Audio Time Stretching with Cluster-Based Compression Rates
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Solution Overview
Problem
Conventional audio time stretching techniques apply a fixed compression rate to all speech signals, leading to distortion during playback, especially when pronunciation features are not considered.
Innovation Solution
Classify audio into clusters based on pronunciation features and apply different compression rates to each cluster, using silence intervals and dynamic time warping to minimize distortion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a fixed compression rate is applied to all speech signals, then the processing is simple and fast, but distortion occurs during playback especially when pronunciation features are not considered
Solution Approach 1:
The speech signal is segmented into multiple clusters based on pronunciation features such as phoneme type, position in utterance, and acoustic characteristics. Each cluster is then processed with a customized compression rate, allowing high-speed processing while maintaining audio quality through feature-based segmentation.
Solution Approach 2:
The compression rate is made dynamic by adjusting it according to the specific cluster characteristics. Different compression rates are applied dynamically based on the pronunciation features detected in each frame, enabling adaptive processing that balances speed and quality.
2Manufacturing precision
If different compression rates are applied to each cluster based on pronunciation features, then audio distortion is minimized, but the processing complexity increases
Solution Approach 1:
The system changes the compression rate parameter based on detected pronunciation features. By monitoring features such as phoneme type, position, and acoustic characteristics, the system automatically adjusts the compression rate parameter to optimize audio quality without manual intervention.
Solution Approach 2:
The system performs self-service by automatically detecting pronunciation features and determining appropriate compression rates without external control. The clustering algorithm autonomously analyzes the speech signal characteristics and applies suitable compression rates, reducing the need for complex external processing.
3Ease of operation
If the same compression rate is applied to every speech signal, then the processing is uniform and simple, but distortion occurs when pronunciation features vary
Solution Approach 1:
Different compression rates are applied to different local regions of the speech signal based on pronunciation features. Each cluster representing specific phoneme characteristics receives a customized compression rate, ensuring that local audio quality is optimized according to the specific features of each segment.
Data Source
AI summary
A device for controlling audio time stretching includes a silence interval unit configured to detect a silence interval of an audio, a cluster unit configured to classify at least one of frames except the detected silence interval of the audio to plural clusters and a script unit configured to set compression rate to the clusters and generate a speed script including information concerning the clusters with the set compression rate. Here, one or more of the clusters have different compression rate from another cluster.