Map Layer Noise Level Prediction via Machine Learning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing technologies lack an efficient method to map noise levels in areas, which is crucial for improving travel quality and the reliability of sound-based navigation systems.
Innovation Solution
An apparatus comprising a processor and memory that uses machine learning to generate a map layer of noise levels by training on images and audio data indicating decibel levels, allowing for predicted decibel levels to be calculated without additional sensor data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If noise levels are mapped using traditional sensor-based methods, then measurement precision is improved, but device complexity and data collection costs increase
Solution Approach 1:
The patent replaces physical acoustic sensors with a machine learning model that processes visual images to predict noise levels. This substitutes the mechanical/sensor-based measurement system with an information-processing system that uses image analysis and trained algorithms to estimate decibel levels without requiring additional hardware sensors in each location.
Solution Approach 2:
The patent creates a virtual copy of noise level data by training a machine learning model on paired image-noise datasets. Once trained, the model can generate noise level predictions for new images, effectively copying the noise measurement capability from locations with sensors to locations without sensors through algorithmic inference rather than physical measurement replication.
2Reliability
If comprehensive noise data is collected across multiple areas, then reliability of noise level data is improved, but loss of time and data collection resources increase
Solution Approach 1:
The patent performs preliminary action by collecting and pairing images with their corresponding noise level measurements during periods when sensor data is available. This training phase stores the learned relationships between visual features and noise levels, enabling rapid prediction without real-time sensor deployment. The model is prepared in advance to handle future prediction requests immediately.
Solution Approach 2:
The patent replaces time-consuming physical sensor deployment and continuous monitoring with a pre-trained machine learning model that can instantly predict noise levels from images. This substitution eliminates the need for ongoing sensor data collection while maintaining reliable noise level information across multiple areas.
3Ease of operation
If existing mapping technologies are used without noise layer integration, then ease of operation is maintained, but object-affected harmful factors increase due to noise impact on navigation systems
Solution Approach 1:
The patent merges the noise level prediction functionality with the existing mapping system by integrating the machine learning model into the map generation pipeline. Noise level data is combined with geographic and visual data to create a unified map layer that includes noise information, allowing navigation systems to access noise awareness without requiring separate operational procedures.
Solution Approach 2:
The patent introduces a noise map layer as an intermediary between the physical noise environment and the navigation system. This intermediate representation translates complex acoustic conditions into usable map data that navigation algorithms can process, enabling noise-aware routing while maintaining the simplicity of existing navigation interfaces and operations.
Data Source
AI summary
An apparatus, method and computer program product are provided for providing a map layer of noise levels. In one example, the apparatus receives input data including an image and location data indicating an area in which the image was captured. The apparatus causes a machine learning model to generate a datapoint in a map layer as a function of the input data. The datapoint indicate a predicted decibel level at the area during an instance in which the image was captured. The machine learning model is trained to generate the datapoint as a function of the input data based on training data, where the training data include images and audio data indicating decibel levels of areas in which the images were captured during instances in which the images were captured.