Automatic Audio Segmentation aims at extracting information about type of audio i.e. silence, clean speech or speech with noise, music etc. The aim of this thesis is to find and extract different features of audio, segment the audio and combine them to form single type of audio. In this work, the audio signal is initially decomposed into non-overlapping frames. Then these frames are decomposed ...

Emotion feature extraction is the key to speech emotional recognition. And ensemble empirical mode decomposition(EEMD) is a newly developed method aimed at eliminating emotion mode mixing present in the original empirical mode decomposition(EMD). To evaluate the performance of this new method, this paper investigates the effect of a parameters pertinent to EEMD: speech emotional envelope. First...

A new modeling and classification method of ultrasonic signals based on empirical mode decomposition(EMD) and neural network is put forward in the paper. Firstly, the original ultrasonic flaw signals are decomposed into a finite number of stationary intrinsic mode functions (IMFs) by EMD, and the Fourier transformation of IMF is made. The next step is to find a set of classification values from...

EEMD Algorithm is usually applied in noise reduction of rolling bearing signal because of its powerful ability in de-noising. But misjudgment in selecting sensitive IMF exists, it results in the incomplete processing of noise reduction. In order to solve this problem, this paper proposes an improved EEMD algorithm. This algorithm adopts Cloud Similarity Measurement in selecting the sensitive in...