Music emotion recognition using deep Gaussian process

Chen, Sih-Huei; Lee, Yuan-Shan; Hsieh, Wen-Chi; Wang, Jia-Ching

doi:10.1109/apsipa.2015.7415321

Cited by 26 publications

(10 citation statements)

References 12 publications

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“…In this regard, there is a mental and physical problem, whether the mind, which can be viewed with hardware, can be separated from each other or not. There is a dualism in which people view each other as separate beings—such as the body and the soul and the monism is called monism [ 27 , 28 ].…”

Section: Background Knowledgementioning

confidence: 99%

Consistency of Medical Data Using Intelligent Neuron Faster R-CNN Algorithm for Smart Health Care Application

Kim

2020

Healthcare

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The purpose of this study is to increase interest in health as human life is extended in modern society. Hence, many people in hospitals produce much medical data (EMR, PACS, OCS, EHR, MRI, X-ray) after treatment. Medical data are stored as structured and unstructured data. However, many medical data are causing errors, omissions and mistakes in the process of reading. This behavior is very important in dealing with human life and sometimes leads to medical accidents due to physician errors. Therefore, this research is conducted through the CNN intelligent agent cloud architecture to verify errors in reading existing medical image data. To reduce the error rule when reading medical image data, a faster R-CNN intelligent agent cloud architecture is proposed. It shows the result of increasing errors of existing error reading by more than 1.4 times (140%). In particular, it is an algorithm that analyses data stored by actual existing medical data through Conv feature map using deep ConvNet and ROI Projection. The data were verified using about 120,000 databases. It uses data to examine human lungs. In addition, the experimental environment established an environment that can handle GPU’s high performance and NVIDIA SLI multi-OS and multiple Quadro GPUs were used. In this experiment, the verification data composition was verified and randomly extracted from about 120,000 medical records and the similarity compared to the original data were measured by comparing about 40% of the extracted images. Finally, we want to reduce and verify the error rate of medical data reading.

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Section: Background Knowledgementioning

confidence: 99%

Consistency of Medical Data Using Intelligent Neuron Faster R-CNN Algorithm for Smart Health Care Application

Kim

2020

Healthcare

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“…They adopted the feed-forward neural network with 10 hidden layers to build the regression model and used the correlation-based method to find out suitable features. Chen et al [29] proposed a system for detecting emotion in music that is based on a deep Gaussian process. The proposed system consisted of two major parts: feature extraction and classification.…”

Section: Music Emotion Recognitionmentioning

confidence: 99%

Automatic Emotion-Based Music Classification for Supporting Intelligent IoT Applications

Seo

Huh

2019

Electronics

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With the arrival of the fourth industrial revolution, new technologies that integrate emotional intelligence into existing IoT applications are being studied. Of these technologies, emotional analysis research for providing various music services has received increasing attention in recent years. In this paper, we propose an emotion-based automatic music classification method to classify music with high accuracy according to the emotional range of people. In particular, when the new (unlearned) songs are added to a music-related IoT application, it is necessary to build mechanisms to classify them automatically based on the emotion of humans. This point is one of the practical issues for developing the applications. A survey for collecting emotional data is conducted based on the emotional model. In addition, music features are derived by discussing with the working group in a small and medium-sized enterprise. Emotion classification is carried out using multiple regression analysis and support vector machine. The experimental results show that the proposed method identifies most of induced emotions felt by music listeners and accordingly classifies music successfully. In addition, comparative analysis is performed with different classification algorithms, such as random forest, deep neural network and K-nearest neighbor, as well as support vector machine.

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“…While lyric structures and features have been rarely studied, melodic information has been processed in previous psychology studies (Gabrielsson, 2016;Swaminathan & Schellenberg, 2015;. On the other hand, with the development of natural language processing (NLP) technology, different lyric features have been widely extracted and analyzed in music emotion recognition (MER) studies (e.g., Delbouys et al, 2018), a field investigating computational models for detecting music emotion (Aljanaki, Yang, & Soleymani, 2017;Chen, Lee et al, 2015). These MER studies have typically focused on improving the prediction effect of constructed models but have not interpreted the model and variables.…”

Section: Introductionmentioning

confidence: 99%

Peer Review #1 of "Using machine learning analysis to interpret the relationship between music emotion and lyric features (v0.2)"

2021

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Music emotion recognition using deep Gaussian process

Cited by 26 publications

References 12 publications

Consistency of Medical Data Using Intelligent Neuron Faster R-CNN Algorithm for Smart Health Care Application

Consistency of Medical Data Using Intelligent Neuron Faster R-CNN Algorithm for Smart Health Care Application

Automatic Emotion-Based Music Classification for Supporting Intelligent IoT Applications

Peer Review #1 of "Using machine learning analysis to interpret the relationship between music emotion and lyric features (v0.2)"

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