Multimedia Recognition of Piano Music Based on the Hidden Markov Model

Piano performance is an art with rich artistic elements and unpredictable performance skills. It is an important carrier for playing beautiful piano sounds. The generation of musical tension and expression of piano performance is a vivid display of piano performance skills. In piano performance, we should pay attention to the cultivation and flexible application of performance skills. In order to ensure the richness and artistry of piano performance, it is fully based on the artistic characteristics of piano performance. Through in-depth analysis of the principle of the hidden Markov model, it is applied to the multimedia recognition process of piano playing music. In the process of obtaining the template, the fundamental frequency of the piano playing music differs greatly, and the piano playing music appears during the performance process. For the problem of low recognition rate, this paper proposes a multimedia recognition method for piano music. Finally, the analysis of experimental results shows that the method proposed in this paper has a 16% higher recognition rate than the traditional method, and it has a certain value in the multimedia recognition of piano music.

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Recognition of Offline Handwritten Chinese Characters of Amount in Words Based on Integrated Features and HMM

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.263-266.2639 ◽

2012 ◽

Vol 263-266 ◽

pp. 2639-2642

Author(s):

Cai Feng Liu ◽

Xue Dong Tian ◽

Fang Yang

Keyword(s):

Markov Model ◽

Hidden Markov Model ◽

Hidden Markov ◽

Recognition Rate ◽

Experimental Results ◽

Chinese Characters ◽

Recognition Method ◽

Voting Rule

A recognition method of offline handwritten Chinese characters of amount in words is presented. The method uses elastic mesh strategy to divide character images written by special men into meshes, and extracts directional element and key point features in every mesh to produce a vector. Based on independent Hidden Markov Model classifiers, this paper uses voting rule to integrate the Hidden Markov Model classifiers. The experimental results show that this method has a relative high recognition rate.

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Recognition of Badminton Shot Action Based on the Improved Hidden Markov Model

Journal of Healthcare Engineering ◽

10.1155/2021/7892902 ◽

2021 ◽

Vol 2021 ◽

pp. 1-8

Author(s):

Chao Ma ◽

Dayang Yu ◽

Hao Feng

Keyword(s):

Markov Model ◽

Hidden Markov Model ◽

Hidden Markov ◽

Rapid Development ◽

Recognition Rate ◽

Sliding Window ◽

Acceleration Sensor ◽

Sports Activity ◽

Average Recognition Rate ◽

Day By Day

In recent years, with the rapid development of sports, the number of people playing various sports is increasing day by day. Among them, badminton has become one of the most popular sports because of the advantages of fewer restrictions on the field and ease of learning. This paper develops a wearable sports activity classification system for accurately recognizing badminton actions. A single acceleration sensor fixed on the end of the badminton racket handle is used to collect the data of the badminton action. The sliding window segmentation technique is used to extract the hitting signal. An improved hidden Markov model (HMM) is developed to identify standard 10 badminton strokes. These include services, forehand chop, backhand chop the goal, the forehand and backhand, forehand drive, backhand push the ball, forehand to pick, pick the ball backhand, and forehand. The experimental results show that the model designed can recognize ten standard strokes in real time. Compared with the traditional HMM, the average recognition rate of the improved HMM is improved by 7.3%. The comprehensive recognition rate of the final strokes can reach up to 95%. Therefore, this model can be used to improve the competitive level of badminton players.

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Multiple Hidden Markov Model Post Processed with Support Vector Machine to Recognize English Handwritten Numerals

International Journal of Artificial Intelligence Tools ◽

10.1142/s0218213018500197 ◽

2018 ◽

Vol 27 (05) ◽

pp. 1850019 ◽

Cited By ~ 2

Author(s):

Binod Kumar Prasad ◽

Goutam Sanyal

Keyword(s):

Support Vector Machine ◽

Markov Model ◽

Hidden Markov Model ◽

Hidden Markov ◽

Digital Communications ◽

Recognition Rate ◽

Reference Level ◽

Support Vector ◽

Modulation Scheme ◽

Central Pixel

This paper presents rotation and size invariant English numerals recognition system with, competitive recognition rate. The novelty of this paper is the introduction of two unique methods of feature extraction namely Pixel Moment of Inertia (PMI) and Delta Distance Coding (DDC). The proposed Multiple Hidden Markov Model (MHMM) is a two tier model to neutralize the effect of two very frequent writing styles of numerals ‘4’ and ‘7’ on their recognition rates. The novelty of PMI is that it finds moment of all the pixels of a specified zone about the central pixel and not about geometrical centroid of image area. In this paper, PMI has been observed to have an upper hand over centroidal MI. DDC is a new technique of curvature coding, based on distance from a reference level and is similar to the logic behind Delta modulation scheme in Digital Communications. Thus, the current paper correlates two digital domains namely, Digital Image Processing and Digital Communications. Support Vector Machine differentiates two close output classes obtained from classification with MHMM. The overall recognition accuracy rate of 99.17% has been achieved based on MNIST database.

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