Spectrogram Enhanced Pitch Period Tracking using MWSG Filter in Noisy Environments

Speech Processing is the study of speech signals which carry individual information such as speaker characteristics, acoustic environment, etc due to which the parameters defining the signal are unique. Pitch Period, Duration, Intensity are the parameters that play the main role in coding speech applications such as authentication, surveillance, speaker recognition. As the conventional filters are static in nature, for non-linear and non-stationary variations of signal parameters adaptive filtering models which are robust are required. Hence the tracking and estimation of the parameters can be done by using Particle-Kalman Filter. It is very important that the signal has to track perfectly even in the presence of noise, by removing the noise and thereby enhancing the output. The approach in this paper is to propose a method for enhancing the performance, using multiple window Savitzky-Golay Filter (MWSG Filter). The performance of filter is measured by parameters Viz., SNR and PSNR

Transform based Speech Enhancement for Auditory Signals based on Iterative Weiner Filtering

One of the important features of Speech processing is speech enhancement. In a noisy environment, speech enhancement plays a vital role. Many research works are being done in speech enhancement methods in recent years but still, it can't be attained. It mainly depends on Speech intelligibility which can improve the speech quality. In this research work, signal representation is considered and the various transforms are applied and compared. The analysis is done with the help of two parameters and the results are compared. Here the enhancement process is focused on using Advanced DCT (ADCT) and Discrete fractional Cosine transform. The ADCT has the advantage of energy compaction and flexible window switching. Iterative Wiener Filtering is used for filtering the coefficients. Pitch Synchronous Analysis (PSA) is combined for finding the exact pitch period.