ML Audio Signal Separation and Enhancement for Easy Processing
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Solution Overview
Problem
Traditional audio processing tools require specialized personnel and significant hardware resources, leading to high costs and complexity for non-specialized users.
Innovation Solution
An electronic device equipped with machine learning models that can perform various audio processing operations autonomously, including signal separation, transcription, and audio enhancement, with user-friendly interfaces, allowing non-specialized users to perform these tasks efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional audio processing tools are used, then audio processing functionality is provided, but specialized personnel intervention and high hardware requirements are needed
Solution Approach 1:
The patent replaces traditional mechanical/audio engineering systems with machine learning-based automated processing. The system uses neural networks to automatically perform audio separation, transcription, and enhancement tasks that previously required specialized audio engineers and complex hardware setups, thereby reducing device complexity and personnel requirements while maintaining processing capabilities
Solution Approach 2:
The system enables self-service audio processing by allowing users to directly interact with machine learning models through user-friendly interfaces. Users can upload audio files and receive automated processing results without needing specialized knowledge or intervention from audio engineers, making the system self-sufficient and easy to operate
2Productivity
If traditional audio processing tools are used, then audio processing functionality is provided, but high licensing costs are incurred
Solution Approach 1:
The patent employs open-source machine learning models and frameworks that can be freely downloaded and used without expensive licensing fees. Instead of requiring costly commercial software licenses, the system uses freely available AI models that provide comparable audio processing capabilities at minimal cost, thereby reducing the financial barrier for users
3Extent of automation
If machine learning models are applied for audio processing, then automated processing is achieved, but computational resources are required
Solution Approach 1:
The system applies machine learning models selectively and efficiently by processing audio data in optimized batches and using computational resources only when and where needed. The architecture processes audio signals through multiple stages, applying heavy computational models only to critical processing steps while using lighter processing for less demanding tasks, thereby achieving automation while managing computational resource consumption
Data Source
AI summary
An electronic device and method for intelligent audio processing is provided. The electronic device receives a first user input for selection of a source audio signal. The electronic device receives a second user input for selection of a first operation to be performed on the source audio signal. The electronic device receives a third user input for selection of at least a first audio signal. The electronic device applies a first machine learning (ML) model on the source audio signal. The electronic device further extracts at least the first audio signal from the source audio signal based on the application of the first ML model on the source audio signal. The electronic device further applies a second ML model on the extracted at least first audio signal from the plurality of audio signals based on the first operation. The electronic device further generates output information as per the first operation.