HRTF-based robust least-squares frequency-invariant beamforming

Barfuss, Hendrik; Huemmer, Christian; Lamani, Gleni; Schwarz, Andreas; Kellermann, Walter

doi:10.1109/waspaa.2015.7336933

Cited by 12 publications

(28 citation statements)

References 10 publications

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“…In [1], we proposed the design of an HRTF-based RLSFI FSB where a desired beamformer responseB(ω, φ, θ) is approximated in the Least-Squares (LS) sense at each frequency ω. In addition, a distortionless response constraint in the desired look direction and a lower bound on the WNG are imposed on the filter coefficients.…”

Section: Hrtf-based Robust Least-squares Frequency-invariant Beamformmentioning

confidence: 99%

“…However, one major drawback of the beamformer design in [1] is its limitation to a plane corresponding to a fixed elevation angle, which turned out to be inappropriate for capturing a threedimensional sound field, especially when considering that a robot head changes its elevation angle relative to the target. Therefore, The research leading to these results has received funding from the European Union's Seventh Framework Programme (FP7/2007-2013) under grant agreement n o 609465.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, The research leading to these results has received funding from the European Union's Seventh Framework Programme (FP7/2007-2013) under grant agreement n o 609465. 1 Please note that even though the robot's head does not have pinnae and the microphone positions are not limited to 'ear positions', we still use the term HRTF here. Furthermore, in the context of this work, HRTFs only model the direct propagation path between a source and a microphone mounted on the robot's head, but no reverberation components.…”

Section: Introductionmentioning

confidence: 99%

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HRTF-based two-dimensional robust least-squares frequency-invariant beamformer design for robot audition

Barfuss

Buerger

Podschus

et al. 2017

2017 Hands-Free Speech Communications and Microphone Arrays (HSCMA)

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In this work, we propose a two-dimensional Head-Related Transfer Function (HRTF)-based robust beamformer design for robot audition, which allows for explicit control of the beamformer response for the entire three-dimensional sound field surrounding a humanoid robot. We evaluate the proposed method by means of both signal-independent and signal-dependent measures in a robot audition scenario. Our results confirm the effectiveness of the proposed two-dimensional HRTF-based beamformer design, compared to our previously published one-dimensional HRTF-based beamformer design, which was carried out for a fixed elevation angle only.

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Section: Hrtf-based Robust Least-squares Frequency-invariant Beamformmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

HRTF-based two-dimensional robust least-squares frequency-invariant beamformer design for robot audition

Barfuss

Buerger

Podschus

et al. 2017

2017 Hands-Free Speech Communications and Microphone Arrays (HSCMA)

Self Cite

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“…The relative transfer functions can be used, e.g., for signal enhancement in the context of binaural listening devices like hearing aids [2]. Beyond this, RTFs can be extended to scatterer-related transfer functions for applications in the field of robot audition, e.g., [3]. Relative impulse responses can in a subsequent step be used, e.g., for Linearly Constraint Minimum Variance (LCMV) beamforming [4].…”

Section: Introductionmentioning

confidence: 99%

Efficient target activity detection based on recurrent neural networks

Gerber

Meier

Kellermann

2017

2017 Hands-Free Speech Communications and Microphone Arrays (HSCMA)

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This paper addresses the problem of Target Activity Detection (TAD) for binaural listening devices. TAD denotes the problem of robustly detecting the activity of a target speaker in a harsh acoustic environment, which comprises interfering speakers and noise ('cocktail party scenario'). In previous work, it has been shown that employing a Feed-forward Neural Network (FNN) for detecting the target speaker activity is a promising approach to combine the advantage of different TAD features (used as network inputs). In this contribution, we exploit a larger context window for TAD and compare the performance of FNNs and Recurrent Neural Networks (RNNs) with an explicit focus on small network topologies as desirable for embedded acoustic signal processing systems. More specifically, the investigations include a comparison between three different types of RNNs, namely plain RNNs, Long Short-Term Memories, and Gated Recurrent Units. The results indicate that all versions of RNNs outperform FNNs for the task of TAD.

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“…Measured Head-Related Transfer Functions (HRTFs) were integrated into the respective convex optimization problem to account for the scattering effects of the robot's head on the sound field. Finally, the HRTF-based (non-polynomial) RLSFI design of [14] was extended to two dimensions in [16], where the desired beamformer response was defined for both azimuth and elevation angles. As a result the beamformer response can now be controlled for all DoAs on a sphere surrounding the three-dimensional microphone array integrated into the humanoid robot's head.…”

Section: Introductionmentioning

confidence: 99%

Design of robust two-dimensional polynomial beamformers as a convex optimization problem with application to robot audition

Barfuss

Bachmann

Buerger

et al. 2017

2017 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)

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We propose a robust two-dimensional polynomial beamformer design method, formulated as a convex optimization problem, which allows for flexible steering of a previously proposed data-independent robust beamformer in both azimuth and elevation direction. As an exemplary application, the proposed twodimensional polynomial beamformer design is applied to a twelveelement microphone array, integrated into the head of a humanoid robot. To account for the effects of the robot's head on the sound field, measured head-related transfer functions are integrated into the optimization problem as steering vectors. The two-dimensional polynomial beamformer design is evaluated using signal-independent and signal-dependent measures. The results confirm that the proposed polynomial beamformer design approximates the original fixed beamformer design very accurately, which makes it an attractive approach for robust real-time dataindependent beamforming.Index Terms-Robust superdirective beamforming, polynomial beamforming, white noise gain, robot audition

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HRTF-based robust least-squares frequency-invariant beamforming

Cited by 12 publications

References 10 publications

HRTF-based two-dimensional robust least-squares frequency-invariant beamformer design for robot audition

HRTF-based two-dimensional robust least-squares frequency-invariant beamformer design for robot audition

Efficient target activity detection based on recurrent neural networks

Design of robust two-dimensional polynomial beamformers as a convex optimization problem with application to robot audition

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