Binaural wind noise detection, cancellation and its evaluation for hearing aids based on HRTF cues

Nakashima, Hideharu; Kouyama, Ryousuke; Hiruma, Nobuhiko; Fujisaka, Yoh-ichi

doi:10.1109/iecon.2015.7392868

Cited by 4 publications

(2 citation statements)

References 5 publications

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“…HRTFs, or Head-Related Transfer Functions, describe the transmission of sound from a point in space to the human ear canal [1]. This concept finds application in various fields, such as personalized sound design for individuals, including noise cancellation [2] and the creation of immersive Virtual Reality (VR) environments [3]. Each person's HRTF is unique, influenced by factors such as head, torso, shoulder, and pinnae shape and other characteristics [4].…”

Section: Introductionmentioning

confidence: 99%

Impact of input preprocessing for HRTF elevation classification over multiple datasets

De Rus,

Lopez-Ballester,

Montagud

et al. 2024

Proceedings of the 10th Convention of the European Acoustics Association Forum Acusticum 2023

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The localization of sound sources on the horizontal plane is mainly aided by perceived interaural level and time differences. However, identifying elevation cues in Head-Related Transfer Functions (HRTFs) remains challenging. Spectral cues play a key role in localizing sources in elevation and are highly individual, resulting from anatomic characteristics specific to each person, such as the shape of the pinnae, head, or torso. In a previous study, we proposed a simple 1D convolutional neural network (CNN) trained to classify HRTF signals into different elevation sectors to identify spectral elevation cues using explainability techniques. Although the model obtained promising results, it was only trained and validated on the CIPIC database. In this work, we focus on developing a model that can generalize across multiple HRTF datasets to achieve good classification performance across various subjects and measurements. Since each dataset is obtained in different conditions (e.g., source signal used, distance between emitters and receivers, spatial resolution, calibration), the preprocessing of the data may significantly impact the overall inter-dataset model performance. We explore different preprocessing techniques and evaluate their impact on the classification task to select meaningful standardization strategies for working with multiple HRTF datasets.

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Section: Introductionmentioning

confidence: 99%

Impact of input preprocessing for HRTF elevation classification over multiple datasets

De Rus,

Lopez-Ballester,

Montagud

et al. 2024

Proceedings of the 10th Convention of the European Acoustics Association Forum Acusticum 2023

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show abstract

“…Furthermore, when hearing aids on both the left and right ears work independently, the perception of the direction of arrival (DOA) and distance to the desired signal position will be uncertain. To preserve spatial information, a binaural wind noise cancellation algorithm using the characteristics of head-related transfer functions (HRTFs) was proposed by the authors [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Low-delay wind noise cancellation with preservation of spatial information for binaural hearing aids

Hiruma¹,

Nakashima

Fujisaka³

2018

Acoust. Sci. & Tech.

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Wind noise annoys hearing-aid users, and it is hard to attach a windscreen to a hearing-aid microphone, for cosmetic reasons. Some hearing-aid devices reduce the low-frequency components of input signals by using high-pass filters to suppress the wind noise. Although wind noise can be attenuated by this approach, the perceived binaural information of the desired signals will also be degraded simultaneously, resulting in partial information loss. We had previously proposed a shorttime fast-Fourier-transform-based (STFT-based) binaural wind noise cancellation algorithm that preserves binaural cues. This algorithm required a frame length of 32 ms to maintain a high frequency resolution. However, it is known that the tolerable group delay for mild hearing loss should be less than approximately 5 ms, in the high-frequency region. In this paper, we propose a low-delay binaural wind noise cancellation algorithm that uses a frequency-warping filter. The processing latency of this algorithm is shorter than the tolerable delay. The objective evaluation results-signal-to-noise ratios and perceptual evaluation of speech quality (PESQ) scores-were improved while maintaining a low latency. Subjective experiments demonstrated that the proposed method produced almost the same score as our previous STFT-based method, in terms of the directionality of output signals.

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A Stereo Wind-Noise Suppressor with Null Beamforming and Frequency-Domain Noise Averaging

Kato

Sugiyama

Komatsu

2018

IEICE Trans. Fundamentals

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Binaural wind noise detection, cancellation and its evaluation for hearing aids based on HRTF cues

Cited by 4 publications

References 5 publications

Impact of input preprocessing for HRTF elevation classification over multiple datasets

Impact of input preprocessing for HRTF elevation classification over multiple datasets

Low-delay wind noise cancellation with preservation of spatial information for binaural hearing aids

A Stereo Wind-Noise Suppressor with Null Beamforming and Frequency-Domain Noise Averaging

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