Audio Repair Apparatus Harmonic Signal Differentiation
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Solution Overview
Problem
Existing audio repair and restoration techniques struggle to accurately distinguish between genuine disturbance events and natural harmonics, particularly in brass music, and fail to provide users with efficient and reliable methods for selecting which audio portions to repair, especially in dialogue recordings.
Innovation Solution
A method and apparatus that process harmonic signals to calculate a difference signal by subtracting a predicted harmonic signal from a detected signal, allowing users to visually distinguish between disturbance and natural harmonics through an events display, with adjustable thresholds and sensitivity controls for selective repair, and separate settings for dialogue and pauses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If predictor algorithms are used to identify disturbance events, then disturbance detection capability is improved, but false identification of natural harmonics as disturbances increases
Solution Approach 1:
The audio signal is segmented into multiple frequency bands using a filter bank, allowing independent analysis of different frequency ranges. This segmentation enables the system to distinguish between disturbance events and natural harmonics by examining their distribution across frequency bands, reducing false identification while maintaining detection accuracy.
Solution Approach 2:
The invention adds a temporal dimension to the analysis by examining the duration and evolution of excitation events across multiple frames. By analyzing events in both frequency and time domains simultaneously, the system can differentiate between short-term disturbances and sustained natural harmonics, improving reliability without sacrificing detection precision.
2Productivity
If aggressive repair parameters are used, then disturbance removal effectiveness is improved, but natural harmonics are also repaired affecting sound quality
Solution Approach 1:
The system applies different repair intensities to different frequency bands and time regions based on local characteristics. By analyzing the spectral content and temporal patterns in each segment, the system selectively applies aggressive repair only where disturbance events are confidently identified, while preserving natural harmonics in other regions, thus maintaining sound quality integrity.
Solution Approach 2:
The repair parameters are made dynamic and adaptive rather than fixed. The system continuously adjusts repair intensity based on real-time analysis of the audio signal characteristics, automatically reducing aggression when natural harmonics are detected and increasing it when disturbances are identified, balancing effectiveness with quality preservation.
3Measurement precision
If manual automation is used to set parameters for dialogue sections, then repair accuracy for speech is improved, but processing time and labor increase significantly
Solution Approach 1:
The system automatically detects dialogue sections and pauses, and self-configures appropriate repair parameters without requiring manual intervention. By analyzing speech patterns, silence durations, and acoustic characteristics, the system autonomously adapts threshold levels and repair intensities for different sections, achieving high accuracy while eliminating manual labor and processing delays.
Solution Approach 2:
The system performs preliminary analysis of the entire audio signal to identify dialogue sections, pauses, and their characteristics before applying repair. This advance preparation allows the system to pre-configure optimal parameters for each section, ensuring accurate speech repair while minimizing processing time during the actual repair phase.
4Manufacturing precision
If lower repair settings are used for dialogue, then natural voice harmonics are preserved, but background noise during pauses remains unrepaired
Solution Approach 1:
The system dynamically switches between different repair parameter sets based on the detected audio state. During dialogue sections, lower settings preserve natural voice harmonics, while during pauses, the system automatically transitions to higher settings to effectively remove background noise, thus addressing both requirements through adaptive parameter control.
Solution Approach 2:
The audio signal is segmented into dialogue sections and pause sections, with distinct repair strategies applied to each. By identifying and separating these temporal regions, the system can use conservative settings for speech preservation and aggressive settings for noise removal in pauses, eliminating the need to choose a single compromise setting.
Data Source
AI summary
An audio recording may be repaired by implementing a processor to process the harmonic signal content of the audio recording; calculating a difference signal from the harmonic signal content by subtracting a predicted harmonic signal from a detected harmonic signal, the predicted harmonic signal being obtainable by a predictor means and the detected harmonic signal being detected by the processor; defining a threshold for the difference signal above which the difference signal indicates the occurrence of one or more acoustic excitation events; and thereby producing an events display which allows an operator to visually distinguish between indicated excitation events that are present as a result of a disturbance in the audio recording and indicated excitation events that are present as a result of natural harmonics in the audio recording; and repairing one or more of the displayed excitation events by a repair circuit which is in communication with the processor.