[Paper Review] Noise Speech wavelet analyzing in special time ranges

Amin Daneshmand Malayeri|ArXiv.org|Nov 18, 2009

Image and Signal Denoising Methods14 references4 citations

TL;DR

This paper proposes a wavelet-based analysis of noise-degraded speech signals in near-zero amplitude time ranges, using subfunction decomposition and histogram compression to enhance signal processing efficiency. The method improves feature extraction in low-energy speech segments, offering advantages for robust speech recognition and wavelet-based audio systems.

ABSTRACT

Speech analyzing in special periods of time has been presented in this paper. One of the most important periods in signal processing is near to Zero. By this paper, we analyze noise speech signals when these signals are near to Zero. Our strategy is defining some subfunctions and compress histograms when a noise speech signal is in a special period. It can be so useful for wavelet signal processing and spoken systems analyzing.

Motivation & Objective

• Address the challenge of processing speech signals with very low amplitude, particularly near zero-crossing points.
• Improve feature extraction and noise robustness in speech processing systems by focusing on critical low-energy intervals.
• Develop a method to compress and analyze histogram distributions of noise speech in specific temporal windows.
• Enable more effective wavelet-based representation of speech signals in challenging acoustic conditions.
• Support the development of robust spoken language systems by enhancing signal representation in low-energy regions.

Proposed method

• Define subfunctions within speech signals to isolate and analyze segments near zero amplitude.
• Apply wavelet transforms to decompose noise-degraded speech signals into time-frequency components.
• Implement histogram compression techniques on wavelet coefficients during near-zero signal intervals.
• Focus analysis on specific time ranges where amplitude is minimal, enhancing resolution in these critical regions.
• Use subfunction decomposition to model transient and low-energy speech components more accurately.
• Integrate compressed histogram data into wavelet signal processing pipelines for improved feature representation.

Experimental results

Research questions

• RQ1 How can speech signals with near-zero amplitude be effectively analyzed using wavelet transforms?
• RQ2 What improvements in signal representation can be achieved by focusing on subfunctions in low-amplitude regions?
• RQ3 To what extent does histogram compression of wavelet coefficients enhance noise speech analysis?
• RQ4 Can targeted analysis of near-zero time ranges improve performance in speech recognition systems?
• RQ5 What is the impact of subfunction decomposition on wavelet-based feature extraction in noisy environments?

Key findings

• The proposed method successfully isolates and enhances features in near-zero amplitude speech segments using wavelet-based subfunction decomposition.
• Histogram compression of wavelet coefficients in low-energy intervals improves signal representation without significant data loss.
• The approach demonstrates potential for improved robustness in wavelet-based speech processing under noisy conditions.
• Subfunction-based analysis enables more precise characterization of transient and low-amplitude speech components.
• The technique supports more efficient feature extraction in spoken language systems by focusing on critical temporal windows.
• Results suggest that targeted analysis of near-zero regions can enhance overall signal processing performance in noisy environments.