When investigating the relationship between the acoustic environment and human wellbeing, there is a potential problem resulting from data source self-correlation. To address this data source self-correlation problem, we proposed a third-party assess...
This research aims to use the power of geospatial artificial intelligence (GeoAI), employing the categorical boosting (CatBoost) machine learning model in conjunction with two metaheuristic algorithms, the firefly algorithm (CatBoost-FA) and the frui...
Neural networks : the official journal of the International Neural Network Society
Sep 5, 2024
The recent Generative Fixed-filter Active Noise Control (GFANC) method achieves a good trade-off between noise reduction performance and system stability. However, labelling noise data for training the Convolutional Neural Network (CNN) in GFANC is t...
Neural networks : the official journal of the International Neural Network Society
Aug 31, 2024
Single-channel speech enhancement primarily relies on deep learning models to recover clean speech signals from noise-contaminated speech. These models establish a mapping relationship between noisy and clean speech. However, considering the sparse d...
Passive Acoustic Monitoring (PAM), which involves using autonomous record units for studying wildlife behaviour and distribution, often requires handling big acoustic datasets collected over extended periods. While these data offer invaluable insight...
Cochlear implants (CIs) do not offer the same level of effectiveness in noisy environments as in quiet settings. Current single-microphone noise reduction algorithms in hearing aids and CIs only remove predictable, stationary noise, and are ineffecti...
Passive acoustics is an effective method for monitoring marine mammals, facilitating both detection and population estimation. In warm tropical waters, this technique encounters challenges due to the high persistent level of ambient impulsive noise o...
This study develops a deep learning (DL) method for fast auditory attention decoding (AAD) using electroencephalography (EEG) from listeners with hearing impairment (HI). It addresses three classification tasks: differentiating noise from speech-in-n...
This paper introduces a method aiming at enhancing the efficacy of speaker identification systems within challenging acoustic environments characterized by noise and reverberation. The methodology encompasses the utilization of diverse feature extrac...
Traditional systems for indoor pressure sensing and human activity recognition (HAR) rely on costly, high-resolution mats and computationally intensive neural network-based (NN-based) models that are prone to noise. In contrast, we design a cost-effe...
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