Hierarchical Time-Series Encoding for Audio Feature Extraction
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Solution Overview
Problem
Conventional time-series data analysis systems fail to effectively uncover long-timescale information, such as rhythm and phrasing in audio data, while successfully capturing short-timescale information like local pitch and timbre.
Innovation Solution
A computing device identifies codewords in separate codebooks to represent short- and long-timescale information, generating a third codebook that combines these to provide a contextual representation, using algorithms like Winner-Take-All and tensor products to integrate short- and long-timescale features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional data coding schemes are used, then short-timescale information is successfully extracted, but long-timescale information fails to be uncovered
Solution Approach 1:
The patent segments time-series data into multiple temporal scales by creating separate codebooks for short-timescale and long-timescale information. The short-timescale codebook captures local pitch and timbre, while the long-timescale codebook captures rhythm and phrasing, allowing each scale to be processed independently and then integrated through hierarchical combination.
Solution Approach 2:
The patent introduces a hierarchical dimension to the encoding process by organizing codebooks into multiple levels. The first codebook represents short-timescale features, the second codebook represents long-timescale features, and the third codebook combines both levels, effectively adding a temporal hierarchy dimension to the traditional single-scale encoding approach.
2Measurement precision
If traditional time-series analysis systems are used, then local pitch and timbre are identified, but rhythm and phrasing remain undetected
Solution Approach 1:
The system segments the analysis task into two distinct parts: short-timescale analysis for local pitch and timbre using the first codebook, and long-timescale analysis for rhythm and phrasing using the second codebook. This segmentation allows each codebook to be optimized for its specific temporal scale, improving detection accuracy for both types of information.
Solution Approach 2:
The patent performs preliminary encoding of short-timescale information into the first codebook before generating the long-timescale codebook. The second codebook is generated by creating histograms of the first codebook's codewords over longer durations, allowing rhythm and phrasing to be detected based on the temporal distribution of previously encoded short-timescale features.
3Loss of information
If separate codebooks for short- and long-timescale information are generated, then comprehensive representation is achieved, but system complexity increases
Solution Approach 1:
The patent merges the short-timescale and long-timescale representations by generating a third codebook that combines codewords from both the first and second codebooks. This hierarchical combination integrates local pitch/timbre information with rhythm/phrasing context, achieving comprehensive temporal representation while organizing the complexity into a structured three-level system.
Data Source
AI summary
A computing device identifies a first codeword in a first codebook to represent short-timescale information of frames in a time-based data item segmented at intervals and identifies a second codeword in a second codebook to represent long-timescale information of the frames. The computing device generates a third codebook based on the first codeword and the second codeword for the frames to add long-timescale information context to the short-timescale information of the frames.