Audio Metadata Reconstruction via Voiceprint and NLP Analysis
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Solution Overview
Problem
Audio recordings without metadata lack essential information such as speaker identification, conversation timing, and sentiment analysis, making it difficult to acquire and analyze audio content effectively.
Innovation Solution
A system and method that reconstructs metadata by extracting characteristics from audio sources using voiceprints and natural language processing (NLP) to identify speakers, create transcripts, and analyze sentiment, thereby filling in missing metadata information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If audio recordings are made without metadata to simplify the recording process, then the ease of operation is improved, but the loss of information increases due to missing speaker identification, timing, and sentiment data
Solution Approach 1:
The system performs preliminary metadata extraction and reconstruction by analyzing audio characteristics, voiceprints, and transcripts before the audio file is fully processed or archived. This preliminary action ensures that even if metadata was not captured during recording, it can be reconstructed later using machine learning models that process the audio content to infer speaker identities, emotions, and contextual information.
Solution Approach 2:
The patent replaces traditional mechanical metadata capture methods (manual tagging or system-generated metadata during recording) with an automated machine learning-based analysis system. The ML models process audio signals, voice patterns, and transcript data to automatically reconstruct metadata, substituting manual or system-level mechanical processes with intelligent automated analysis.
2Ease of manufacture
If traditional post-processing techniques are used to add basic metadata, then the ease of manufacture is improved, but the loss of information persists for complex data such as speaker characteristics and sentiment
Solution Approach 1:
The system replaces traditional post-processing metadata restoration techniques with machine learning-based analysis. Instead of using basic system metadata or manual tagging, the ML models analyze audio signals, voiceprints, and transcript content to automatically extract speaker identities, emotional states, and contextual information, thereby recovering complex data that traditional methods cannot obtain.
Solution Approach 2:
The patent introduces transcripts and voiceprint analysis as intermediary elements between the raw audio and the final metadata reconstruction. The system first generates transcripts from speech recognition, extracts voiceprints for speaker identification, and then uses these intermediaries to inform the metadata reconstruction process, enabling more accurate recovery of speaker and sentiment information than direct post-processing alone.
3Loss of information
If machine learning processes are used to analyze audio outputs and reconstruct metadata, then the loss of information is reduced, but the device complexity increases
Solution Approach 1:
The system segments the metadata reconstruction process into distinct functional modules: audio preprocessing, speech-to-text transcription, voiceprint extraction for speaker identification, sentiment analysis, and metadata assembly. Each module handles a specific aspect of the analysis, allowing the complex overall task to be managed through specialized, independent components that can be developed, tested, and optimized separately.
Solution Approach 2:
The patent uses transcripts and voiceprint data as intermediary representations that bridge the gap between raw audio and final metadata. These intermediaries simplify the complexity by breaking down the analysis into manageable stages: first converting audio to text, then extracting speaker characteristics from voiceprints, and finally synthesizing metadata from these structured intermediaries rather than attempting to extract all information directly from raw audio in a single complex step.
Data Source
AI summary
The disclosed invention provides system and method to reconstruct metadata of audio outputs in which portions of metadata are missing. The system and method of the disclosed invention utilizes characteristics of speakers in audio outputs, voiceprints to identify speakers, and transcripts of the audio outputs to further analyze the audio outputs through machine learning process. The metadata reconstruction system performs operations that include isolating the metadata of the audio output, detecting missing portions of the metadata, detecting characteristics of speakers involved in the audio output, identifying the speakers from the characteristics of the speakers by utilizing voiceprints of speakers, creating a transcript of the audio output, analyzing the transcript by using natural language processing (NLP), annotating the transcript with identified speakers, constructing metadata with the identified speakers and results of the analysis of the transcript, and recombining the constructed metadata with the audio output to produce reconstructed audio output.