Fog Computing Approach for Music Cognition System Based on Machine Learning Algorithm

• Published in: IEEE transactions on computational social systems Vol. 5; no. 4; pp. 1142 - 1151
• Main Authors: Lu, Lifei, Xu, Lida, Xu, Boyi, Li, Guoqiang, Cai, Hongming
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.12.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial intelligence; Audio signal process; Cloud computing; Cognition; Cognition & reasoning; Computer simulation; Data models; Data processing; Edge computing; fog computing; Hidden Markov models; Internet of Things; Internet of Things (IoT); Machine learning; Markov chains; Music; music cognition; Nodes; Probabilistic models
• ISSN: 2329-924X; 2373-7476
• DOI: 10.1109/TCSS.2018.2871694

With the wide spreading of mobile and Internet of Things (IoT) devices, music cognition as a meaningful task for music promotion has attracted a lot of attention around the world. How to automatically generate music score is an important part in music cognition, which acts as an important carrier so as to disposing huge quantity of music data in IoT networks or Internet. For the reason that the computers lack of the domain knowledge and cognitive ability, it is hard for computers to recognize the melody of music or write score while listening to the music. Therefore, a music cognition system is introduced to cognate music and automatically write score based on machine learning methods. First, considering large-scale data processing is needed by machine learning algorithms and a number of music devices are involved in the cognition system through Internet, fog computing is adopted in the proposed architecture to efficiently allocate computing resources. Then, the system can collect, preprocess, and store raw music data on the fringe nodes. Meanwhile, these data will be transmitted from fog nodes to cloud servers to form music databases. Then, machine learning algorithms, such as hidden Markov model and Gaussian mixture model, are performed in cloud servers to recognize music melody. Finally, a case study of music score generation demonstrates the proposed system. It is shown that the method provides an effective support to generate music score, and also proposed a promising way for the research and application of music cognition.