Audio Event Detection Using Cascaded ML Models
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Solution Overview
Problem
Existing technologies face challenges in accurately detecting and classifying interaction events in recorded audio conversations, particularly distinguishing between intentional and unintentional silences and background noise, which can negatively impact customer service quality.
Innovation Solution
A system utilizing a cascaded approach with machine learning models for real-time detection and classification of interaction events in recorded audio streams, employing an audio-based interaction event detection model and a text-based classification model to identify the type and cause of events, such as silences, and sending alerts or prompts based on predefined rules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If basic audio event detection is performed, then detection speed is improved, but classification accuracy deteriorates
Solution Approach 1:
The detection system is segmented into two distinct stages: a first machine learning model that performs rapid detection of interaction events, and a second machine learning model that performs accurate classification of detected events. This segmentation allows each model to be optimized for its specific function, with the first model prioritizing speed and the second model prioritizing accuracy, thereby resolving the contradiction between detection speed and classification accuracy.
Solution Approach 2:
The first machine learning model acts as an intermediary that performs initial detection and filtering of audio events. It identifies potential interaction events and passes them to the second model for classification. This intermediary role allows the system to achieve both fast detection (through the first model) and accurate classification (through the second model), resolving the contradiction between speed and precision.
2Adaptability or versatility
If multiple audio types are detected, then detection comprehensiveness is improved, but false positive rate deteriorates
Solution Approach 1:
The system uses feedback from the first machine learning model's detection results to guide the second model's classification process. The second model receives the detected events as input and provides feedback classification that filters out false positives. This feedback mechanism allows the system to maintain comprehensive detection of multiple audio types while reducing false positives through the classification stage.
Solution Approach 2:
The first machine learning model serves as an intermediary that broadly detects multiple audio types, while the second model acts as a filter that validates these detections. This two-stage intermediary approach ensures comprehensive detection of various audio events (speech, music, noise, silence) while the second model eliminates false positives, resolving the contradiction between comprehensiveness and reliability.
3Loss of time
If real-time detection is implemented, then response time is improved, but processing accuracy deteriorates
Solution Approach 1:
The real-time detection process is segmented into two sequential stages: the first machine learning model performs rapid detection to minimize response time, and the second machine learning model performs accurate classification to ensure processing accuracy. By segmenting the processing pipeline, the system can achieve both real-time response (through the fast first model) and high accuracy (through the precise second model), resolving the contradiction between response time and processing accuracy.
Solution Approach 2:
The first machine learning model performs preliminary detection action to identify potential interaction events quickly, before the second model performs more thorough classification. This preliminary action enables real-time response by doing the bulk of the detection work upfront, while the second model refines the accuracy. The preliminary detection stage thus reduces response time without sacrificing overall processing accuracy.
Data Source
AI summary
Detection of interaction events in recorded audio streams is disclosed, including: detecting an interaction event within a recorded audio stream; analyzing text before and after the interaction event in the recorded audio stream to determine a causer of the interaction event; and determining an action to be performed in response to the interaction event based at least in part on the causer of the interaction event.