Audio Preparation System for Broadcast-Quality Vocal Normalization
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Solution Overview
Problem
Vocal recordings made outside professional studios often suffer from inconsistency and amateurish quality due to variations in recording hardware/software, microphone positioning, and environmental noise, making it challenging to achieve broadcast-standard audio.
Innovation Solution
The system analyzes audio signals using machine learning models, such as convolutional neural networks, to identify necessary adjustments for noise removal, timbral profiling, and dynamic range compression, enabling the adjustment of audio parameters to achieve professional-studio quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If recordings are made in home studios or remote locations, then accessibility and convenience are improved, but audio quality consistency deteriorates
Solution Approach 1:
The system enables self-service audio mastering through automated analysis and processing. The machine learning model automatically detects audio quality issues and applies appropriate remediation without requiring human operators to have specialized mastering skills, allowing anyone to achieve professional-quality audio from any recording location.
Solution Approach 2:
The system dynamically adjusts multiple audio parameters including noise floor thresholds, dynamic range compression ratios, equalization curves, and de-essing levels based on the specific characteristics of each recording. These parameter changes are automatically optimized to compensate for variations in recording environments, hardware, and techniques.
2Manufacturing precision
If automated processing is applied to fix audio issues, then audio quality is improved, but processing complexity increases
Solution Approach 1:
The patent replaces manual audio mastering operations with an automated machine learning system. The machine learning model analyzes audio characteristics and automatically applies processing, substituting the mechanical process of manual adjustment with an intelligent automated system that reduces operational complexity while maintaining or improving audio quality.
3Manufacturing precision
If multiple processing steps are applied to remediate audio issues, then audio quality is improved, but processing time increases
Solution Approach 1:
The system performs preliminary analysis of the audio signal to identify specific quality issues before applying processing. The machine learning model pre-determines which remediation steps are necessary based on the recorded audio characteristics, allowing for optimized processing sequences that address only the identified issues rather than applying all possible processing steps.
Data Source
AI summary
The present application relates to systems and methods for audio preparation and delivery. Such systems and methods may involve a controller configured to carry out operations. The operations include receiving source audio comprising a vocal portion. The operations also include selecting, using a trained machine learning model, a primary voice profile based on an analysis of the vocal portion of the received source audio. The primary voice profile is selected from a plurality of predetermined voice profiles. The operations also include adjusting, based on the selected primary voice profile, at least a portion of the source audio. The operations also include providing output audio based on the adjusted portion of source audio.