Simiao Chen

Sparse Bayesian learning (SBL) is widely used for sound field reconstruction (SFR). Among various SBL approaches, pattern-coupled SBL has been demonstrated to achieve superior performance. Building on the pattern-coupled SBL framework, this study replaces matrix multiplication with tensor and matrix cross-correlation operations, significantly reducing the algorithm’s spatial and temporal complexity. Furthermore, we compare the performance of different coupling structures within the pattern-coupled SBL method for reconstructing room impulse responses (RIRs) in the time domain. Specifically, we analyze nine coupling structures that incorporate both temporal and spatial coupling terms and validate their performance via experiments using two datasets. The results indicate that the coupling structure known as CPC-ST (Centered Pattern-Coupled with Spatio-Temporal Coupling) achieves the best performance, especially in the extrapolation of the sound field. For lightweight systems, where a slight performance trade-off is acceptable, the coupling structure known as CPC-S (Centered Pattern-Coupled with Spatial Coupling) is also recommended due to its balance between effectiveness and simplicity.

This work presents a pattern-coupled structured sparse Bayesian learning method for reconstructing room impulse responses (RIRs) in the time domain. It exploits the temporal properties of RIRs for improved reconstruction performance. Existing Bayesian methods with time-dependent regularization exploit the physical knowledge that an RIR transitions from a highly-sparse early part to a non-sparse later part. Building on this foundation, the proposed method utilizes a pattern-coupled hierarchical Gaussian prior in both spatial and temporal dimensions, employing a triangular mesh for the spatial dimension. The inverse problem is solved via stochastic variational inference. Performance assessments with experimental measurements illustrate the effectiveness of the proposed method.

Miniature loudspeakers exhibit pronounced nonlinear characteristics due to physical size limitations and the demand for sufficient sound pressure. The primary nonlinear properties, including the force factor, mechanical stiffness, and mechanical resistance, depend on the displacement or velocity of the voice coil. The creep effect in the loudspeaker’s suspension system, which significantly affects low-frequency displacement, is often neglected in existing studies on nonlinear dynamics. This study enhances the modeling of miniature loudspeakers by incorporating the creep effect into an extended nonlinear model. The voice coil displacement and total harmonic distortion (THD) predicted by the extended model were validated against experimental data. The displacement and THD values predicted by the nonlinear model in consideration of the creep effect corresponded closely to the actual measurement values, substantiating the effectiveness and precision of the model.

Total harmonic distortion (THD) plays an important role in the performance of miniature loudspeakers used in mobile phones. Traditional nonlinear models fail to predict THD in the higher frequency domain. Significant discrepancies can be found between the prediction and measurement. In this study, an extended model considering nonlinear acoustic damping is proposed to address this issue. Flow separation occurring near the orifices of the sound port is of particular interest. Two velocity-dependent parameters were derived to model the nonlinear damping effect. Based on the extended model, the dynamics of miniature loudspeakers are described using the state-space method. Experiments and calculations confirm the validity of the extended model. The influence of nonlinear acoustic damping on THD is also discussed.