Audio Pulse Phase Coherence in Signal Transformation
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Solution Overview
Problem
Existing audio signal transformation techniques fail to preserve phase coherence between harmonics at voice pulse onsets, leading to suboptimal quality in pitch and time scaling transformations, especially in voice processing and synthesis applications.
Innovation Solution
The method processes time domain digital audio samples to derive audio pulses represented as vectors of sinusoids, determines starting times to reduce phase differences, and applies windowing operations based on transformation parameters like pitch transposition, time scaling, and timbre modification, ensuring shape-invariant transformations that preserve phase coherence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional audio signal transformation techniques are used, then pitch and time scaling transformations can be performed, but phase coherence between harmonics at voice pulse onsets is not preserved, leading to suboptimal quality
Solution Approach 1:
The patent applies windowing operations to audio pulses before transformation to pre-align phase characteristics. By determining optimal starting times for audio pulses based on their characteristic frequencies and reducing phase differences in advance, the system ensures phase coherence is maintained during pitch and time scaling transformations, directly resolving the contradiction between transformation capability and quality preservation
2Manufacturing precision
If high-quality phase-coherent transformations are achieved, then voice synthesis and processing quality improves, but computational complexity increases
Solution Approach 1:
The patent segments the audio signal into individual audio pulses, each associated with a characteristic frequency. By processing each pulse independently with determined starting times and applying windowing operations selectively, the system achieves phase-coherent transformations while managing computational complexity through localized processing rather than global signal processing
Solution Approach 2:
The patent transforms audio pulses by changing their starting times based on characteristic frequencies to reduce phase differences. This parameter adjustment approach enables phase coherence preservation through mathematical optimization of pulse timing rather than complex computational algorithms, balancing transformation quality with computational efficiency
3Measurement precision
If audio pulses are processed with determined starting times to reduce phase differences, then time-frequency resolution improves, but processing time increases
Solution Approach 1:
The system determines starting times for audio pulses automatically based on their characteristic frequencies without requiring manual intervention or iterative optimization. The phase difference reduction is achieved through direct calculation from frequency information, enabling the system to self-optimize time-frequency resolution while minimizing processing time overhead
Data Source
AI summary
A sequence is received of time domain digital audio samples representing sound (e.g., a sound generated by a human voice or a musical instrument). The time domain digital audio samples are processed to derive a corresponding sequence of audio pulses in the time domain. Each of the audio pulses is associated with a characteristic frequency. Frequency domain information is derived about each of at least some of the audio pulses. The sound represented by the time domain digital audio samples is transformed by processing the audio pulses using the frequency domain information. The sound transformation utilizes overlapping windows and a computational cost function which depends on a product of the number of the pitch periods and the inverse of the minimum fundamental frequency within the window is determined.