The compression of three-dimensional sound field signals has always been a very important issue. Recently, an Independent Component Analysis (ICA) based Higher Order Ambisonics (HOA) compression method introduces blind source separation to solve the shortcomings of discontinuity between frames in the existing Singular Value Decomposition (SVD) based methods. However, ICA is weak to model the reverberant environment, and its target is not to recover original signal. In this work, we replace ICA with autoencoder to further improve the above method’s ability to cope with reverberation conditions and ensure the unanimous optimization both in separation and recovery by reconstruction loss. We constructed a dataset with simulated and recorded signals, and verified the effectiveness of our method through objective and subjective experiments.

In recent years, many researches focus on sound source localization based on neural networks, which is an appealing but difficult problem. In this paper, a novel time-domain end-to-end method for sound source localization is proposed, where the model is trained by two strategies with both cross entropy loss and mean square error loss. Based on the idea of multi-task learning, CNN is used as the shared hidden layers to extract features and DNN is used as the output layers for each task. Compared with SRP-PHAT, MUSIC and a DNN-based method, the proposed method has better performance.

The traditional eigen beam based localization algorithms are usually not employed on the non-spherical microphone array, for which the eigen beam is hard to be obtained. In this paper, the transfer functions are introduced to calculated the eigen beam on the non-spherical microphone array. Based on it, three localization algorithms including the eigen beam based intensity vector, eigen beam based beamforming, eigen beam based MUSIC, are employed and their performance on localization are evaluated.