In this paper, a method for modeling distance dependent head-related transfer functions is presented. The HRTFs are first decomposed by spatial principal component analysis. Using deep neural networks, we model the spatial principal component weights of different distances. Then we realize the prediction of HRTFs in arbitrary spatial distances. The objective and subjective experiments are conducted to evaluate the proposed distance model and the distance variation function model, and the results have shown that the proposed model has less spectral distortions than distance variation function model, and the virtual sound generated by the proposed model has better performance in terms of distance localization.

The Ambisonic technique has been widely used for soundfield recording and reproduction recently. However, the basicAmbisonic decoding method will break down when the play-back loudspeakers distribute unevenly. Various methods havebeen proposed to solve this problem. This paper introducesseveral improvements to a recently proposed Ambisonic de-coding method, the matching projection method, for unevenloudspeaker layouts. The first improvement is energy preserv-ing; the second is introducing the “in-phase” weight, and thethird is introducing partial projection coefficients. To eval-uate the improved method, we compared it with the origi-nal one and the all-round Ambisonic decoding method witha 2-dimension unevenly arranged loudspeaker array. The re-sult shows our method greatly improves the original methodwhere the loudspeaker arranges very sparsely or densely.

The traditional multiple audio objects codec extracts the parameters of each object in the frequency domain and produces serious confusion because of high coincidence degree in subband among objects. This paper uses sparse domain instead of frequency domain and reconstruct audio object using the binary mask from the down-mixed signal based on the sparsity of each audio object. In order to overcome high coincidence degree of subband among different audio objects, the sparse autoencoder neural network is established. On this basis, a multiple audio objects codec system is built up. To evaluate this proposed system, the objective and subjective evaluation are carried on and the results show that the proposed system has the better performance than SAOC.