Audio Metadata Generation With Joint ASR, AED, and AT Models
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Solution Overview
Problem
Current audio processing systems treat automatic speech recognition (ASR), acoustic event detection (AED), and audio tagging (AT) as separate tasks, limiting synergy and efficiency in processing complex audio scenes.
Innovation Solution
A neural network architecture that jointly trains a transformer model and a connectionist temporal classification (CTC) based model to perform ASR, AED, and AT tasks, producing metadata with time-dependent and time-agnostic attributes of audio events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If separate task-specific neural network architectures are used for ASR, AED, and AT tasks, then each task can be performed with dedicated optimization, but the system complexity increases and synergy between tasks is lost
Solution Approach 1:
The patent combines multiple task-specific neural networks (ASR, AED, AT) into a single unified neural network architecture. This unified network processes audio inputs and generates outputs for all three tasks simultaneously, reducing system complexity while maintaining task performance through shared feature extraction layers and task-specific output layers.
Solution Approach 2:
The unified neural network is designed to perform multiple functions (ASR, AED, and AT tasks) within a single architecture. The network accepts audio inputs and produces diverse outputs including speech transcriptions, acoustic event detections, and audio tags, making the system universal and eliminating the need for separate dedicated networks for each task.
2Measurement precision
If multiple separate neural networks are trained independently for different transcription tasks, then each network can be optimized for its specific task, but training data requirements increase and training time is extended
Solution Approach 1:
The patent merges multiple independent training processes into a single unified training process. The unified neural network is trained on a combined dataset that serves all three tasks (ASR, AED, AT) simultaneously, reducing total training time and data requirements while maintaining task-specific optimization through multi-task learning mechanisms.
Solution Approach 2:
The unified neural network is designed to learn multiple tasks concurrently during a single training phase. The network architecture includes shared feature extraction components that learn general audio patterns applicable to all tasks, while task-specific output layers maintain specialized capabilities, achieving both efficiency and optimization.
3Measurement precision
If separate neural networks are used for each transcription task, then task-specific performance can be maximized, but the overall system efficiency and synergy between tasks are reduced
Solution Approach 1:
The patent merges separate neural network systems into a single unified network that processes audio inputs and generates outputs for ASR, AED, and AT tasks in an integrated manner. This consolidation improves system efficiency by eliminating redundant processing while maintaining task-specific performance through shared feature extraction and specialized output layers.
Solution Approach 2:
The unified neural network is designed to handle multiple transcription tasks (ASR, AED, AT) within a single multi-functional architecture. The network simultaneously optimizes for all tasks by sharing computational resources and learning transferable features, thereby improving overall system efficiency and synergy while preserving task-specific performance capabilities.
Data Source
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AI summary
An audio processing system is provided. The audio processing system comprises an input interface configured to accept an audio signal. Further, the audio processing system comprises a memory configured to store a neural network trained to determine different types of attributes of multiple concurrent audio events of different origins, wherein the types of attributes include time-dependent and time-agnostic attributes of speech and non-speech audio events. Further, the audio processing system comprises a processor configured to process the audio signal with the neural network to produce metadata of the audio signal, the metadata including one or multiple attributes of one or multiple audio events in the audio signal.