Howling Detection in Hearing Aids Based on Generalized Teager-Kaiser Operator

Khoubrouy, Soudeh A.; Panahi, Issa; Hansen, John H. L.

doi:10.1109/taslp.2014.2377575

Cited by 1 publication

(2 citation statements)

References 22 publications

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“…Spectral Flux (SF) feature-based VAD is preferred in our approach [57]. The SF feature is defined by (10):…”

Section: Voice Activity Detectormentioning

confidence: 99%

“…Hearing aid manufacturers [3]- [5] and numerous researchers have developed efficient signal processing algorithms to advance the performance of HADs, such as noise suppression, speech enhancement [6]- [8], acoustic feedback cancellation (AFC) [9], [10], speech source localization and beamforming [11]- [13], and speech-speaker recognition [16], [17]. From the psychoacoustics point of view, speech perception can be improved notably with these algorithms in noisy environments.…”

Section: Introductionmentioning

confidence: 99%

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Robust Three-Microphone Speech Source Localization Using Randomized Singular Value Decomposition

Tokgoz¹,

Panahi

2021

IEEE Access

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Direction-of-arrival (DOA) estimation is a fundamental technique in array signal processing due to its wide applications in beamforming, speech enhancement and many other assistive speech processing technologies. In this paper, we devise a novel DOA technique based on randomized singular value decomposition (RSVD) to improve the performance of non-uniform non-linear microphone arrays (NUNLA). The accurate and efficient singular value decomposition of large data matrices is computationally challenging, and randomization provides an effective tool for performing matrix approximation, therefore, the developed DOA estimation utilizes a modified dictionary-based RSVD method for localizing single speech sources under low signal-to-noise ratios (SNR). Unlike previous methods developed for uniform linear microphone arrays, the proposed approach with L-shaped three microphone setup has no ‘left-right’ ambiguity. We present the performance of our proposed method in comparison to other techniques. The demonstrated experiments shows at-least 20% performance improvement using simulated data and 25% performance improvement using real data when compared with similar DoA estimation techniques for NUNLA. The proposed method exploits frame-based online time delay of arrival (TDOA) measurements which facilitates the proposed algorithm to run on real-time devices. We also show an efficient real-time implementation of the proposed method on a Pixel 3 Android smartphone using its built-in three microphones for hearing aid applications.

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“…Spectral Flux (SF) feature-based VAD is preferred in our approach [57]. The SF feature is defined by (10):…”

Section: Voice Activity Detectormentioning

confidence: 99%