Augmented reality audio (ARA) combines virtual sound sources with the real sonic environment of the user. An ARA system can be realized with a headset containing binaural microphones. Ideally, the ARA headset should be acoustically transparent, that is, it should not cause audible modification to the surrounding sound. A practical implementation of an ARA mixer requires a lowlatency headphone reproduction system with additional equalization to compensate for the attenuation and the modified ear canal resonances caused by the headphones. This paper proposes digital IIR filters to realize the required equalization and evaluates a real-time prototype ARA system. Measurements show that the throughput latency of the digital prototype ARA system can be less than 1.4 ms, which is sufficiently small in practice. When the direct and processed sounds are combined in the ear, a comb filtering effect is brought about and appears as notches in the frequency response. The comb filter effect in speech and music signals was studied in a listening test and it was found to be inaudible when the attenuation is 20 dB. Insert ARA headphones have a sufficient attenuation at frequencies above about 1 kHz. The proposed digital ARA system enables several immersive audio applications, such as a virtual audio tourist guide and audio teleconferencing.

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This work proposes graphic equalizer designs with third-octave and Bark frequency divisions using symmetric band filters with a prescribed Nyquist gain to reduce approximation errors. Both designs utilize an iterative weighted least-squares method to optimize the filter gains, accounting for the interaction between the different band filters, to ensure excellent accuracy. A third-octave graphic equalizer with a maximum magnitude-response error of 0.81 dB is obtained, which outperforms the previous state-of-the-art design. The corresponding error for the Bark equalizer, which is the first of its kind, is 1.26 dB. This paper also applies a recently proposed neural gain control in which the filter gains are predicted with a multilayer perceptron having two hidden layers. After the training, the resulting network quickly and accurately calculates the filter gains for third-order and Bark graphic equalizers with maximum errors of 0.86 dB and 1.32 dB, respectively, which are not much more than those of the corresponding weighted least-squares designs. Computing the filter gains is about 100 times faster with the neural network than with the original optimization method. The proposed designs are easy to apply and may thus lead to widespread use of accurate auditory graphic equalizers.Appl. Sci. 2020, 10, 1222 2 of 22 using a neural network, which handled the optimization calculation [19]. The second paper extended the neurally-controlled equalizer (EQ) method to the widely used third-octave GEQ design [20], where the 31 EQ bands also loosely approximated the bandwidths of human auditory filters [21].Compared to our previous conference paper [20], this article presents an improved design method using second-order band filters with a symmetric shape on the logarithmic frequency scale, which was recently proposed for an octave GEQ [22]. The proposed design allows the parametric EQ used as band filters in the GEQ to have a controllable gain at the Nyquist limit, in contrast to the earlier designs that forced the Nyquist gain to one (i.e., 0 dB) [3]. Furthermore, this paper extends both the weighted least-squares (WLS) GEQ design method and the neurally-controlled EQ method to operate in Bark bands, which are a more accurate approximation of the human auditory resolution than the third-octave bands [23].Currently, two important metrics in GEQ design are accuracy and computational complexity. Both aspects have an impact when a GEQ is controlled by a computer in automated tasks without a human listener fine-tuning the gains, e.g., when equalizing music to be above a masking threshold in time varying noise [24,25]. Previous GEQs that are not very accurate (see the measurements, e.g., in [10]) have typically required a human operator to listen to the effect of the command gains as he/she controls them, since the accuracy of different command gain settings, as well as the visual feedback of the command gain sliders can be highly inaccurate. One of the goals of this work was to get the proposed neurally controlled GEQs to provide ...

An adaptive perceptual equalizer for headphones is introduced. It estimates the effect of auditory masking while considering the char acteristics of the headphones, ambient noise, and music. The sys tem 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 micro phone 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.

A novel graphic equalizer design comprised of a single secondorder section per band is proposed, where the band filters have a symmetric shape about their center frequency in the entire audio range. The asymmetry of the band filters at high frequencies close to the Nyquist limit has been one source of inaccuracy in previous designs. The interaction between the different band filters is accounted for using the weighted least-squares design, which employs an interaction matrix. In contrast to prior works, the interaction matrix is designed with a different prototype gain for each band filter, helping to keep the maximum approximation error below 1 dB at the center frequencies and between them when the neighboring command gains are the same. An iteration step can further diminish the approximation error. Comparisons of the proposed design with previous methods show that it is the most accurate graphic equalizer design to date.