setup
  3. DNN Methods
  4. DNN Architectures
  5. Loss Functions
  6. Residual Learning
  7. Evaluation
  8. Datasets
  9. Model Setup and Training
  10. Evaluation Metrics
  11. Results
  12. Conclusion

Figures (3)

  • Figure 1: Proposed neural Ambisonics encoder architecture.
  • Figure 2: The proposed residual learning framework trains a neural encoder end-to-end to minimize a loss computed from the sum of the neural and linear encoder outputs.
  • Figure 3: Frequency-dependent coherence for the linear encoder (top) and the neural encoders with residual learning (solid lines) and without residual learning (dashed lines). Higher coherence suggests better reproduction of SH directivity patterns.