Adaptive Volterra filters (AVFs) are usually used to identify nonlinear systems, such as loudspeaker systems, and ordinary adaptive algorithms can be used to update the filter coefficients of AVFs. However, AVFs require huge computational complexity even if the order of the AVF is constrained to the second order. Improving calculation efficiency is therefore an important issue for the real-time implementation of AVFs. In this paper, we propose a novel sub-band AVF with high calculation efficiency for second-order AVFs. The proposed sub-band AVF consists of four parts: input signal transformation for a single sub-band AVF, tap length determination to improve calculation efficiency, switching the number of sub-bands while maintaining the estimation accuracy, and an automatic search for an appropriate number of sub-bands. The proposed sub-band AVF can improve calculation efficiency for which the dominant nonlinear components are concentrated in any frequency band, such as loudspeakers. A simulation result demonstrates that the proposed sub-band AVF can realize higher estimation accuracy than conventional efficient AVFs.

In this paper, we propose a 3rd-order nonlinear IIR filter for compensating nonlinear distortions of loudspeaker systems. Nonlinear distortions are common around the lowest resonance frequency for electrodynamic loudspeaker systems. One interesting approach to compensating nonlinear distortions is to employ a mirror filter. The mirror filter is derived from the nonlinear differential equation for loudspeaker systems. The nonlinear parameters of a loudspeaker system, which include the force factor, stiffness, and so forth, depend on the displacement of the diaphragm. The conventional filter structure, which is called the 2nd-order nonlinear IIR filter that originates the mirror filter, cannot reduce nonlinear distortions at high frequencies because it does not take into account the nonlinearity of the self-inductance of loudspeaker systems. To deal with this problem, the proposed filter takes into account the nonlinearity of the self-inductance and has a 3rd-order nonlinear IIR filter structure. Hence, this filter can reduce nonlinear distortions at high frequencies while maintaining a lower computational complexity than that of a Volterra filter-based compensator. Experimental results demonstrate that the proposed filter outperforms the conventional filter by more than 2dB for 2nd-order nonlinear distortions at high frequencies.

In this paper, we propose a parameter estimation method using Volterra kernels for the nonlinear IIR filters, which are used for the linearization of closed-box loudspeaker systems. The nonlinear IIR filter, which originates from a mirror filter, employs nonlinear parameters of the loudspeaker system. Hence, it is very important to realize an appropriate estimation method for the nonlinear parameters to increase the compensation ability of nonlinear distortions. However, it is difficult to obtain exact nonlinear parameters using the conventional parameter estimation method for nonlinear IIR filter, which uses the displacement characteristic of the diaphragm. The conventional method has two problems. First, it requires the displacement characteristic of the diaphragm but it is difficult to measure such tiny displacements. Moreover, a laser displacement gauge is required as an extra measurement instrument. Second, it has a limitation in the excitation signal used to measure the displacement of the diaphragm. On the other hand, in the proposed estimation method for nonlinear IIR filter, the parameters are updated using simulated annealing (SA) according to the cost function that represents the amount of compensation and these procedures are repeated until a given iteration count. The amount of compensation is calculated through computer simulation in which Volterra kernels of a target loudspeaker system is utilized as the loudspeaker model and then the loudspeaker model is compensated by the nonlinear IIR filter with the present parameters. Hence, the proposed method requires only an ordinary microphone and can utilize any excitation signal to estimate the nonlinear parameters. Some experimental results demonstrate that the proposed method can estimate the parameters more accurately than the conventional estimation method.

Earlier attempts to deploy two units of parametric loudspeakers have shown encouraging results in improving the accuracy of spatial audio reproductions. As compared to a pair of conventional loudspeakers, this improvement is mainly a result of being free of crosstalk due to the sharp directivity of the parametric loudspeaker. By replacing the normal parametric loudspeaker with the steerable parametric loudspeaker, a flexible sweet spot can be created that tolerates head movements of the listener. However, spatial aliasing effects of the primary frequency waves are always observed in the steerable parametric loudspeaker. We are motivated to make use of the spatial aliasing effects to create two sound beams from one unit of the steerable parametric loudspeaker. Hence, a reduction of power consumption and physical size can be achieved by cutting down the number of loudspeakers used in an audio system. By introducing a new parameter, namely the relative steering angle, we propose a stereophonic beamsteering method that can control the amplitude difference corresponding to the interaural level difference (ILD) between two sound beams. Currently, this proposed method does not support the reproduction of interaural time differences (ITD).

This work presents a four-channel headset achieving a 5.1-channel-like hearing experience using a low-complexity head-related transfer function (HRTF) model and a simplified reverberator. The proposed down-mixing architecture enhances the sound localization capability of a headset using the HRTF and by simulating multiple sound reflections in a room using Moorer's reverberator. Since the HRTF has large memory and computation requirements, the common-acoustical-pole and zero (CAPZ) model can be used to reshape the lower-order HRTF model. From a power consumption viewpoint, the CAPZ model reduces computation complexity by approximately 40%. The subjective listening tests in this study shows that the proposed four-channel headset performs much better than stereo headphones. On the other hand, the four-channel headset that can be implemented by off-the-shelf components preserves the privacy with low cost.

We propose an active noise control (ANC) system for reducing periodic noise generated in a high magnetic field such as noise generated from magnetic resonance imaging (MRI) devices (MR noise). The proposed ANC system utilizes optical microphones and piezoelectric loudspeakers, because specific acoustic equipment is required to overcome the high-field problem, and consists of a head-mounted structure to control noise near the user's ears and to compensate for the low output of the piezoelectric loudspeaker. Moreover, internal model control (IMC)-based feedback ANC is employed because the MR noise includes some periodic components and is predictable. Our experimental results demonstrate that the proposed ANC system (head-mounted structure) can significantly reduce MR noise by approximately 30 dB in a high field in an actual MRI room even if the imaging mode changes frequently.

In this letter, the compensation ability of nonlinear distortions for loudspeaker systems is demonstrated using dynamic distortion measurement. Two linearization methods using a Volterra filter and a Mirror filter are compared. The conventional evaluation utilizes swept multi-sinusoidal waves. However, it is unsatisfactory because wideband signals such as those of music and voices are usually applied to loudspeaker systems. Hence, the authours use dynamic distortion measurement employing a white noise. Experimental results show that the two linearization methods can effectively reduce nonlinear distortions for wideband signals.

In this paper, we propose a low complexity realization method for compensating for nonlinear distortion. Generally, nonlinear distortion is compensated for by a linearization system using a Volterra kernel. However, this method has a problem of requiring a huge computational complexity for the convolution needed between an input signal and the 2nd-order Volterra kernel. The Simplified Volterra Filter (SVF), which removes the lines along the main diagonal of the 2nd-order Volterra kernel, has been previously proposed as a way to reduce the computational complexity while maintaining the compensation performance for the nonlinear distortion. However, this method cannot greatly reduce the computational complexity. Hence, we propose a subband linearization system which consists of a subband parallel cascade realization method for the 2nd-order Volterra kernel and subband linear inverse filter. Experimental results show that this proposed linearization system can produce the same compensation ability as the conventional method while reducing the computational complexity.