A Novel Technique for Fetal Heart Rate Estimation Based on Ensemble Learning

The autocorrelation algorithm is the most commonly used method for extracting fetal heart rate from ultrasound Doppler fetal monitors. The traditional autocorrelation algorithm can not always extract the detection cycle accurately. During the calculation process, the heartbeat cycle may not be recognized, or the cycle may be doubled or halved recognized. Combining the characteristics of envelope curve with average magnitude difference function curve, this paper designs a set of extreme point search scheme and a fetal heart cycle recognition model based on ensemble learning to assist in screening the best fetal heart cycle. The aim of this study is to improve the precision of the fetal heart rate calculation. The experimental results show that the proposed method can effectively screen out the best fetal heart cycle with enhanced reliability and robustness.

Pitch Estimation using Time Domain Algorithm

Speech is classified into voice, unvoiced and silence. The voice speech is the periodic vibration of vocal folds. Background noise affects the speech signals. In many speech applications calculation of pitch plays a major role. The paper proposes a pitch detection algorithm based on the short-time average magnitude difference function (AMDF) and the short-term autocorrelation function (ACF). Detecting the Pitch within the speech signal is important in most of all the speech related applications. Detection of Pitch is useful in identification of speaker. One solution to get detect with the pitch is by using the time domain algorithms. This paper gives idea about estimation and detection of pitch in time domain algorithm for different voice samples

A Signal Period Detection Algorithm Based on Morphological Self-Complementary Top-Hat Transform and AMDF

Period detection technology for weak characteristic signals is very important in the fields of speech signal processing, mechanical engineering, etc. Average magnitude difference function (AMDF) is a widely used method to extract the period of periodic signal for its low computational complexity and high accuracy. However, this method has low detection accuracy when the background noise is strong. In order to improve this method, this paper proposes a new method of period detection of the signal with single period based on the morphological self-complementary Top-Hat (STH) transform and AMDF. Firstly, the signal is de-noised by the morphological self-complementary Top-Hat transform. Secondly, the average magnitude difference function of the noise reduction sequence is calculated, and the falling trend is suppressed. Finally, a calculating adaptive threshold is used to extract the peaks at the position equal to the period of periodic signal. The experimental results show that the accuracy of periodic extraction of AMDF after Top-Hat filtering is better than that of AMDF directly. In summary, the proposed method is reliable and stable for detecting the periodic signal with weak characteristics.

Driver Voice Identification System Using Auto-Correlation Function and Average Magnitude Difference Function

This study presents a driver identification system using voice analysis for a vehicle security system. The structure of the proposed system has three parts. The first procedure is speech pre-processing, the second is feature extraction of sound signals, and the third is classification of driver voice. Initially, a database of sound signals for several drivers was established. The volume and zero-crossing rate (ZCR) of sound are used to detect the voice end-point in order to reduce data computation. Then the Auto-correlation Function (ACF) and Average Magnitude Difference Function (AMDF) methods are applied to retrieve the voice pitch features. Finally these features are used to identify the drivers by a General Regression Neural Network (GRNN). The experimental results show that the development of this voice identification system can use fewer feature vectors of pitch to obtain a good recognition rate.

A Speech Endpoint Detection Based on Empirical Mode Decomposition and Average Magnitude Difference Function

Speech endpoint detection plays an important role in speech signal processing. In this paper, a method of speech endpoint detection based on empirical mode decomposition is introduced for accurately detecting the speech endpoint. This method used in speech signal decomposition gets a set of intrinsic mode functions (IMF). An IMF which contained a lot of noise must be filtered, and the rest of IMFs can be reconstructed to a new speech signal. The speech endpoint is detected by average magnitude difference function precisely. Simulation experiments show that the method proposed in this paper can eliminate the impact of noise effectively and detect the speech signal endpoint accurately.