In this paper, we use LMS filter and design a model with the help of Simulink using MATLAB 11a software. The effect of interference of acoustic noise in speech signals is the most common problem in speech processing. Acoustic noise reduces the intelligibility of speech signals by masking them. We need to suppress this acoustic noise for better communication. By using this technique, the acousti...

The paper addresses a problem of isolated vowels recognition in patients following total laryngectomy. The visual and acoustic speech modalities were separately incorporated in the machine learning algorithms. The authors used the Mel Frequency Cepstral Coefficients as acoustic descriptors of a speech signal. A lip contour was extracted from a video signal of the speaking faces using OpenCV sof...

The auditory brain stem response to speech mimics the acoustic characteristics of the speech signal with remarkable fidelity. This makes it possible to derive from it considerable theoretical and clinically applicable information relevant to auditory processing of complex stimuli. Years of research have led to the current characterization of these neural events with respect to the underlying ac...

Speech perception is the process by which listeners presented with a distribution of audible frequencies modulated in amplitude (loudness) and spectral (the frequency set) content across time turn this sound into a coherent unit of perception that is interpreted as language. Classic studies established that speech is not perceived by simplymapping sets of invariant acoustic properties onto diff...

A constant problem is to localize a number of acoustic sources, to separate their individual signals and to estimate their strengths in a propagation medium. An acoustic receiving array with signal processing algorithms is then used. The most widely used algorithm is the conventional beamforming algorithm but it has a very low resolution and high sidelobes that may cause a signal leakage proble...

the record of human brain neural activities, namely electroencephalogram (eeg), is generally known as a non-stationary and nonlinear signal. in many applications, it is useful to divide the eegs into segments within which the signals can be considered stationary. combination of empirical mode decomposition (emd) and hilbert transform, called hilbert-huang transform (hht), is a new and powerful ...

Acoustic echo cancellation is important for audio teleconferencing when simultaneous communication of speech is necessary. In acoustic echo cancellation, a measured microphone signal contains two signals such as the near-end speech signal and the far-end echoed speech signal. The goal is to remove the far-end echoed speech signal from the microphone signal so that only the near-end speech signa...

Surface acoustic wave (SAW) devices have wide range of applications in microwave signal processing. Microwave SAW components benefit from higher quality factors and much smaller crosstalk when compared to their electromagnetic counterparts. Efficient routing modulation SAWs are essential for building large-scale versatile acoustic-wave circuits. Here, we demonstrate integrated thermo-acoustic m...

This paper demonstrates a KDD method applied to audio data analysis, particularly, it presents possibilities which result from replacing traditional methods of analysis and acoustic signal processing by KDD algorithms when restoring audio recordings at&cted by strong noise.

The mechanical processes of the cardiac cycle generate vibratory and acoustic signals that are received on the chest wall. We describe signal processing and feature extraction methods utilizing these signals for continuous non-invasive monitoring of cardiac systolic function. Vibro-acoustic heart signals were acquired from eleven subjects during a routine pharmacological stress echocardiography...