ML Audio Signal Processing for Studio-Quality Noise Cleanup
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Solution Overview
Problem
Individuals lack access to sophisticated recording equipment and facilities to produce high-quality recordings, making it impractical or costly to visit larger recording studios, especially for remote locations.
Innovation Solution
A media production platform utilizing a flipped vocoder approach, machine learning frameworks, and a room simulator module to manipulate noisy audio signals, converting them into studio-quality recordings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If individuals use basic recording equipment, then accessibility and ease of use are improved, but recording quality deteriorates
Solution Approach 1:
The patent introduces a machine learning model as an intermediary component between the basic recording equipment and the final audio output. The model processes the raw audio signal from simple microphones and transforms it into studio-quality sound, effectively mediating between low-cost input and high-quality output without requiring direct access to expensive recording facilities
Solution Approach 2:
The patent replaces the mechanical/acoustic system of professional recording studios (specialized rooms, acoustic treatment, expensive microphones) with a computational system based on machine learning algorithms. Instead of physically isolating recordings in acoustically treated spaces, the system uses software-based processing to achieve similar quality outcomes
2Manufacturing precision
If individuals visit professional recording studios, then recording quality is improved, but cost and accessibility deteriorate
Solution Approach 1:
The patent creates a computational copy of the acoustic characteristics and processing capabilities of professional recording studios. By training machine learning models on studio-recorded audio data, the system learns to replicate studio-quality sound production, making these capabilities available through software rather than requiring physical presence in expensive facilities
Solution Approach 2:
The patent develops a universal audio processing system that can be deployed on various devices (smartphones, computers, tablets) through cloud-based or local machine learning models. This single system serves the function previously requiring access to specialized recording studios, making professional-quality recording capability universally accessible across different platforms and locations
3Ease of operation
If noisy audio signals are recorded in non-studio environments, then accessibility is improved, but audio quality and noise levels deteriorate
Solution Approach 1:
The patent trains machine learning models using noisy, non-studio audio recordings as training data, converting the previously harmful noise into useful learning signals. The model learns to identify and separate desired audio content from background noise by exposure to real-world noisy environments, then applies this learned capability to clean up new recordings made in similar conditions
Solution Approach 2:
The patent performs preliminary training of machine learning models with noisy audio data before actual recording takes place. This pre-training phase enables the model to develop noise reduction capabilities in advance, so when the model processes new recordings, it can effectively remove noise that would otherwise degrade audio quality
Data Source
AI summary
Introduced here are computer programs and associated computer-implemented techniques for manipulating noisy audio signals to produce clean audio signals that are sufficiently high quality so as to be largely, if not entirely, indistinguishable from “rich” recordings generated by recording studios. When a noisy audio signal is obtained by a media production platform, the noisy audio signal can be manipulated to sound as if recording occurred with sophisticated equipment in a soundproof environment. Manipulation can be performed by a model that, when applied to the noisy audio signal, can manipulate its characteristics so as to emulate the characteristics of clean audio signals that are learned through training.