Environmental Audio Source Separation Based on Improved K-means Clustering with Adaptive Genetic Algorithm
نویسندگان
چکیده
In daily life environmental sounds that are present around us include intricately mixed sounds emitted from different sources. This work aim is to contribute a method for sound source separation by means of the enhanced k-means clustering with adaptive genetic algorithm. At first removes the features from the input audio signal by means of Mel Frequency Cepstral Coefficients (MFCC) and spectral features. Based on the features we gather the input audio signal to background sound and event sound. The suggested method employs the enhanced k-means with adaptive genetic algorithm for collecting the audio signal. Now the traditional k-means clustering algorithm is enhanced using the centroids selection by the adaptive genetic algorithm. At last we categorize the event sound by means of the Neuro fuzzy classifier. The Neuro fuzzy classifier is employed to categorize the event sound based on the features. So, that the suggested method attains the better classification with high accuracy.
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