HUM-TO-CHORD CONVERSION USING CHROMA FEATURES AND HIDDEN MARKOV MODEL

Music is basically a sound arranged in such a way to produce a harmonious and rhythmic sound. The basis of music is a tone, which is a natural sound and has different frequencies for each sound. Each constant sound represents a tone. The tones can also be represented in a chord. Humans are capable of creating a sound or imitating a tone from other human beings, but they are naturally unable to represent them into musical notation without musical instruments. This research addresses a model of Hum-to-Chord (H2C) conversion using a Chroma Feature (CF) to extract the characteristics and a Hidden Markov Model (HMM) to classify them. A 10-fold cross-validating shows that the best model is represented by the chroma coefficients of 55 and HMM with a codebook of 16, which gives an average accuracy of 94.83%. Examining on a 30% testing set proves that the best model has a high accuracy of up to 97.78%. Most errors come from the chords with both high and low octaves since they are unstable. Compared to a similar model called musical note classification (MNC), the proposed H2C model performs better in terms of both accuracy and complexity.

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High-accuracy hierarchical parallel technique for hidden Markov model-based 3D magnetic resonance image brain segmentation

Concurrency and Computation Practice and Experience ◽

10.1002/cpe.2959 ◽

2012 ◽

Vol 26 (1) ◽

pp. 194-216 ◽

Cited By ~ 4

Author(s):

Ali A. El-Moursy ◽

Hanan ElAzhary ◽

Akmal Younis

Keyword(s):

Magnetic Resonance ◽

Markov Model ◽

Hidden Markov Model ◽

Magnetic Resonance Image ◽

Hidden Markov ◽

High Accuracy ◽

Brain Segmentation ◽

Model Based ◽

Parallel Technique

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Musical instruments recognition using hidden Markov model

Conference Record of the Thirty-Sixth Asilomar Conference on Signals Systems and Computers 2002 ACSSC-02 ◽

10.1109/acssc.2002.1197175 ◽

2003 ◽

Cited By ~ 2

Author(s):

Jonghyun Lee ◽

Joohwan Chun

Keyword(s):

Markov Model ◽

Hidden Markov Model ◽

Hidden Markov ◽

Musical Instruments

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The Classification of Heartbeats from Two-Channel ECG Signals Using Layered Hidden Markov Model

Frontiers in Biomedical Technologies ◽

10.18502/fbt.v9i1.8146 ◽

2021 ◽

Author(s):

Azadeh Sadoughi ◽

Mohammad Bagher Shamsollahi ◽

Emad Fatemizadeh

Keyword(s):

Time Series ◽

Markov Model ◽

Hidden Markov Model ◽

Hidden Markov ◽

Heart Diseases ◽

Classification Performance ◽

Statistical Tool ◽

Average Accuracy ◽

Proposed Model

Purpose: Cardiac arrhythmia is one of the most common heart diseases that can have serious consequences. Thus, heartbeat arrhythmias classification is very important to help diagnose and treat. To develop the automatic classification of heartbeats, recent advances in signal processing can be employed. The Hidden Markov Model (HMM) is a powerful statistical tool with the ability to learn different dynamics of the real time-series such as cardiac signals. Materials and Methods: In this study, a hierarchy of HMMs named Layered HMM (LHMM) was presented to classify heartbeats from the two-channel electrocardiograms. For training in the first layer, the morphology of the heartbeats was used as observations, while observations in the second layer were the inference results of the first layer. The performance of the proposed LHMM was evaluated in classifying three types of heartbeat arrhythmias (Atrial premature beats (A), Escape beats (E), Left bundle branch block beats (L)) using fifteen records of the MIT-BIH arrhythmia database. Furthermore, the obtained results of the proposed model were compared with other HMM generalizations. Results: The best average accuracy was achieved 97.10±1.63%. The best sensitivity of 96.8±1.24%, 98.85±0.52%, and 95.64±1.41 were obtained for A, E, and L, respectively. Furthermore, the results of the proposed method were better than other HMM generalizations. Conclusion: Extracting information from time-series dynamics by HMM-based methods has good classification results. The proposed model shows that applying a two-layered HMM can lead to better extraction of information from the observations; therefore, the classification performance of cardiac arrhythmias has been improved using LHMM.

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