Dialog Detector Using Multi-Scale Context Windows
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Solution Overview
Problem
Conventional dialog detectors introduce latency in detection and struggle to extract robust rhythmic features, leading to inaccurate discrimination between speech and singing voices, which affects robustness and real-time applications.
Innovation Solution
The use of different-length context windows, including short-term, mid-term, and long-term contexts, to represent audio frames, combined with adaptive determination methods based on amplitude and spectral information, and a pre-cleaning method to remove uncorrelated noise, along with a music content detector to refine dialog confidence scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single fixed-length context window is used for audio feature extraction, then the device complexity is low and the processing is simple, but the detection accuracy is insufficient and rhythmic features cannot be robustly extracted
Solution Approach 1:
The patent divides the audio signal analysis into multiple context windows of different lengths (short-term, mid-term, and long-term contexts). Each context window extracts features at different temporal scales, allowing the system to capture both local rhythmic patterns and broader speech characteristics. This segmentation enables robust rhythmic feature extraction without requiring an overly complex single-window system.
Solution Approach 2:
The patent introduces a temporal dimension by using context windows of varying lengths rather than a single fixed length. This multi-scale temporal analysis adds a dimensional aspect to feature extraction, enabling the system to capture rhythmic patterns at different time scales simultaneously, thereby improving detection accuracy through multi-resolution analysis.
2Reliability
If a long context window is used to improve robustness and rhythmic feature extraction, then the detection accuracy improves, but the response speed decreases due to increased processing time
Solution Approach 1:
The patent segments the context analysis into multiple parallel streams with different window lengths. The short-term context provides rapid response for immediate rhythmic changes, while mid-term and long-term contexts provide robustness for sustained patterns. This segmentation allows the system to deliver responses at multiple time scales simultaneously, maintaining both speed and reliability.
Solution Approach 2:
The patent applies partial action by using multiple context windows of different lengths rather than requiring analysis of the entire long-term context for every detection decision. The short-term and mid-term contexts can provide sufficient information for many rapid decisions, while the long-term context provides additional robustness when needed, avoiding the full processing overhead of always analyzing the complete long-term window.
3Productivity
If multiple context windows of different lengths are used to improve detection accuracy and response speed, then the detection performance improves, but the computational complexity and processing load increase
Solution Approach 1:
The patent segments the feature extraction process into multiple independent context window analyses that can be performed in parallel. Each context window operates independently to extract features at its specific temporal scale, allowing for parallel computation that reduces overall processing time despite the increased number of operations. This segmentation makes the computational complexity more manageable through parallelization.
4Measurement precision
If conventional audio feature extraction is used, then the processing is fast and simple, but the discrimination between speech and singing voices is inaccurate
Solution Approach 1:
The patent segments the audio analysis into multiple context windows that extract rhythmic and spectral features at different temporal resolutions. This multi-scale segmentation enables the system to distinguish between speech and singing voices by analyzing patterns across different time scales, capturing both fine-grained articulation details and broader prosodic characteristics that conventional single-window methods miss.
Data Source
AI summary
The present application relates to a method of extracting audio features in a dialog detector in response to an input audio signal, the method comprising dividing the input audio signal into a plurality of frames, extracting frame audio features from each frame, determining a set of context windows, each context window including a number of frames surrounding a current frame, deriving, for each context window, a relevant context audio feature for the current frame based on the frame audio features of the frames in each respective context, and concatenating each context audio feature to form a combined feature vector to represent the current frame. The context windows with the different length can improve the response speed and improve robustness.