Sound Determination Device Using Phase Distance Analysis
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Solution Overview
Problem
Existing sound determination devices fail to accurately determine the frequency signal of a to-be-extracted sound within a mixed sound for each time-frequency domain, particularly struggling with sounds like engine sounds with varying pitch cycles and toneless sounds distorted by noise.
Innovation Solution
A noise elimination device that includes a frequency analysis unit and a to-be-extracted sound determination unit, which calculates phase distances between frequency signals to discriminate between toned and toneless sounds by analyzing the cyclic repetition of phase patterns, allowing for precise identification of frequency signals in each time-frequency domain.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If pitch cycle extraction is performed for each time domain, then the sound recognition process is simplified, but it becomes impossible to determine the frequency signal of the to-be-extracted sound for each time-frequency domain and to accurately identify sounds with varying pitch cycles
Solution Approach 1:
The patent segments the sound analysis into time-frequency domains by performing frequency analysis at multiple time points. Instead of analyzing the entire time domain at once, the system divides it into discrete time segments and performs spectral analysis on each segment, enabling precise identification of frequency signals at specific time-frequency locations while maintaining manageable computational complexity.
Solution Approach 2:
The patent transitions from one-dimensional time domain analysis to two-dimensional time-frequency domain analysis. By introducing the frequency dimension through spectral analysis at multiple time points, the system creates a time-frequency representation that allows precise localization and determination of frequency signals for toned sounds, including those with varying pitch cycles.
2Measurement precision
If determination is based on spectrum shape such as harmonic structure and centroid frequency, then toned sounds can be identified, but when large noise is superimposed and the spectrum shape is distorted, the to-be-extracted sound cannot be determined
Solution Approach 1:
The patent performs preliminary frequency analysis at multiple time points to establish the time-varying spectral characteristics of the sound before making determination. By analyzing the frequency content at each time segment and tracking how these characteristics evolve over time, the system builds a robust temporal-spectral profile that can distinguish toned sounds from noise even when the spectrum is distorted by noise superposition.
Solution Approach 2:
The system uses feedback from multiple frequency analysis results at different time points to iteratively refine the sound determination. By examining the consistency and evolution of spectral patterns across time segments, the system can distinguish true toned sound characteristics from noise-induced spectral distortions, improving determination accuracy in noisy environments.
3Device complexity
If conventional sound determination methods are used, then processing is simpler, but it becomes impossible to determine sounds whose pitch cycle varies, such as engine sounds
Solution Approach 1:
The patent employs dynamic analysis by performing frequency analysis at multiple time points to capture the time-varying nature of sounds with varying pitch cycles. Instead of using a static analysis approach, the system continuously updates the spectral representation across time segments, enabling it to track and identify sounds whose frequency characteristics change over time, such as engine sounds with varying rotational speeds.
Solution Approach 2:
The system maintains continuous monitoring of the sound signal by performing frequency analysis at multiple successive time points. This continuous analysis approach ensures that sounds with varying pitch cycles are captured throughout their entire duration, allowing the system to identify and determine such sounds even as their spectral characteristics evolve over time.
Data Source
AI summary
A sound determination device (100) includes: an FFT unit (2402) which receives a mixed sound including a to-be-extracted sound and a noise, and obtains a frequency signal of the mixed sound for each of a plurality of times included in a predetermined duration; and a to-be-extracted sound determination unit (101 (j)) which determines, when the number of the frequency signals at the plurality of times included in the predetermined duration is equal to or larger than a first threshold value and a phase distance between the frequency signals out of the frequency signals at the plurality of times is equal to or smaller than a second threshold value, each of the frequency signals with the phase distance as a frequency signal of the to-be-extracted sound. The phase distance is a distance between phases of the frequency signals when a phase of a frequency signal at a time t is ψ(t) (radian) and the phase is represented by ψ′(t)=mod 2π(ψ(t)−2πft) (where f is an analysis-target frequency).