Audio Correlation via Latent Evidence Vectors
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Solution Overview
Problem
Current systems fail to efficiently identify and correlate audio snippets across multiple audio files, especially when recorded in different environments with varying background noises and attenuations, making it challenging to find related multimedia files in large databases.
Innovation Solution
The method involves extracting audio streams, converting them into latent evidence indices, and using the Viterbi algorithm and Kernalized Locality-Sensitive Hashing to determine the relatedness between query and database videos, allowing for fast and efficient identification of matching audio snippets despite environmental differences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If exact match algorithms are used to find audio snippets, then precision of matching is improved, but the system cannot handle recordings with different background noises and attenuations from multiple cameras
Solution Approach 1:
The patent transforms the audio matching problem from exact parameter matching to probabilistic parameter estimation. Instead of requiring exact matches of audio snippets, the system estimates latent evidence probability vectors that represent the likelihood of different audio events occurring. This allows the system to handle variations in background noise, attenuation, and recording conditions while maintaining matching accuracy through probabilistic reasoning rather than deterministic comparison.
2Reliability
If traditional audio matching systems are used, then exact duplicates can be found, but related audio snippets from different environments cannot be correlated
Solution Approach 1:
The patent introduces latent evidence probability vectors as an intermediary representation between raw audio snippets and matching results. These probability vectors serve as a mediator that abstracts away environmental variations (background noise, attenuation, camera-specific characteristics) while preserving the essential information needed for correlation. By comparing these intermediary representations rather than raw audio data, the system achieves reliable correlation across different recording environments.
3Measurement precision
If comprehensive audio analysis is performed across large databases, then correlation accuracy is improved, but processing time increases significantly
Solution Approach 1:
The patent performs preliminary action by pre-computing and storing latent evidence probability vectors for all audio snippets in the database during an indexing phase. This preliminary processing transforms the raw audio data into a compact probabilistic representation that can be quickly compared during query operations. When a correlation query is executed, the system only needs to compare the pre-computed probability vectors rather than performing comprehensive audio analysis on all database files, dramatically reducing processing time while maintaining correlation accuracy.
Data Source
AI summary
A method for determining a relatedness between a query video and a database video is provided. A processor extracts an audio stream from the query video to produce a query audio stream, extracts an audio stream from the database video to produce a database audio stream, produces a first-sized snippet from the query audio stream, and produces a first-sized snippet from the database audio stream. An estimation is made of a first most probable sequence of latent evidence probability vectors generating the first-sized audio snippet of the query audio stream. An estimation is made of a second most probable sequence of latent evidence probability vectors generating the first-sized audio snippet of the database audio stream. A similarity is measured between the first sequence and the second sequence producing a score of relatedness between the two snippets. Finally a relatedness is determined between the query video and a database video.