Wavelet Transform Based Denoising in Transient Evoked OAE Measurement
The major purpose of this paper is to investigate the theories of various time-frequency analysis (TFA) and its capabilities in representing TEOAE signals. Owing to the tiny, noisy and nonstationary characteristics of TEOAE signals, conventional time- and frequency- domain based analysis are not adequate to extract all the information embedded within the original signals. TFAs can effectively decompose the original signals into time-frequency distributions (TFDs) that can provide both time and frequency resolutions. More precise medical diagnosis can thus be achieved. Because TFAs can represent signal features more efficiently, higher performance is accomplished in several biomedical applications, such as signal compressions, and pattern recognitions, by TFA-based signal processing methodologies. The mathematical backgrounds of several commonly used linear and quadratic TFAs are described. We used a simulated TEOAE signal to testify that the TFAs can efficiently decompose the original signal, and the results of various TFAs are compared and discussed. The specific feature of how different frequency components vary with time, which is similar to the Cochlear organ, can be successfully extracted by the wavelet transform. Because the acquired TEOAE signals are severely contaminated by environmental white noise, we designed a TFA-based active denoising methodology, called wavelet shrinkage, to suppress the embedded white noise during the measurement. The proposed method is more efficient than traditional statistically averaging method and is implemented in the DSP-based system.