1D-CNN Acoustic Garbage Classification
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Solution Overview
Problem
Current automatic garbage classification technologies are inefficient and inaccurate, relying heavily on manual separation and requiring extensive education and supervision, with image recognition facing challenges due to variability in garbage types and mixed substances, while existing acoustic methods are cumbersome or computationally complex.
Innovation Solution
An acoustic garbage classification method utilizing a one-dimensional convolutional neural network (1D-CNN) that acquires and preprocesses sound signals from falling garbage, builds a sound database, and trains a model for real-time classification, optimizing hyperparameters through orthogonal experiments to achieve accurate and efficient classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual garbage separation is used, then classification accuracy can be maintained through human knowledge, but time consumption and education costs increase significantly
Solution Approach 1:
The patent replaces the mechanical human separation process with an acoustic detection system. A microphone captures sound signals generated by garbage falling, and a 1D-CNN model automatically classifies garbage types based on acoustic features, eliminating the need for manual separation while maintaining high accuracy.
Solution Approach 2:
The system enables garbage to classify itself through the acoustic signals it generates during the dropping process. The garbage's own physical properties (material, shape, size) naturally produce distinctive sound patterns that the 1D-CNN model can recognize and classify without human intervention.
2Extent of automation
If image recognition technology is used for garbage classification, then automated classification can be achieved, but classification accuracy decreases due to color and shape variability and mixed substances
Solution Approach 1:
The patent replaces the visual image recognition system with an acoustic detection system. Instead of analyzing visual features that are affected by color, shape variability, and mixed substances, the system captures and analyzes sound signals generated by garbage falling, which are directly correlated with material properties.
Solution Approach 2:
The patent changes the detection parameter from visual features (color, shape) to acoustic features (frequency, amplitude, time-domain characteristics). Sound signals are highly correlated with material properties, providing more reliable classification features that are less affected by appearance variations and mixed substances.
3Extent of automation
If 2D-CNN is used for acoustic garbage classification after converting 1D sound data to 2D images, then classification can be performed, but computational complexity increases and real-time processing becomes difficult
Solution Approach 1:
The patent applies 1D-CNN directly to the one-dimensional time-series sound signal without converting it to a 2D image representation. This preserves the temporal sequence information in the acoustic signal while reducing computational complexity compared to 2D-CNN approaches, enabling real-time processing.
Solution Approach 2:
The patent extracts acoustic features directly from the 1D sound signal and feeds them into the 1D-CNN model, eliminating the unnecessary step of converting sound data to 2D images. This direct extraction approach reduces computational overhead while maintaining classification effectiveness.
4Device complexity
If manual feature extraction and shallow classifiers are used for acoustic classification, then simpler models can be employed, but classification accuracy decreases and the process becomes cumbersome
Solution Approach 1:
The patent performs preliminary feature extraction automatically within the 1D-CNN model through convolutional operations that learn optimal acoustic features from raw sound signals. This eliminates the need for manual feature engineering while achieving high classification accuracy through deep learning.
Solution Approach 2:
The patent replaces manual feature extraction and shallow classification with an automated 1D-CNN deep learning system. The convolutional layers automatically learn and extract discriminative acoustic features from the input signals, and the fully connected layers perform classification, achieving high accuracy without manual intervention.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The method achieves accurate and efficient garbage classification, reducing the need for manual separation and computational complexity, enabling quick and real-time processing suitable for mobile devices, thereby improving recycling rates and practicality.
Implementation Method 1
acquiring sound signals generated by falling garbage
Data Source
AI summary
An acoustic garbage classification method using a one-dimensional convolutional neural network (1D-CNN) is provided. The method includes: acquiring sound signals generated by falling garbage; preprocessing the sound signals; acquiring and preprocessing the sound signals of different types of garbage, building a sound database for garbage classification, and establishing and training a 1D-CNN model; acquiring a sound signal of garbage to be classified, and inputting the sound signal into the trained 1D-CNN for garbage classification to obtain a classification result. The present disclosure is helpful to assist people in accurate garbage classification, improves the accuracy of garbage classification and recycling, and has high practical and popularization value.