iMusic: a session-sensitive clustered classical music recommender system using contextual representation learning

Roy, Samarjit; Biswas, Mousumi; De, Debashis

doi:10.1007/s11042-020-09126-8

Cited by 25 publications

(8 citation statements)

References 40 publications

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“…Firstly, in the dimension of learning attitude, there is no significant difference between the students who use the proposed method and those who use the literature method proposed by Roy et al [ 33 ]. Learners believe that the relevant learning contents of music knowledge are important and valuable.…”

Section: System Performance Comparison Testmentioning

confidence: 97%

Application of Lightweight Deep Learning Model in Vocal Music Education in Higher Institutions

Zhang

Liu

2022

Computational Intelligence and Neuroscience

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The aim is to improve the teaching quality of music majors and cultivate their innovative ability. This article takes Vocal Music Education (VME) method as the research object to explore the teaching reform of Music Major courses. Firstly, this article makes an in-depth study on Big Data Analytics (BDA) and Digital Twins (DTs) technology and constructs a DTs platform connecting real teaching space and virtual teaching space. Secondly, the DTs platform is divided into online learning feature analysis and virtual-real teaching space integration functional modules. This article explores the online immersive education process design and technology application of the DTs platform from the two aspects of teaching and technology. Afterward, it designs a student action and expression recognition network based on the Visual Geometry Group (VGG) Net model and Google Net model in teaching data collection and management. Finally, the proposed system is tested. The test results show that the active and passive interaction curves of the traditional VME system have no obvious fluctuation, indicating that the interaction of the traditional VME system is not strong, and the ability of active feedback information is poor. By contrast, the active and passive interaction curves in the proposed VME have large fluctuations, showing that the proposed VME has more frequent interaction, and the teaching information can get real-time and active feedback. Therefore, the proposed VME system can better stimulate students’ learning desire. Meanwhile, the constructed Neural Network (NN) has the highest recognition accuracy of 99.07% on the student action and expression dataset. When tested with the image data taken by the research experiment, the highest accuracy is 89%, with an average of more than 85%. The proposed VME system provides ideas for applying DTs technology in the college of music education.

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Section: System Performance Comparison Testmentioning

confidence: 97%

Application of Lightweight Deep Learning Model in Vocal Music Education in Higher Institutions

Zhang

Liu

2022

Computational Intelligence and Neuroscience

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“…ere are two ways to get it. One is the system through selective or according to the public user characteristics and product characteristics information presented by the user to take the initiative to choose, to inform the system [19][20][21]. e other way is that Complexity the system collects the user's behavioural information through buried points for different functions to dig.…”

Section: Analysis Of Music Feature Recognitionmentioning

confidence: 99%

[Retracted] Optimization of Music Feature Recognition System for Internet of Things Environment Based on Dynamic Time Regularization Algorithm

Kai¹

2021

Complexity

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Because of the difficulty of music feature recognition due to the complex and varied music theory knowledge influenced by music specialization, we designed a music feature recognition system based on Internet of Things (IoT) technology. The physical sensing layer of the system places sound sensors at different locations to collect the original music signals and uses a digital signal processor to carry out music signal analysis and processing. The network transmission layer transmits the completed music signals to the music signal database in the application layer of the system. The music feature analysis module of the application layer uses a dynamic time regularization algorithm to obtain the maximum similarity between the test template and the reference. The music feature analysis module of the application layer uses the dynamic time regularization algorithm to obtain the maximum similarity between the test template and the reference template to realize the feature recognition of the music signal and determine the music pattern and music emotion corresponding to the music feature content according to the recognition result. The experimental results show that the system operates stably, can capture high-quality music signals, and can correctly identify music style features and emotion features. The results of this study can meet the needs of composers’ assisted creation and music researchers’ analysis of a large amount of music data, and the results can be further transferred to deep music learning research, human-computer interaction music creation, application-based music creation, and other fields for expansion.

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“…The goal here is that companies can improve their customer-relations management by enhancing customer satisfaction and loyalty. It is also sometimes interesting to cluster items in order to have a better understanding of them, as in [34], where a recommender system for Indian classical music is presented. This system makes recommendations to users based on their listening history and also analyzes and groups melodies based on note structures available in Indian classical music.…”

Section: Related Workmentioning

confidence: 99%

User encoding for clustering in very sparse recommender systems tasks

Pérez-Núñez

Díez

Luaces

et al. 2021

Multimed Tools Appl

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Recommender Systems are a very useful tool which let companies and service providers focus in the preferences of their customers, helping them to avoid an overwhelming variety of choices. In this context, clustering tools can play an important role to detect groups of customers with similar tastes. Thus, companies can make personalized marketing campaigns, offering to their users new products which have been consumed by other users with comparable preferences. In this paper we present a general framework to cluster users with respect to their tastes when the registers stored about the interactions between users and products are extremely scarce. Commonly, clustering methods employ the values of features describing the samples to be clustered (users in our case), but such features are not always available. We propose some alternative representations for users, in which their tastes are gathered to some extent, so that clustering algorithms can take advantage and make more homogeneous groups in this regard. To illustrate the performance of the whole framework, we tested it on six popular datasets commonly used as a benchmark for recommender systems, as well as on an extremely sparse real-world dataset that records the preferences of readers to click promoted links in digital publications. In the experimental section we compare our proposed representations to other common user encodings. We show that clustering users attending only to their feature values or to the items they have evaluated gives rise to the worst scores in terms of taste homogeneity.

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iMusic: a session-sensitive clustered classical music recommender system using contextual representation learning

Cited by 25 publications

References 40 publications

Application of Lightweight Deep Learning Model in Vocal Music Education in Higher Institutions

Application of Lightweight Deep Learning Model in Vocal Music Education in Higher Institutions

[Retracted] Optimization of Music Feature Recognition System for Internet of Things Environment Based on Dynamic Time Regularization Algorithm

User encoding for clustering in very sparse recommender systems tasks

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