Deep Learning Audio Translation for Lyric Synchronization
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Solution Overview
Problem
Current translation methods of original works, such as musical pieces, fail to preserve cultural connotations, rhythm, and other features when translating lyrics from one language to another, resulting in a lack of quality and authenticity in the translated versions.
Innovation Solution
A computer-implemented method and system using deep learning models to translate audio data, synchronizing translated text with the original voice features and audio properties, allowing the translated lyrics to be sung in the original voice and rhythm, thereby retaining the cultural nuances and musicality of the original work.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If literal translation or robotic reading is used for translating lyrics, then translation speed is improved, but translation quality and cultural connotation preservation deteriorate
Solution Approach 1:
The patent introduces deep learning models as an intermediary between the source lyrics and target translation. The system uses audio data of the original work as a mediator to teach the model voice features, rhythm, and cultural nuances, enabling the translation to preserve these elements while achieving automated translation.
Solution Approach 2:
The system changes the parameters of translation by incorporating multiple dimensions beyond literal meaning. It adjusts for voice characteristics, rhythm patterns, tempo, and cultural context when generating translations, transforming the translation process from simple text substitution to a multi-parameter optimization problem.
2Loss of information
If deep learning models are used to learn voice features and audio properties, then translation quality and cultural connotation preservation are improved, but system complexity increases
Solution Approach 1:
The patent segments the translation system into distinct functional modules: an audio analysis module that extracts voice features and audio properties, a translation module that generates target language lyrics, and a synthesis module that combines translated text with original musical characteristics. This segmentation manages complexity by distributing functions across specialized components.
Solution Approach 2:
The deep learning model serves as an intermediary that learns and stores cultural connotations, voice features, and rhythmic patterns from training data. This intermediary captures complex cultural information that would otherwise be difficult to encode, simplifying the overall system architecture by centralizing knowledge representation in the trained model.
3Stability of the object's composition
If translated text is synchronized to original text position in synthesized voice, then rhythm and tempo preservation are improved, but synchronization precision requirements increase
Solution Approach 1:
The system performs preliminary analysis of the original audio data to extract timing information, rhythm patterns, and tempo characteristics before generating the translation. By pre-processing the audio to identify structural elements and timing markers, the system establishes a framework that guides subsequent synchronization operations, reducing the precision burden during final alignment.
Solution Approach 2:
The deep learning model uses feedback from the original audio's rhythmic and temporal structure to adjust the timing and placement of translated lyrics. The system continuously refines synchronization by comparing generated output against the original tempo and rhythm patterns, making real-time adjustments to maintain rhythmic integrity.
Data Source
AI summary
Audio data of an original work is received. Text in the audio data is translated to a target language. The audio data is passed to a first deep learning model to learn voice features in the audio data. The audio data is passed to a second deep learning model to learn audio properties in the audio data. The translated text is synchronized to play in the position of original text of the original work in a synthesized voice. A translated audio data of the original work is created by combining the synchronized translated text in the synthesized voice with music of the audio data.