Performance limits in subband beamforming

Nordholm, Sven; Claesson, Ingvar; Grbic, Nedelko

doi:10.1109/tsa.2003.811543

Cited by 14 publications

(6 citation statements)

References 22 publications

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“…Thus, we seek the beamformer that is the solution to (15) Clearly, the inner maximization is obtained at so that our problem reduces to (16) which is equal to the MSE of (9) with . Therefore, is an MMSE beamformer (10) matched to ,…”

Section: A Minimax Mse Beamformingmentioning

confidence: 99%

“…However, this approach has slow convergence and requires a large number of computations [31]. A computationally cheaper strategy is subband beamforming [15]- [20], [31], in which the wideband signal is decomposed into a number of frequency bands by means of a filter bank. These subbands have a reduced bandwidth allowing the application of narrowband beamformers.…”

Section: B Estimating a Deterministic Wideband Signal Using A Subbanmentioning

confidence: 99%

“…In some applications, the actual magnitude value may be of importance. This is particularly true in the context of subband beamforming [15]- [20] which gained interest in recent years, due to its ability to reduce the complexity of conventional broadband strategies. In this context, beamforming is performed independently in decimated frequency bands, and the outputs of the channels are combined.…”

mentioning

confidence: 99%

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A Competitive Mean-Squared Error Approach to Beamforming

Eldar

Nehorai

Rosa

2007

IEEE Trans. Signal Process.

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Abstract-We treat the problem of beamforming for signal estimation where the goal is to estimate a signal amplitude from a set of array observations. Conventional beamforming methods typically aim at maximizing the signal-to-interference-plus-noise ratio (SINR). However, this does not guarantee a small mean-squared error (MSE), so that on average the resulting signal estimate can be far from the true signal. Here, we consider strategies that attempt to minimize the MSE between the estimated and unknown signal waveforms. The methods we suggest all maximize the SINR but at the same time are designed to have good MSE performance. Since the MSE depends on the signal power, which is unknown, we develop competitive beamforming approaches that minimize a robust MSE measure. Two design strategies are proposed: minimax MSE and minimax regret. We demonstrate through numerical examples that the suggested minimax beamformers can outperform several existing standard and robust methods, over a wide range of signal-to-noise ratio (SNR) values. Finally, we apply our techniques to subband beamforming and illustrate their advantage in estimating a wideband signal.Index Terms-Beamforming, minimax mean-squared error, minimax regret, robust beamforming, subband beamforming.

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Section: A Minimax Mse Beamformingmentioning

confidence: 99%

Section: B Estimating a Deterministic Wideband Signal Using A Subbanmentioning

confidence: 99%

mentioning

confidence: 99%

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A Competitive Mean-Squared Error Approach to Beamforming

Eldar

Nehorai

Rosa

2007

IEEE Trans. Signal Process.

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“…However, these methods are limited to scenarios where the target and interfering sources are separated in space with respect to the recording microphones: a condition that is not consistently guaranteed. In addition, the limited amplification factor achievable with beamforming may be insufficient when a greater suppression of the interfering speakers is desired to improve a user’s experience [32]. An approach that addresses these shortcomings is needed to maximize the benefits of AAD and produce a cleaner, more robust amplification of a target speaker; a result that can be incorporated into assistive hearing devices.…”

Section: Introductionmentioning

confidence: 99%

Neural decoding of attentional selection in multi-speaker environments without access to clean sources

et al. 2017

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Objective People who suffer from hearing impairments can find it difficult to follow a conversation in a multi-speaker environment. Current hearing aids can suppress background noise; however, there is little that can be done to help a user attend to a single conversation amongst many without knowing which speaker the user is attending to. Cognitively controlled hearing aids that use auditory attention decoding (AAD) methods are the next step in offering help. Translating the successes in AAD research to real-world applications poses a number of challenges, including the lack of access to the clean sound sources in the environment with which to compare with the neural signals. We propose a novel framework that combines single-channel speech separation algorithms with AAD. Approach We present an end-to-end system that (1) receives a single audio channel containing a mixture of speakers that is heard by a listener along with the listener’s neural signals, (2) automatically separates the individual speakers in the mixture, (3) determines the attended speaker, and (4) amplifies the attended speaker’s voice to assist the listener. Main results Using invasive electrophysiology recordings, we identified the regions of the auditory cortex that contribute to AAD. Given appropriate electrode locations, our system is able to decode the attention of subjects and amplify the attended speaker using only the mixed audio. Our quality assessment of the modified audio demonstrates a significant improvement in both subjective and objective speech quality measures. Significance Our novel framework for AAD bridges the gap between the most recent advancements in speech processing technologies and speech prosthesis research and moves us closer to the development of cognitively controlled hearable devices for the hearing impaired.

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“…Another method to reduce some of the disadvantages of fully adaptive beamforming is achieved via subband beamforming techniques [42], [43]. A subband beamformer has the advantages of improved interference suppression, quicker convergence, and reduced computational complexity compared to the general fully adaptive beamformer [43].…”

Section: Figure 23 Microphone Array Beamformer In a Hands-free Systemmentioning

confidence: 99%

Acoustic echo cancellation in the presence of microphone arrays

Burton¹

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Performance limits in subband beamforming

Cited by 14 publications

References 22 publications

A Competitive Mean-Squared Error Approach to Beamforming

A Competitive Mean-Squared Error Approach to Beamforming

Neural decoding of attentional selection in multi-speaker environments without access to clean sources

Acoustic echo cancellation in the presence of microphone arrays

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