應用隱藏式及揭露式馬可夫模型於音訊內容識別

Applying Hidden Markov Model and Observable Markov Model for Audio Content Identification

黃俊凱(Gen-Kai Huang)

指導教授 ： 簡福榮

國立臺北科技大學/電資學院/電機工程系研究所/碩士(2007年)

摘要

本論文探討隱藏式馬可夫模型(Hidden Markov Model)和揭露式馬可夫模型（Observable Markov Model）用於音訊訊號的識別，實驗中每個馬可夫模型的狀態是由一組高斯混合機率密度函數做為觀察音訊之分類並且使用了梅爾頻率倒頻譜係數（Mel-Frequency Cepstral Coefficients）特徵值來描述音訊。整個音訊內容識別架構分別由資料庫訓練階段，和識別階段兩個階段組成。實驗中音訊資料庫包含共分成12類，其中包含九種樂器獨奏、交響樂及男女聲演唱。本論文實驗的分類模型使用了高斯混合模型(Gaussian Mixture Model)、隱藏式馬可夫模型以及揭露式馬可夫模型以作為性能評比。實驗結果顯示，相較於隱藏式馬可夫模型與梅爾頻率倒頻譜係數的組合，揭露式馬可夫模型與梅爾頻率倒頻譜係數的組合可以執行的更快，而且即使在不同的失真情況，例如：切割、MP3壓縮、AAC壓縮、振幅失真、時間長度的改變下仍有優越且趨於隱藏式馬可夫模型的正確率。

關鍵字

音訊內容識別 ； 高斯混合模型 ； 隱藏式馬可夫模型 ； 向量量化

並列摘要

In this thesis, both Hidden Markov Model and Observable Markov Model (OMM) are developed as the audio fingerprints for each audio signal. Each state of both Markov Models is classified by a set of gaussian mixture probabilities and the features Mel-Frequency Cepstral Coefficients (MFCC) are taken into consideration in the experiments. The framework consists of two phases, one is the database training phase and the other is the identification phase. The audio database used in the experiments is divided into 12 categories, including 9 kinds of musical instruments , symphony and males and females singing. Three classifiers that consist of Gaussian Mixture Model, Hidden Markov Model, and Observable Markov Model are investigated. The experimental results show that the OMM(MFCC) scheme can execute faster than the HMM(MFCC) and performs graceful degradation even when suffering various distortion, such as clipping , MP3 compression, AAC compression, amplitude modification, and time-scale modification, etc.

並列關鍵字

Audio Content Identification ； Gaussian Mixture Model ； Hidden Markov model ； Vector Quantization

參考文獻

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