A graphic delay equalizer based on a high-order nonparametric allpass filter design is proposed. Command points at the centers of octave frequency bands are connected with polynomial interpolation to form a continuous target group-delay curve as function of frequency. The required number of all-pass sections depends on the area under the target curve. The group-delay area at low audio frequencies is small due to the linear frequency scale, so only a few allpass sections can be assigned there, which reduces the accuracy. Design accuracy can be improved by adding a constant delay to the target curve, which increases the area. Two use cases are presented: the group-delay equalization of a multi-way loudspeaker and the linearization of the phase response of a regular magnitude-only graphic equalizer. The graphic delay equalizer can be used to enhance the perceptual quality of audio systems or to produce audio effects.

This paper describes a neural network based method to simplify the design of a graphic equalizer without sacrificing the accuracy of approximation. The key idea is to train a neural network to predict the mapping from target gains to the optimized band filter gains at specified center frequencies. The prediction is implemented with a feedforward neural network having a hidden layer with 20 neurons in the case of the ten-octave graphic equalizer. The band filter coefficients can then be quickly and easily computed using closed-form formulas. This work turns, for the first time, the accurate graphic equalization design into a feedforward calculation without matrix inversion or iterations. The filter gain control using the neural network reduces the computing time by 99.6% in comparison to the least-squares design method it is imitating and contributes an approximation error of less than 0.1 dB. The resulting neurally controlled graphic equalizer will be highly useful in various audio and music processing applications, which require time-varying equalization.

This letter proposes a real-time perceptual model predicting the experienced distraction occurring in interfering audio-on-audio situations. The proposed model improves the computational efficiency of a previous distraction model, which cannot provide predictions in real time. The chosen approach was to utilize similar features as the previous model, but to use faster underlying algorithms to calculate these features. The results show that the proposed model has a root mean squared error of 11.9%, compared to the previous model's 11.0%, while only taking 0.04% of the computational time of the previous model. Thus, while providing similar accuracy as the previous model, the proposed model can be run in real time. The proposed distraction model can be used as a tool for evaluating and optimizing sound-zone systems. Furthermore, the real-time capability of the model introduces new possibilities, such as adaptive sound-zone systems.

A high-order graphic equalizer has the advantage that the gain in one band is highly independent of the gains in the adjacent bands. However, all practical filters have transition bands, which interact with the adjacent bands and create errors in the desired magnitude response. This letter proposes a filter optimization algorithm for a high-order graphic equalizer, which minimizes the errors in the transition bands by iteratively optimizing the orders of adjacent band filters. The optimization of the filter order affects the shape of the transition band, thus enabling the search for the optimum shape relative to the adjacent filter. The optimization is done offline, and during filtering only the gains of the band filters are altered. In an example case, the proposed method was able to meet the given peak-error limitations of ±2 dB, when the total order of the graphical equalizer was 328, whereas the non-optimized filter could not meet the requirements even when the total order was raised to 672. Optimized high-order graphical equalizers can be widely used in audio signal processing applications.

This paper proposes a high-precision graphic equalizer based on second-order parallel filters. Previous graphic equalizers suffer from interaction between adjacent band filters, especially at high gain values, which can lead to substantial errors in the magnitude response. The fixed-pole design of the proposed parallel graphic equalizer avoids this problem, since the parallel second-order filters are optimized jointly. When the number of pole frequencies is twice the number of command points of the graphic equalizer, the proposed non-iterative design matches the target curve with high precision. In the three example cases presented in this paper, the proposed parallel equalizer clearly outperforms other non-iterative graphic equalizer designs, and its maximum global error is as low as 0.00-0.75 dB when compared to the target curve. While the proposed design has superior accuracy, the number of operations in the filter structure is increased only by 23% when compared to the second-order Regalia-Mitra structure. The parallel structure also enables the utilization of parallel computing hardware, which can nowadays easily outperform the traditional serial processing. The proposed graphic equalizer can be widely used in audio signal processing applications.

An adaptive perceptual equalizer for headphones is introduced. It estimates the effect of auditory masking while considering the characteristics of the headphones, ambient noise, and music. The system utilizes a psychoacoustic masking model to estimate the level to which the music should be raised to have the same perceived tonal balance in noise as it has in a quiet environment. Prototype testing showed that the most important task is to make the music audible in each Bark band. The compensation of the partial masking further improves the perceived sound quality. The system uses a microphone of a headset to capture the ambient noise. The equalization is implemented using a high-order graphical equalizer that does not require subband decomposition of the music signal. The proposed equalizer also retains reasonable SPL levels: in an example case, the maximum gain in one Bark band was 11 dB while the overall SPL increase was only 2.5 dB.