The mood of Chinese Pop music: Representation and recognition

Hu, Xiao; Yang, Yi-Hsuan

doi:10.1002/asi.23813

Cited by 18 publications

(9 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Deep learning is a new research in machine learning research that can simplify the process of interpreting human brain data [ 13 , 14 ]. In-depth phonetic research has become a field of recognition research, and the research field is expanding [ 15 ]. Voice recognition generally has two working modes: recognition mode and command mode.…”

Section: Research Methods Of Speech Recognition Technology In Music S...mentioning

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

View full text Add to dashboard Cite

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.

show abstract

Section: Research Methods Of Speech Recognition Technology In Music S...mentioning

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

View full text Add to dashboard Cite

show abstract

“…According to Ref. [10][11][12][13][14], these studies mainly solve the problem that music (emotion) recognition models in different cultural backgrounds are not general. However, there has not been any cross-cultural research on the connection between music and vision.…”

Section: Cross-cultural Study Related To Musicmentioning

confidence: 99%

A dataset for learning stylistic and cultural correlations between music and videos

Chen

Zhang

et al. 2022

Cognitive Comp and Systems

View full text Add to dashboard Cite

Music-visual retrieval is of broad interest in the field of Music Information Retrieval (MIR). Most research relies on emotional tags or is based on content but does not consider stylistic and cultural differences between music and videos. As a result, only onesided dimensions are considered for automatic music retrieval for videos, while the stylistic correlation between audio-visual is ignored. At the same time, the needs of different cultural regions cannot be well met. Therefore, the first labelled extensive Music Video (MV) dataset, Next-MV, is constructed in this paper consisting of 6000 pieces of 30-s MV fragments, including five music style labels and four cultural labels. The proposed Next-Net framework is built to study the correlation between music style and visual style. The optimal audiovisual feature set and model structure are obtained in the experiments. The accuracy reached 71.1%, higher than the baseline model (66.9%). Furthermore, in the cross-cultural experiment, it is found that the accuracy of the general fusion model (71.1%) is between the model trained by within-dataset (76%) and the model trained by cross-dataset (60%), indicating that culture has a significant influence on the correlation between music and visual. The experiments of pair classification on cultures are further carried out. It is found that Rock and Dance are more culturally influenced than R&B and Hip-hop. Among all the cultures discussed, Chinese and Japanese music and videos show great differences among most of the styles, while Korean music videos styles are more similar to western styles than other eastern cultures. K E Y W O R D S cross culture, cross-modal processing, deep learning architectures, multimodal fusion, new datasetsThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

show abstract

“…First, the dataset was investigated via SVM, and an accuracy of 85% was achieved for six-emotion classification. Then, the dataset was analyzed by means of a support vector re-gressor (SVR), and the accuracy was 25% for valence and 79% for arousal [28].…”

Section: Classification Approachmentioning

confidence: 99%

“…Instead, the datasets provide emotional annotations, and lists of songs and where to find them [29][30][31]. Some datasets include extracted features [32], and some datasets consider the cultural background of the annotators [28] [33]. Datasets that do not provide audio files can lead to problems because we cannot make any potentially required changes to the process.…”

Section: Datasetmentioning

confidence: 99%

“…Previous works that utilized a similar model structure demonstrated that the accuracy was better when discrimination started with arousal than when discrimination started with valence [2] [7][8] [28]. Additionally, many regression approach studies have shown that arousal prediction is always more accurate than valence prediction [15] [24][25][26][27][28]. Therefore, we initiated unit 1 to discriminate between high and lowarousal songs by training the model with the entire dataset.…”

Section: 2 Cascaded Modelmentioning

confidence: 99%

See 1 more Smart Citation

Emotion Classification System for Digital Music with a Cascaded Technique

2020

View full text Add to dashboard Cite

Music selection is difficult without efficient organization based on metadata or tags, and one effective tag scheme is based on the emotion expressed by the music. However, manual annotation is labor intensive and unstable because the perception of music emotion varies from person to person. This paper presents an emotion classification system for digital music with a resolution of eight emotional classes. Russell’s emotion model was adopted as common ground for emotional annotation. The music information retrieval (MIR) toolbox was employed to extract acoustic features from audio files. The classification system utilized a supervised machine learning technique to recognize acoustic features and create predictive models. Four predictive models were proposed and compared. The models were composed by crossmatching two types of neural networks, i.e., Levenberg-Marquardt (LM) and resilient backpropagation (Rprop), with two types of structures: a traditional multiclass model and the cascaded structure of a binary-class model. The performance of each model was evaluated via the MediaEval Database for Emotional Analysis (DEAM) benchmark. The best result was achieved by the model trained with the cascaded Rprop neural network (accuracy of 89.5%). In addition, correlation coefficient analysis showed that timbre features were the most impactful for prediction. Our work offers an opportunity for a competitive advantage in music classification because only a few music providers currently tag music with emotional terms.

show abstract

The mood of Chinese Pop music: Representation and recognition

Cited by 18 publications

References 39 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

A dataset for learning stylistic and cultural correlations between music and videos

Emotion Classification System for Digital Music with a Cascaded Technique

Contact Info

Product

Resources

About