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Noise Sources Identification

SvanNET AI

SvanNET AI is a proprietary functionality developed by Svantek for their SvanNET AMS (Automatic Monitoring Services). This online solution supports multi-point connections with Svantek’s noise and vibration monitoring stations. The AI module within SvanNET AMS enables automatic noise source recognition and classification using artificial intelligence and machine learning. It employs machine learning algorithms to analyze recorded audio data, accurately categorizing sound sources into 28 distinct classes, such as industrial noise, traffic, and natural sounds. By automating the noise source identification process, SvanNET AI provides precise and real-time noise monitoring, enabling cities to manage urban noise pollution more effectively.

What are the applications of noise source identification?

In the UK, noise source identification is used for various applications:

  • Urban Noise Management: Large cities like London, Manchester, and Birmingham monitor and manage urban noise pollution. By accurately identifying and classifying noise sources, local authorities can implement targeted noise control measures, such as traffic management or construction regulations, to improve residents’ quality of life.
  • Environmental Monitoring: Environmental agencies monitor noise pollution in sensitive areas, such as near wildlife reserves or residential zones. Noise source identification helps in maintaining acceptable noise levels and protecting both human and wildlife habitats.
  • Industrial Noise Control: Factories and industrial sites across the UK monitor noise emissions and ensure compliance with noise regulations. Noise source identification helps in reducing the impact of industrial noise on surrounding communities.
  • Transportation Noise Monitoring: Airports, railways, and highways monitor noise levels and identify specific sources of noise, such as different types of vehicles or aircraft. Noise source identification data can be used to develop strategies to mitigate noise pollution from transportation.

  • Public Health and Safety: By providing real-time data on noise pollution, public health officials can understand the impact of noise on community health and develop policies to reduce noise-related health issues.

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SV279PRO Noise Monitoring Station
Noise Monitoring
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Automatic Reporting

SvanNET AI

Features

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Noise Sources Identification

Accurately identifies and categorises noise sources in real-time.

SvanNET AI excels in noise source identification, using advanced machine learning algorithms to analyse recorded audio data. It can classify sound sources into 28 distinct categories, such as industrial noise, traffic, construction, and natural sounds. This precise identification enables cities to monitor and manage noise pollution more effectively, ensuring targeted noise control measures and improved urban living conditions.
Noise-Sources-Identification
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Audio Events Classification

Classifies specific audio events using event triggers and real-time analysis.

SvanNET AI provides robust audio event classification by recording WAVE files with at least a 16 kHz sampling rate and using event triggers to capture noise events. This system can classify and analyse specific noise events in real-time, offering immediate detection and categorisation of sources. This capability allows for timely responses to noise pollution issues and supports detailed analysis of noise patterns in urban environments.
AI Audio Events Classification
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Automatic Reporting

Generates detailed reports with prediction confidence and data visualisation.

SvanNET AI automates the reporting process, providing comprehensive and detailed reports on noise data. The system includes prediction confidence levels for each identified noise event, helping users understand the reliability of classifications. Additionally, SvanNET AI produces visualisations such as charts with sound classes and markers, making it easier to interpret and analyse noise data. This automatic reporting feature simplifies the management of urban noise, supporting informed decision-making and efficient noise control strategies.
Automatic Reporting
Noise-Sources-Identification

Noise Sources Identification

Accurately identifies and categorises noise sources in real-time.

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AI Audio Events Classification

Audio Events Classification

Classifies specific audio events using event triggers and real-time analysis.

Read more

Automatic Reporting

Automatic Reporting

Generates detailed reports with prediction confidence and data visualisation.

Read more

Noise Sources Recognition

Applications

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Environmental Noise

SvanNET AI can classify sound sources into 28 distinct categories

The main application of SvanNET AI in the UK is in environmental noise management, particularly for urban noise and traffic. It is used to monitor, identify, and categorise various noise sources in cities, helping authorities and planners implement targeted noise reduction measures. By providing accurate and real-time data on noise pollution, SvanNET AI supports the development of effective strategies to mitigate the adverse effects of urban noise on public health and improve overall urban living conditions.
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Environmental Noise Sources

SvanNET AI

Videos

SvanNET AI functionality

Watch a new handy video about the SvanNET AI functionality that can be used for automatic noise source classification.

AI noise monitoring system

SvanNET AI

SvanNET AI

SvanNET AI is a Svantek proprietary functionality for SvanNET AMS, an online solution that supports multi-point connection with Svantek’s noise and vibration monitoring stations. The AI module enables automatic noise source recognition and classification by using artificial intelligence and machine learning. This AI system employs machine learning algorithms to analyze recorded audio data, accurately categorizing sound sources into 28 distinct classes, such as industrial noise, traffic, and natural sounds. By automating the noise source identification process, SvanNET AI provides precise and real-time noise monitoring, enabling cities to manage urban noise pollution more effectively.













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