Intelligent Classification of Sound Signals with Applications in Engine Noise Diagnostics and Audio Preprocseeing

• Main Authors: Meng-Chun Chen, 陳孟君
• Other Authors: Ming-Sian Bai
• Format: Others
• Language: en_US
• Published: 2006
• Online Access: http://ndltd.ncl.edu.tw/handle/83556076427664478023

碩士 === 國立交通大學 === 機械工程系所 === 94 === A processor that integrates various intelligent classification and preprocessing algorithms is presented in this thesis. Classification algorithms including the Nearest Neighbor Rule (NNR), the Artificial Neural Networks (ANN), the Fuzzy Neural Networks (FNN), and the Hidden Markov Models (HMM) are employed to classify and identify engine noise, singers and instruments. Audio features in the time and frequency domains are extracted and preprocessed prior to classification. A training phase is required to establish a feature space template. This is followed by a test phase, where the audio features of the test data are calculated and matched with the feature space template. In addition to audio classification, the proposed system provides several Independent Component Analysis (ICA)-based preprocessing functions for blind source separation, voice removal, and noise reduction. The proposed techniques were applied to process various kinds of audio program materials. The results reveal that the performance of methods are satisfactory, but varies with algorithm and program material used in the tests.