Audio Type Detection Using Multi-Algorithm Classification
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Solution Overview
Problem
Existing intelligent assistant devices face challenges in augmenting their functions for safety, security, monitoring, or surveillance while reducing false positive detections of adverse events.
Innovation Solution
The use of machine learning-based processing of audio information from a monitored environment to detect events, including the application of multiple machine learning algorithms for classification and the generation of alerts for specific event types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional audio detection methods are used in intelligent assistant devices, then the device can perform basic audio processing, but the detection accuracy of abnormal events is insufficient and false positive rates are high
Solution Approach 1:
The audio detection process is divided into multiple independent stages: initial audio event detection, feature extraction, classification, and verification. Each stage processes specific aspects of the audio signal independently, allowing the system to achieve high detection accuracy while reducing false positives through progressive filtering and validation at each segment.
Solution Approach 2:
Multiple machine learning algorithms are introduced as intermediary processing layers between the raw audio input and the final detection output. These intermediary algorithms (including but not limited to support vector machines, neural networks, and random forests) transform and refine the audio features, enabling accurate event identification while filtering out false detections through collaborative decision-making across multiple algorithmic layers.
2Measurement precision
If multiple machine learning algorithms are applied for audio classification, then the detection accuracy and reliability improve, but the computational complexity and processing time increase
Solution Approach 1:
Audio signals undergo preliminary processing including preprocessing, feature extraction, and initial filtering before being fed into multiple machine learning algorithms. This preliminary action reduces the dimensionality and complexity of the input data, allowing subsequent complex algorithms to operate more efficiently with reduced computational burden while maintaining high classification accuracy.
Solution Approach 2:
The system applies multiple machine learning algorithms selectively based on the detected audio event characteristics. Not all algorithms are executed for every audio input; instead, the system dynamically selects and applies only the necessary subset of algorithms appropriate for the specific event type detected, reducing overall computational complexity while maintaining high accuracy for relevant events.
Data Source
AI summary
Artificial intelligence-based processing can be used to classify audio information received from an audio input unit. In an example, audio information can be received from a microphone configured to monitor an environment. A processor circuit can identify identifying one or more features of the audio information received from the microphone and use a first applied machine learning algorithm to analyze the one or more features and determine whether the audio information includes an indication of an abnormal event in the environment. In an example, the processor circuit can use a different second applied machine learning algorithm, such as a neural network-based deep learning algorithm, to analyze the same one or more features and classify the audio information as including an indication of a particular event type in the environment.