AIMC Topic: Noise

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End-to-end feature fusion for jointly optimized speech enhancement and automatic speech recognition.

Scientific reports
Speech enhancement (SE) and automatic speech recognition (ASR) in real-time processing involve improving the quality and intelligibility of speech signals on the fly, ensuring accurate transcription as the speech unfolds. SE eliminates unwanted backg...

Moving beyond the noise: geospatial modelling of urban sound environments in a sub-Saharan African city.

Scientific reports
Cities encompass a mixture of artificial, human, animal, and nature-based sounds, which through long and short-term exposures, can impact on physical and mental health. Yet, most epidemiological research has focused on only transportation noise, leav...

A multi-domain collaborative denoising bearing fault diagnosis model based on dynamic inter-domain attention mechanism and noise-aware loss function.

PloS one
Rolling bearings are the core transmission components of large-scale rotating machinery such as wind power gearboxes and aviation engines, so timely and effective monitoring and diagnosis of their status are crucial to ensure the stable operation of ...

Automatic development of speech-in-noise hearing tests using machine learning.

Scientific reports
Understanding speech in noisy environments is a primary challenge for individuals with hearing loss, affecting daily communication and quality of life. Traditional speech-in-noise tests are essential for screening and diagnosing hearing loss but are ...

Influence pathways of noise exposure on people's negative emotions and health across different activity contexts: A neural network-based double machine learning approach.

Health & place
Noise is a major global environmental issue that raises concerns about both mental and physical health. However, few studies have investigated the mediating role of emotions in the pathways linking noise exposure to health outcomes. Additionally, man...

SuperM2M: Supervised and mixture-to-mixture co-learning for speech enhancement and noise-robust ASR.

Neural networks : the official journal of the International Neural Network Society
The current dominant approach for neural speech enhancement is based on supervised learning by using simulated training data. The trained models, however, often exhibit limited generalizability to real-recorded data. To address this, this paper inves...

Endpoint-aware audio-visual speech enhancement utilizing dynamic weight modulation based on SNR estimation.

Neural networks : the official journal of the International Neural Network Society
Integrating visual features has been proven effective for deep learning-based speech quality enhancement, particularly in highly noisy environments. However, these models may suffer from redundant information, resulting in performance deterioration w...

Machine learning-based urban noise appropriateness evaluation method and driving factor analysis.

PloS one
The evaluation of urban noise suitability is crucial for urban environmental management. Efficient and cost-effective methods for obtaining noise distribution data are of great interest. This study introduces various machine learning methods and appl...

Forecasting air pollution with deep learning with a focus on impact of urban traffic on PM10 and noise pollution.

PloS one
Air pollution constitutes a significant worldwide environmental challenge, presenting threats to both our well-being and the purity of our food supply. This study suggests employing Recurrent Neural Network (RNN) models featuring Long Short-Term Memo...

MosquitoSong+: A noise-robust deep learning model for mosquito classification from wingbeat sounds.

PloS one
In order to assess risk of mosquito-vector borne disease and to effectively target and monitor vector control efforts, accurate information about mosquito vector population densities is needed. The traditional and still most common approach to this i...