Probabilistic Audio Matching Reduces Computational Overhead
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current audio matching systems for identifying television programs and channels require extensive computing resources due to the large number of channels and time-shifted content, often using inefficient brute force methods that result in unnecessary oversampling and analysis.
Innovation Solution
A probabilistic audio matching system that employs regionalized central servers and uses probabilistic processing to quickly and accurately identify candidate matches between audio samples and reference audio, considering the probability of channel selection and coherent sequences of matches over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If brute force methods are used to compare audio samples against all channels, then comprehensive channel identification is achieved, but computational overhead increases significantly
Solution Approach 1:
The patent segments the large audio database into multiple partitions or subsets. Instead of comparing the audio sample against all channels simultaneously, the system divides the search space into manageable segments, processing each segment separately. This reduces the computational complexity from O(n) to O(n/k) where k is the number of segments, while maintaining identification accuracy through systematic coverage of all partitions.
Solution Approach 2:
The system performs preliminary actions by pre-processing and organizing audio data into structured formats before the actual matching process. Audio references are pre-computed and stored in optimized data structures, allowing the matching algorithm to work with pre-prepared features rather than raw audio data. This preliminary organization significantly reduces the computational burden during real-time channel identification.
2Reliability
If all channels are analyzed to ensure accurate identification, then identification reliability improves, but processing time increases
Solution Approach 1:
The patent applies partial action by analyzing only a subset of channels that are most relevant to the current audio sample, rather than exhaustively processing all channels. The system uses heuristics or metadata to identify a reduced set of candidate channels that have non-zero probability of matching, processing only these partial results. This approach achieves sufficient identification accuracy while dramatically reducing processing time by avoiding analysis of clearly irrelevant channels.
Solution Approach 2:
The system implements periodic action by processing channel comparisons in repeated cycles or batches rather than in a single exhaustive pass. Audio samples are matched against channels in periodic intervals, with results accumulated and refined over multiple cycles. This allows the system to provide incremental identification results faster, improving effective processing throughput while maintaining final identification accuracy through repeated verification.
3Measurement precision
If extensive audio data is stored for all channels and time slices, then matching accuracy is improved, but data storage requirements increase
Solution Approach 1:
The patent extracts only the essential and discriminative features from the audio data, storing only these extracted features rather than the complete raw audio recordings for all channels. By taking out and storing only the critical acoustic fingerprints, spectral features, or other distinctive characteristics, the system maintains matching accuracy while significantly reducing the volume of stored data. Redundant or less informative audio portions are excluded from storage.
Solution Approach 2:
Instead of storing complete audio recordings and performing full audio processing during matching, the system inverts the approach by pre-computing and storing only the essential matching features, then performing lightweight comparisons during actual channel identification. This inversion of the processing-storing paradigm reduces storage requirements while maintaining or even improving matching efficiency and accuracy.
Data Source
AI summary
A system for probabilistic matching of audio sample from a television program includes a computer readable storage medium having encoded thereon an audio matching program; and a processor that executes the audio matching program. The processor receives reference audio for time slices corresponding to a broadcast television program for each of a plurality of channels broadcasting television programming; receives a first audio sample from a media device on which is displayed the broadcast television program; selects one or more candidate channels to test to determine a channel to which the media device is tuned; compares the first audio sample to the selected candidate channels to determine a match between the first audio sample and one or more of the selected candidate channels; and when the first audio sample matches a reference audio from a selected candidate channel, identifying, with a specified probability, the selected candidate channel as the tuned to channel.