Audio Acoustic Impulse Response Convolution for Realistic Dubbing
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Solution Overview
Problem
Conventional methods for dubbing dialog into different languages and adding audio elements to audiovisual content struggle to accurately replicate the acoustic characteristics of environments, leading to unrealistic and inconsistent sound, which can be distracting for viewers.
Innovation Solution
A computer-implemented method using a machine learning model trained with acoustic impulse responses and environment profiles to modify dubbed dialog and audio elements, ensuring they sound as if recorded in specific environments, such as rooms or outdoor venues, by combining these responses with audio samples to create a realistic audio track.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional dubbing methods are used to translate dialog into different languages, then the translation efficiency is improved, but the acoustic realism of the dubbed dialog deteriorates
Solution Approach 1:
The patent introduces an acoustic impulse response as an intermediary element that mediates between the anechoic dubbed dialog and the target environment. By convolving the dubbed dialog with the appropriate impulse response, the system transfers acoustic characteristics from reference recordings to the dubbed content, achieving realistic environmental simulation without manual processing
Solution Approach 2:
The patent replaces the manual mechanical process of audio engineering with an automated computational system. Machine learning models automatically identify acoustic environments and select appropriate impulse responses, substituting human expertise with algorithmic processing that consistently applies acoustic transformations to dubbed dialog
2Manufacturing precision
If manual audio processing is used to manipulate dubbed versions, then the acoustic characteristics can be adjusted, but the time consumption and labor intensity increase
Solution Approach 1:
The system enables self-service processing where the dubbed dialog automatically receives appropriate acoustic treatment without human intervention. The machine learning model autonomously analyzes the target environment, selects the matching impulse response, and applies the convolution, making the entire acoustic processing pipeline self-operating
Solution Approach 2:
The patent pre-computes and stores acoustic impulse responses from various environments in advance. These pre-prepared acoustic signatures are readily available when needed, eliminating the need for real-time environmental analysis and impulse response generation during the dubbing process
3Reliability
If anechoic recordings are used for dubbed dialog, then the recording quality in controlled environments is improved, but the realism when played back in real environments deteriorates
Solution Approach 1:
The patent applies different acoustic qualities to different parts of the audio signal. The dubbed dialog itself maintains high fidelity from the anechoic recording, while the impulse response adds localized environmental characteristics. This allows the core speech quality to remain excellent while the environmental context becomes authentic
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach automates the process of creating realistic-sounding audio tracks, reducing the impact of human bias and inconsistencies, and provides a more immersive viewing experience by accurately replicating environmental acoustics, thus enhancing the audiovisual content.
Implementation Method 1
Each environment profile may be associated with a specific acoustic impulse response, which corresponds to (and represents) the acoustic properties of the environment (e.g., echo, reverb, damping, etc.)
Implementation Method 2
The acoustic characteristics may include reverb, decay of certain frequencies, etc.
Data Source
AI summary
The disclosed computer-implemented method may include obtaining an audio sample from a content source, inputting the obtained audio sample into a trained machine learning model, obtaining the output of the trained machine learning model, wherein the output is a profile of an environment in which the input audio sample was recorded, obtaining an acoustic impulse response corresponding to the profile of the environment in which the input audio sample was recorded, obtaining a second audio sample, processing the obtained acoustic impulse response with the second audio sample, and inserting a result of processing the obtained acoustic impulse response and the second audio sample into an audio track. Various other methods, systems, and computer-readable media are also disclosed.