Assisted Listening Using a Headset: Enhancing audio perception in real, augmented, and virtual environments

Välimäki, Vesa; Franck, Andreas; Rämö, Jussi; Gamper, Hannes; Savioja, Lauri

doi:10.1109/msp.2014.2369191

Cited by 45 publications

(38 citation statements)

References 18 publications

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“…A wearable array with many microphones spread over a wide area would offer greater spatial resolution than the small arrays embedded in most hearing aids, headsets, and mobile phones today. Wearable microphone arrays could dramatically improve performance in assistive listening [5,6], augmented reality [7], and machine perception applications.…”

Section: Introductionmentioning

confidence: 99%

“…To simulate and evaluate wearable audio systems, researchers could use impulse responses measured with microphones placed all across the body. Note that whereas HRTFs are often used in human perceptual applications-for example, to create virtual sources in a listener's auditory environment [7]-these body-related transfer functions (BRTFs) are not directly related to human hearing. Rather, they help machines to localize, separate, and enhance real-world sound sources, and could be used alongside HRTFs in listening enhancement applications.…”

Section: Introductionmentioning

confidence: 99%

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Acoustic Impulse Responses for Wearable Audio Devices

Corey

Tsuda

Singer

2019

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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We present an open-access dataset of over 8000 acoustic impulse from 160 microphones spread across the body and affixed to wearable accessories. The data can be used to evaluate audio capture and array processing systems using wearable devices such as hearing aids, headphones, eyeglasses, jewelry, and clothing. We analyze the acoustic transfer functions of different parts of the body, measure the effects of clothing worn over microphones, compare measurements from a live human subject to those from a mannequin, and simulate the noise-reduction performance of several beamformers. The results suggest that arrays of microphones spread across the body are more effective than those confined to a single device.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Acoustic Impulse Responses for Wearable Audio Devices

Corey

Tsuda

Singer

2019

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…In augmented reality audio, a headset having microphones near the user's ears is used for combining reproduced and ambient sounds [203][204][205][206]. The ambient sounds captured by the microphones must be equalized to enhance their naturalness [203,205,207].…”

Section: Headphone Equalizationmentioning

confidence: 99%

“…Rämö et al [220] and Välimäki et al [206] have described a noise-based equalization system for headphone listening, which is sketched in Figure 17(b). It captures the ambient noise using an external microphone, such as the one in the cord of a hands-free headset, typically used for the user's own voice.…”

Section: Equalization To Combat Ambient Noisementioning

confidence: 99%

All About Audio Equalization: Solutions and Frontiers

Välimäki

Reiss

2016

Applied Sciences

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Audio equalization is a vast and active research area. The extent of research means that one often cannot identify the preferred technique for a particular problem. This review paper bridges those gaps, systemically providing a deep understanding of the problems and approaches in audio equalization, their relative merits and applications. Digital signal processing techniques for modifying the spectral balance in audio signals and applications of these techniques are reviewed, ranging from classic equalizers to emerging designs based on new advances in signal processing and machine learning. Emphasis is placed on putting the range of approaches within a common mathematical and conceptual framework. The application areas discussed herein are diverse, and include well-defined, solvable problems of filter design subject to constraints, as well as newly emerging challenges that touch on problems in semantics, perception and human computer interaction. Case studies are given in order to illustrate key concepts and how they are applied in practice. We also recommend preferred signal processing approaches for important audio equalization problems. Finally, we discuss current challenges and the uncharted frontiers in this field. The source code for methods discussed in this paper is made available at https://code.soundsoftware.ac.uk/projects/allaboutaudioeq.

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“…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.…”

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confidence: 99%

Third-Octave and Bark Graphic-Equalizer Design with Symmetric Band Filters

2020

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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 ...

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Assisted Listening Using a Headset: Enhancing audio perception in real, augmented, and virtual environments

Cited by 45 publications

References 18 publications

Acoustic Impulse Responses for Wearable Audio Devices

Acoustic Impulse Responses for Wearable Audio Devices

All About Audio Equalization: Solutions and Frontiers

Third-Octave and Bark Graphic-Equalizer Design with Symmetric Band Filters

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