Sound field reconstruction (SFR) augments the information of a sound field captured by a microphone array. Using basis function decomposition, conventional SFR methods are straightforward and computationally efficient but may require more microphones than needed to measure the sound field. Recent studies show that pure data-driven and learning-based methods are promising in some SFR tasks, but they are usually computationally heavy and may fail to reconstruct a physically valid sound field. This paper proposes a compact acoustics-informed neural network (AINN) method for SFR, whereby the Helmholtz equation is exploited to regularize the neural network. As opposed to pure data-driven approaches that solely rely on measured sound pressures, the integration of the Helmholtz equation improves robustness of the neural network against variations during the measurement processes and prompts the generation of physically valid reconstructions. The AINN is designed to be compact and able to predict not only the sound pressures but also sound pressure gradients within a spatial region of interest based on measured sound pressures along the boundary. Experiments with acoustic transfer functions measured in different environments demonstrate the superiority of the AINN method over the traditional cylindrical harmonics and singular value decomposition methods.

This paper proposed multiple circular arrays of vector sensors for analyzing the three dimensional sound field. By exploiting the fact that a finite number of spatial basis functions can represent the sound field within a region, the designed arrays allow the analysis of the sound field around the arrays sequentially by either a frequency-domain method or a time- domain method. The arrays are compact and thus are suitable for integrating with small-sized devices. The time-domain method is of low latency and computationally simple, and thus are suitable for providing the devices with spatial acoustic features in real- time. The effectiveness of the proposed arrays and the sound field analysis methods are confirmed by simulations, and compared with another array.

The sound field separation methods can separate the target field from the interfering noises, facilitating the study of the acoustic characteristics of the target source, which is placed in a noisy environment. However, most of the existing sound field separation methods are derived in the frequency-domain, thus they are best suited for separating stationary sound fields. In this paper, a time-domain sound field separation method is developed that can separate the non-stationary sound field generated by the target source over a sphere in real-time. A spherical array sets up a boundary between the target source and the interfering sources, such that the outgoing field on the array is only generated by the target source. The proposed method decomposes the pressure and the radial particle velocity measured by the array into spherical harmonic coefficients, and recovers the target outgoing field based on the time-domain relationship between the decomposition coefficients and the theoretically derived spatial filter responses. Simulations show the proposed method can separate non-stationary sound fields both in free field and room environments, and over a longer duration with small errors. The proposed method could serve as a foundation for developing future time-domain spatial sound field manipulation algorithms.

This paper proposes a time-domain nearfield beamformer with spherical harmonic decomposition. The beamformer design is separated into two stages: sound field measurement and beamformer coefficient design. This makes it easier for the beamformer to be implemented by different array structures. The beamformer coefficients are further separated into several parts, making it easier to design a beamformer with different characteristics. The time-domain implementation minimizes the latency between the array input and the beamformer response, and the nearfield focusing improves the farfield noise suppression ability of the beamformer. The proposed beamformer could be useful for emerging acoustic applications such as virtual reality and drones, and for further development of more advanced real-time nearfield beamformers. Simulations and experiments examine the performance of the proposed beamformer.

One major issue of implementing broadband active noise control systems in reverberant rooms is the lack of reference signals. In this work, by exploiting the spatial sound field characteristics, a time-domain sound field separation method is developed to generate the reference signal for broadband active noise control systems in reverberant rooms. The time-domain sound field separation method separates the outgoing field produced by the primary source from the secondary source feedback and room reverberation on a spherical array, based on spherical harmonic decomposition of the sound field and the radial particle velocity measured by the array. Both the effectiveness of the time-domain sound field separation method and the applicability of the separated outgoing field as the reference signal for a broadband active noise control system in a reverberant room are demonstrated by simulations.