Enhancing Multi-Channel Speech with Limited Microphones via Spherical Harmonic Transform

The performance of traditional beamforming algorithms is influenced by the number of microphones, with performance improving as the number increases. However, in practice, the number of microphones is often limited. In this paper, we propose a novel virtual microphone estimation method that combines the strengths of both traditional and neural network-based approaches using the spherical harmonic transform (SHT), effectively addressing their respective limitations. Our method predicts the SHT coefficients at virtual positions and inversely transforms them into virtual speech signals, leveraging spatial information in the spherical harmonic domain for more accurate and effective virtual microphone estimation. Evaluations on the open MS-SNSD dataset demonstrate that the proposed method outperforms established baselines.