Auditory Attention Decoding (AAD) can help to determine the identity of the attended speaker during an auditory selective attention task, by analyzing and processing measurements of electroencephalography (EEG) data. Most studies on AAD are based on scalp-EEG signals in two-speaker scenarios, which are far from real application. Ear-EEG has recently gained significant attention due to its motion tolerance and invisibility during data acquisition, making it easy to incorporate with other devices for applications. In this work, participants selectively attended to one of the four spatially separated speakers’ speech in an anechoic room. The EEG data were concurrently collected from a scalp-EEG system and an ear-EEG system (cEEGrids). Temporal response functions (TRFs) and stimulus reconstruction (SR) were utilized using ear-EEG data. Results showed that the attended speech TRFs were stronger than each unattended speech and decoding accuracy was 41.3% in the 60s (chance level of 25%). To further investigate the impact of electrode placement and quantity, SR was utilized in both scalp-EEG and ear-EEG, revealing that while the number of electrodes had a minor effect, their positioning had a significant influence on the decoding accuracy. One kind of auditory spatial attention detection (ASAD) method, STAnet, was testified with this ear-EEG database, resulting in 93.1% in 1-second decoding window. The implementation code and database for our work are available on GitHub: https://github.com/zhl486/Ear_EEG_code.git and Zenodo: https://zenodo.org/records/10803261.

The Ambisonic technique has been widely used in fields such as sound-field reproduction, sound source localization, and acoustic noise control. In this paper, a recently proposed Ambisonic decoding method, the partially matching projection method (PMPD), was described in detail and extended for more practical use with near-field sources and reverberant playback environments. Several experiments were conducted to evaluate the performance of the method. In the experiments, we compared performances of PMPD method with three other Ambisonic decoding methods, “All-round Ambisonic Decoding” (AllRAD) method, mode matching decoding (MMD) method and regularized mode matching decoding (RMMD) method. Different playback conditions were considered to investigate the generalization of the method, including unevenly and evenly distributed loudspeakers, as well as free-field and reverberant playback environments. In terms of reproduction errors near sweep-spot and energy of loudspeaker gains, PMPD method worked well for different signals and playback environments. Whereas the other three methods encontered ill-posed problems or large reproduction errors in certain conditions. This means that PMPD method achieves a balence between precise reproductioin near sweet-spot and energy of loudspeaker gains with ill-posed problems. PMPD method has a more robust performance than the other three compared methods for different signals and playback environments. And we conclude that PMPD method is effective to be used as an Ambisonic decoding method, especially for different playback environments.

Head-related transfer function (HRTF) plays an important role in the construction of 3D auditory display. This article presents an individual HRTF modeling method using deep neural networks based on spatial principal component analysis. The HRTFs are represented by a small set of spatial principal components combined with frequency and individual-dependent weights. By estimating the spatial principal components using deep neural networks and mapping the corresponding weights to a quantity of anthropometric parameters, we predict individual HRTFs in arbitrary spatial directions. The objective and subjective experiments evaluate the HRTFs generated by the proposed method, the principal component analysis (PCA) method, and the generic method. The results show that the HRTFs generated by the proposed method and PCA method perform better than the generic method. For most frequencies the spectral distortion of the proposed method is significantly smaller than the PCA method in the high frequencies but significantly larger in the low frequencies. The evaluation of the localization model shows the PCA method is better than the proposed method. The subjective localization experiments show that the PCA and the proposed methods have similar performances in most conditions. Both the objective and subjective experiments show that the proposed method can predict HRTFs in arbitrary spatial directions.

The Ambisonic technique has been widely used for sound field recording and reproduction recently. However, the basic Ambisonic decoding method will break down when the playback loudspeakers distribute unevenly. Various methods have been proposed to solve this problem. This paper introduces several improvements to a recently proposed Ambisonic decoding method, the matching projection method, for uneven loudspeaker layouts. The first improvement is energy preserving; the second is introducing the "in-phase" weight, and the third is introducing partial projection coefficients. To evaluate the improved method, we compared it with the original one and the all-round Ambisonic decoding method with a 2-dimension unevenly arranged loudspeaker array. The result shows our method greatly improves the original method where the loudspeaker arranges very sparsely or densely.