Music Emotions Recognition Based on Feature Analysis

Lv, Chaohui; Li, Shengnan; Huang, Linxiao

doi:10.1109/cisp-bmei.2018.8633223

Cited by 4 publications

(4 citation statements)

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“…This method still showed the ability to obtain timing information. In the same year, Lv et al (2018) classified the audio based on the traditional SVM method, which verified that the traditional method still remained effective in audio processing. Hu et al (2018) proposed a model method for the emotion classification of Chinese pop music.…”

Section: Related Workmentioning

confidence: 82%

Deep Learning-Based Artistic Inheritance and Cultural Emotion Color Dissemination of Qin Opera

Han

2022

Front. Psychol.

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How to enable the computer to accurately analyze the emotional information and story background of characters in Qin opera is a problem that needs to be studied. To promote the artistic inheritance and cultural emotion color dissemination of Qin opera, an emotion analysis model of Qin opera based on attention residual network (ResNet) is presented. The neural network is improved and optimized from the perspective of the model, learning rate, network layers, and the network itself, and then multi-head attention is added to the ResNet to increase the recognition ability of the model. The convolutional neural network (CNN) is optimized from the internal depth, and the fitting ability and stability of the model are enhanced through the ResNet model. Combined with the attention mechanism, the expression of each weight information is strengthened. The multi-head attention mechanism is introduced in the model and a multi-head attention ResNet, namely, MHAtt_ResNet, is proposed. The network structure can effectively identify the features of the spectrogram, improve the weight information of spectrogram features, and deepen the relationship between distant information in long-time series. Through experiments, the proposed model has high emotional classification accuracy for Qin opera, and with the increase of the number of data sets, the model will train a better classification effect.

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Section: Related Workmentioning

confidence: 82%

Deep Learning-Based Artistic Inheritance and Cultural Emotion Color Dissemination of Qin Opera

Han

2022

Front. Psychol.

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“…Sereval works also used other parameters instead of biosignals for emotion recognition. Among them, we can highlight the groups that assess emotion through audio analysis (Greer et al, 2020;Lv et al, 2018;Mo & Niu, 2019;Lopes et al, 2019;Kumar et al, 2016;Panda et al, 2020;Chapaneri & Jayaswal, 2018). Textual aspects also have great potential in the field of emotion recognition in music.…”

Section: Emotion Recognition Associated With Acoustic Stimulimentioning

confidence: 99%

“…Malheiro et al (2018), in turn, involved both types of problem. Another aspect observed was that the SVM was the algorithm that was most present in the returned studies (Wardana et al, 2018;Hsu et al, 2020;Lv et al, 2018;Dutta et al, 2020;Mo & Niu, 2019;Bakhtiyari et al, 2019;Lopes et al, 2019;Bo et al, 2017;Nemati & Naghsh-Nilchi, 2017;Rahman et al, 2020;Nawa et al, 2018;Dantcheva et al, 2017), followed by Artificial Neural Networks (Zhang et al, 2019;Lv et al, 2018;Dutta et al, 2020;Goyal et al, 2016;Marimpis et al, 2020;Bakhtiyari et al, 2019;Rahman et al, 2019;Rahman et al, 2020;Chapaneri & Jayaswal, 2018).…”

Section: Emotion Recognition Associated With Acoustic Stimulimentioning

confidence: 99%

Affective computing in the context of music therapy: a systematic review

Santana

Lima

Torcate

et al. 2021

RSD

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Music therapy is an effective tool to slow down the progress of dementia since interaction with music may evoke emotions that stimulates brain areas responsible for memory. This therapy is most successful when therapists provide adequate and personalized stimuli for each patient. This personalization is often hard. Thus, Artificial Intelligence (AI) methods may help in this task. This paper brings a systematic review of the literature in the field of affective computing in the context of music therapy. We particularly aim to assess AI methods to perform automatic emotion recognition applied to Human-Machine Musical Interfaces (HMMI). To perform the review, we conducted an automatic search in five of the main scientific databases on the fields of intelligent computing, engineering, and medicine. We search all papers released from 2016 and 2020, whose metadata, title or abstract contains the terms defined in the search string. The systematic review protocol resulted in the inclusion of 144 works from the 290 publications returned from the search. Through this review of the state-of-the-art, it was possible to list the current challenges in the automatic recognition of emotions. It was also possible to realize the potential of automatic emotion recognition to build non-invasive assistive solutions based on human-machine musical interfaces, as well as the artificial intelligence techniques in use in emotion recognition from multimodality data. Thus, machine learning for recognition of emotions from different data sources can be an important approach to optimize the clinical goals to be achieved through music therapy.

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“…(Nasrullah & Zhao, 2018) took temporal structure as a feature for artist classification using a convolutional recurrent neural network (CRNN) and experimented on the artist20 music dataset. (Lv et al, 2018) used a support vector machine and convolutional neural network for music emotion classification such as sad, exciting, serene, and happy using music feature set as input. (Ghosal & Kolekar, 2018) applied an ensemble technique of convolutional neural network and long short-term memory (CNN LSTM) and transfer learning model for music genre recognition.…”

Section: Background Workmentioning

confidence: 99%

Detecting Generic Music Features with Single Layer Feedforward Network using Unsupervised Hebbian Computation

Das

Kolya²

2020

International Journal of Distributed Artificial Intelligence

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In this work, the authors extract information on distinct baseline features from a popular open-source music corpus and explore new recognition techniques by applying unsupervised Hebbian learning techniques on our single-layer neural network using the same dataset. They show the detailed empirical findings to simulate how such an algorithm can help a single layer feedforward network in training for music feature learning as patterns. The unsupervised training algorithm enhances the proposed neural network to achieve an accuracy of 90.36% for successful music feature detection. For comparative analysis against similar tasks, they put their results with the likes of several previous benchmark works. They further discuss the limitations and thorough error analysis of the work. They hope to discover and gather new information about this particular classification technique and performance, also further understand future potential directions that could improve the art of computational music feature recognition.

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Music Emotions Recognition Based on Feature Analysis

Cited by 4 publications

References 0 publications

Deep Learning-Based Artistic Inheritance and Cultural Emotion Color Dissemination of Qin Opera

Deep Learning-Based Artistic Inheritance and Cultural Emotion Color Dissemination of Qin Opera

Affective computing in the context of music therapy: a systematic review

Detecting Generic Music Features with Single Layer Feedforward Network using Unsupervised Hebbian Computation

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