Recognition of Vocal and Non-Vocal Segments in Musical Sound Tracks
碩士 === 國立臺北科技大學 === 資訊工程系研究所 === 100 === In this thesis, we use MFCC, LPC, LPCC feature extractions and HMM(Hidden Markov Model) tool to do training and create a model. Then use the model to recognize the testing songs. The songs in the database will be separated into two parts, training songs and t...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Others |
| Language: | zh-TW |
| Published: |
2012
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| Online Access: | http://ndltd.ncl.edu.tw/handle/2dkh4f |
| Summary: | 碩士 === 國立臺北科技大學 === 資訊工程系研究所 === 100 === In this thesis, we use MFCC, LPC, LPCC feature extractions and HMM(Hidden Markov Model) tool to do training and create a model. Then use the model to recognize the testing songs. The songs in the database will be separated into two parts, training songs and testing songs. We compare MFCC and LPCC Likelihood difference to increase the recognition rate.In addition, we tried to recognize the Vocal and Non-Vocal segments by computing correlation coefficient of left channel and right channel of the stereo songs.
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