Machine Learning Audio Signal Manipulation for Studio-Quality Recordings
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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 machine learning framework with a superset model, comprising a flipped vocoder and adversarial training, to manipulate noisy audio signals and simulate studio conditions, producing studio-quality recordings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If individuals use basic recording equipment at home, then accessibility and cost are improved, but recording quality deteriorates
Solution Approach 1:
The patent replaces physical acoustic treatment systems and professional recording equipment with a machine learning-based digital signal processing system. The neural network model processes audio signals computationally to achieve studio-quality recordings, substituting mechanical/acoustic systems with algorithmic ones.
Solution Approach 2:
The system transforms audio signals by applying learned transformations from training data. The model adjusts acoustic parameters such as reverberation, echo, and noise characteristics digitally, changing the audio signal parameters to match professional studio recordings without physical acoustic treatment.
2Manufacturing precision
If individuals visit professional recording studios, then recording quality is improved, but time and cost increase
Solution Approach 1:
The system creates digital copies of professional studio acoustic characteristics by training on studio recordings. The neural network learns and replicates the acoustic properties of professional studios, allowing home recordings to be transformed into studio-quality outputs without physical presence in a studio.
Solution Approach 2:
The acoustic treatment and quality enhancement is performed as a preliminary digital processing step on recorded audio. The machine learning model applies pre-trained transformations to raw recordings, achieving studio quality before final production, eliminating the need for time-consuming studio sessions.
3Manufacturing precision
If acoustic treatment is applied to home recording spaces, then recording quality is improved, but complexity and cost of setup increase
Solution Approach 1:
The patent replaces physical acoustic treatment devices (foam panels, bass traps, diffusers) with a software-based neural network model. The digital signal processing system achieves acoustic improvement without requiring complex physical setup or specialized equipment in the recording space.
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.