Poultry Voiceprint Analysis for Abnormal Sound Detection in Dense Flocks
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Solution Overview
Problem
In large-scale poultry farming, identifying abnormal poultry sounds from a mixture of many sounds is difficult due to high stocking density, leading to rapid disease spread and economic losses, as current methods rely heavily on human experience and struggle with noise interference.
Innovation Solution
A poultry voiceprint identification system using band-pass filtering, spectral subtraction, and artificial intelligence sound models to analyze sound features, classifying them as normal or abnormal poultry sounds, and providing a user interface for observation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If band-pass filtering and spectral subtraction are applied to extract sound features, then measurement precision of poultry sounds is improved, but device complexity increases
Solution Approach 1:
The sound signal processing is divided into multiple sequential stages: band-pass filtering to isolate frequency ranges, spectral subtraction to remove background noise, and feature extraction to identify poultry sound characteristics. This segmentation allows each processing stage to focus on specific aspects of sound analysis, improving overall measurement precision while making the complex processing manageable through modular organization
Solution Approach 2:
The patent introduces intermediate processing steps between raw sound recording and final identification. Band-pass filtering acts as an intermediary to isolate relevant frequency components, and spectral subtraction serves as another intermediary layer to eliminate background noise. These intermediary processes bridge the gap between raw mixed sounds and actionable sound features, enhancing measurement precision
2Reliability
If AI sound models are used to classify sound features, then reliability of disease detection is improved, but device complexity increases
Solution Approach 1:
The system performs preliminary processing of sound signals before they reach the AI classification model. Sound features are extracted and pre-processed in advance, organizing raw audio data into structured formats that the AI model can efficiently process. This preliminary action improves the reliability of disease detection by ensuring high-quality input data while allowing the AI model to focus specifically on classification tasks
Solution Approach 2:
The patent replaces manual sound analysis with automated AI sound models. Instead of relying on mechanical or manual inspection methods, the system uses machine learning algorithms to classify sound features and identify disease conditions. This substitution significantly improves detection reliability and consistency while the modular architecture manages the increased system complexity
3Loss of time
If early detection of vocal changes is implemented, then loss of time for disease response is reduced, but measurement precision requirements increase
Solution Approach 1:
The system continuously monitors and analyzes vocal changes in real-time, performing preliminary detection of abnormal sound patterns before diseases progress to more serious stages. By implementing ongoing sound feature analysis and comparing against baseline characteristics, the system achieves early detection while maintaining measurement precision through continuous calibration and reference to normal vocal patterns
Solution Approach 2:
The patent implements feedback mechanisms where detected sound patterns are continuously compared against known disease signatures and normal variations. The system provides feedback loops that refine detection accuracy over time, allowing early detection of vocal changes while maintaining high measurement precision through iterative improvement and validation against established benchmarks
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
Accurately identifies abnormal poultry sounds with high accuracy, enabling early detection of diseases and reducing economic losses by improving the ability to distinguish poultry sounds from noise, even in high-density environments.
Implementation Method 1
filtering out the recording information to generate a filtered recording information with a specific frequency range
Implementation Method 2
filtering out the recording information to generate a filtered recording information with a specific frequency range by band-pass filtering and spectral subtraction
Implementation Method 3
dividing the filtered recording information into a plurality of sound information though a frequency domain
Data Source
AI summary
A poultry voiceprint identification system includes a receiver, a feature processing module, a feature analysis module and an artificial intelligence sound model. The receiver is arranged in a poultry house for receiving a recording information of a poultry house for a period of time. A stocking density of poultry in the poultry house is 7 per square meter or more. The feature processing module converts the recording information into a plurality of sound features via filtering, segmentation and extraction methods. The artificial intelligence sound model generates a training group according to the sound features. The feature analysis module analyzes each sound feature to determine a sound state of each sound feature through an artificial intelligence sound model. The sound state includes a normal poultry sound state or an abnormal poultry sound state. The system accurately and quickly identifies the sound from abnormal poultry among high stocking density poultry.