Multipitch Tracking by Factorial Hidden Markov Model Using Spectro-Temporal Modulations of Fourier Spectrogram

• Main Authors: Hsieh, Kun-Yeh, 謝坤燁
• Other Authors: Chi, Tai-Shih
• Format: Others
• Language: zh-TW
• Published: 2014
• Online Access: http://ndltd.ncl.edu.tw/handle/8d76n5

碩士 === 國立交通大學 === 電信工程研究所 === 102 === In recent years, pitch plays an important role in audio signal processing. Pitch tracking used in a wide range of applications. Single pitch tracking can make the error between the estimated pitch and true pitch within 5% in 90% frames, but there is a lot of room for improvement in multiple pitch tracking. In this thesis, we will apply Robust Algorithm Pitch Tracking (RAPT) to track the single speaker signal and to build up the prior probability and transition probability matrix of each speaker, and then we convert the spectrogram into rate-scale domain by the means which is inspired by cortical stage of auditory perceptual model. We use the value of rate-scale domain as feature vector and model the feature vector using Gaussian mixture models. Then we employ the mixture maximization model to establish the probability model for the feature vector of mixture speech. Finally, a FHMM is applied for tracking pitch over time. In the result of experiment, we found the system using rate-scale as feature vector has much capability of resisting noise than spectrum.