Music emotion recognition using PSO‐based fuzzy hyper‐rectangular composite neural networks

Chin, Yu Hao; Hsieh, Yi-Zeng; Su, Mu Chun; Lee, Shu Fang; Chen, Miao Wen; Wang, Jia Ching

doi:10.1049/iet-spr.2016.0021

Cited by 25 publications

(9 citation statements)

References 29 publications

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“…Chin et al proposed a new music emotion content recognition system, which integrates three computational intelligence tools, namely, the hyper-rectangular composite neural network (HRCNN), fuzzy system, and PSO. They extracted original features from each piece of music and cleared rules are transformed into fuzzy rules with confidence factors [ 9 ]. The spectrum markers is intended to achieve robustness, so Gutiérrez and Garcia define and established an appropriate fitness evaluation method by converting its highly correlated parameters into various genes [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

Improvement of Speech Recognition Technology in Piano Music Scene Based on Deep Learning of Internet of Things

Bai

2022

Computational Intelligence and Neuroscience

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The main goal of speech recognition technology is to use computers to convert human analog speech signals into computer-generated signals, such as behavior patterns or binary codes. Different from speaker identification and speaker confirmation, the latter attempts to identify or confirm the speaker who uttered the speech rather than the lexical content contained in it. The short-term idea is that it should be able to record the musical sound played by the user with a certain musical instrument, then extract the note and duration information from it, and finally generate the corresponding MID file according to the MIDI standard, which can set the type of musical instrument in advance to complete the function of musical sound transformation, such as playing with a harmonica, and playing the MID at the end is the piano sound. With the rapid development of the mobile Internet, fields such as machine learning, electronic communication, and navigation have placed high demands on real-time and standard text recognition technology. This paper merges the sound of visual music into text-based data set training, uses the exported scanner features for model training, uses the model to extract features, then uses the features for prior training, and then uses pretraining. DNN results show that the combined training of target prevention and expansion plans, by replacing long-term and short-term memory networks, end-to-end speech recognition programs, and behavioral tests organized by mobile devices, can provide a larger receptive field combined with expanded convolution instead of long and short periods. The experimental results show that when the input sampling point is 2400, it can be seen that the convergence speed of the model becomes slower with more than 90 iterations and the loss of the model on the verification set increases with the increase in the number of iterations. This shows that the model in this paper can fully meet the needs of speech recognition in piano music scenes.

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Section: Introductionmentioning

confidence: 99%

Improvement of Speech Recognition Technology in Piano Music Scene Based on Deep Learning of Internet of Things

Bai

2022

Computational Intelligence and Neuroscience

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“…is designed. Owing to the fuzzy nature of an emotional state, attempts to model decision making cannot be done with absolute certainty [55]. Hence, a CLIPS‐based expert system that relies on the idea that all decisions admit of degrees is designed.…”

Section: Discussionmentioning

confidence: 99%

Phase‐aware subspace decomposition for single channel speech separation

Wiem

Anouar

Bouzid

2020

IET signal process.

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Single channel speech separation (SCSS) is often required as post-processing in several applications that facilitate automatic human-to-human or human-to-machine communication in challenging acoustic environments such as voice command for smart homes or robotics. The proposed SCSS system, that the authors call phase-aware subspace decomposition (PASD), relies on subspace decomposition for speech separation followed by a phase-aware mask for final subspace recovery. In fact, the proposed approach decomposes the mixture into a sparse and low-rank subspace in the frequency domain by rank minimising that relies on iterative decomposition using adaptive thresholding in each iteration to achieve soft estimation and considers phase-information for reconstruction. Separation results are reported in terms of both intrusive and non-intrusive metrics using realistic recordings corrupted with real-life noises. As speech separation systems are expected to have maximal interference rejection without speech distortion, we also evaluate the proposed system by recognising speech from a target speaker in the presence of either concurrent speech or noise. Recognition results show that separated signals are of high intelligibility so that they can be exploited by other automatic applications. The extensive evaluation under different test scenarios proves that PASD consistently improves the quality of the separated signals, compared to other benchmark approaches.

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“…e system integrates three computational intelligence tools, namely, hyper-rectangular composite neural network, fuzzy system, and PSO [7]. His research has provided new ideas for music emotion recognition, but because the data used in the research are too small, it seems that it is easy to be replaced by new algorithms in the long run.…”

Section: Related Workmentioning

confidence: 99%

Music Emotion Recognition Model Using Gated Recurrent Unit Networks and Multi-Feature Extraction

Niu

2022

Mobile Information Systems

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A large number of music platforms have appeared on the Internet recently. The deep learning framework for music recommendation is still very limited when it comes to accurately identifying the emotional type of music and recommending it to users. Languages, musical styles, thematic scenes, and the ages to which they belong are all common classifications. And this is far from sufficient, posing difficulties in music classification and identification. As a result, this paper uses the methods of music emotion multi-feature extraction, BiGRU model design, and music theme scene classification model design to improve the accuracy of music emotion recognition. It develops the BiGRU emotion recognition model and compares it to other models. BiGRU can correctly identify happy and sad emotion music up to 79 percent and 81.01 percent of the time, respectively. It goes far beyond Rnet-LSTM, and the greater the difference in emotion categories, the easier it is to analyze the feature sequence containing emotional features and the higher the recognition accuracy. This is especially evident in the accuracy with which happiness and sadness are recognized. It can meet users' needs for music recognition in a variety of settings.

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Music emotion recognition using PSO‐based fuzzy hyper‐rectangular composite neural networks

Cited by 25 publications

References 29 publications

Improvement of Speech Recognition Technology in Piano Music Scene Based on Deep Learning of Internet of Things

Improvement of Speech Recognition Technology in Piano Music Scene Based on Deep Learning of Internet of Things

Phase‐aware subspace decomposition for single channel speech separation

Music Emotion Recognition Model Using Gated Recurrent Unit Networks and Multi-Feature Extraction

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