AIMC Topic: Noise

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Assessing the affective quality of soundscape for individuals: Using third-party assessment combined with an artificial intelligence (TPA-AI) model.

The Science of the total environment
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...

Assessment of noise pollution-prone areas using an explainable geospatial artificial intelligence approach.

Journal of environmental management
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...

GFANC-RL: Reinforcement Learning-based Generative Fixed-filter Active Noise Control.

Neural networks : the official journal of the International Neural Network Society
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...

A dual-region speech enhancement method based on voiceprint segmentation.

Neural networks : the official journal of the International Neural Network Society
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...

Windy events detection in big bioacoustics datasets using a pre-trained Convolutional Neural Network.

The Science of the total environment
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...

Deep learning restores speech intelligibility in multi-talker interference for cochlear implant users.

Scientific reports
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...

Machine-learning based detection of marine mammal vocalizations in snapping-shrimp dominated ambient noise.

Marine environmental research
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...

Deep learning-based auditory attention decoding in listeners with hearing impairment.

Journal of neural engineering
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...

Enhancing speaker identification through reverberation modeling and cancelable techniques using ANNs.

PloS one
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...

Enhanced Noise-Resilient Pressure Mat System Based on Hyperdimensional Computing.

Sensors (Basel, Switzerland)
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...