Multi-Level Local Feature Coding Fusion for Music Genre Recognition

Ng, Wing W. Y.; Zeng, Wei-jie; Wang, Ting

doi:10.1109/access.2020.3017661

Cited by 30 publications

(15 citation statements)

References 39 publications

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“…The GTZAN Genre mainly divides music genres into 10 categories: blues, country, hip-hop, jazz, pop, disco, classical, rock, reggae, and metal. The ISMIR2004 Genre mainly divides music into six genres: classical, electronic, jazz/blues, metal/punk, and rock/pop ( Ng et al, 2020 ).…”

Section: Methodsmentioning

confidence: 99%

The Use of Deep Learning-Based Intelligent Music Signal Identification and Generation Technology in National Music Teaching

Tang

Zhang²,

Zhang³

2022

Front. Psychol.

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The research expects to explore the application of intelligent music recognition technology in music teaching. Based on the Long Short-Term Memory network knowledge, an algorithm model which can distinguish various music signals and generate various genres of music is designed and implemented. First, by analyzing the application of machine learning and deep learning in the field of music, the algorithm model is designed to realize the function of intelligent music generation, which provides a theoretical basis for relevant research. Then, by selecting massive music data, the music style discrimination and generation model is tested. The experimental results show that when the number of hidden layers of the designed model is 4 and the number of neurons in each layer is 1,024, 512, 256, and 128, the training result difference of the model is the smallest. The classification accuracy of jazz, classical, rock, country, and disco music types can be more than 60% using the designed algorithm model. Among them, the classification effect of jazz schools is the best, which is 77.5%. Moreover, compared with the traditional algorithm, the frequency distribution of the music score generated by the designed algorithm is almost consistent with the spectrum of the original music. Therefore, the methods and models proposed can distinguish music signals and generate different music, and the discrimination accuracy of different music signals is higher, which is superior to the traditional restricted Boltzmann machine method.

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Section: Methodsmentioning

confidence: 99%

The Use of Deep Learning-Based Intelligent Music Signal Identification and Generation Technology in National Music Teaching

Tang

Zhang²,

Zhang³

2022

Front. Psychol.

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“…The recognition accuracy is improved by using the information obtained from the offline data set, including RGB information and available depth and bone information. Ng et al proposed an optimization method based on machine learning to match dance technical movements and music in music arrangement [ 19 ]. First, combined with machine learning theory, this method constructs the mapping relationship between dance action and music based on historical sample data set and obtains the evaluation function of the harmonious relationship between dance action and music.…”

Section: Related Workmentioning

confidence: 99%

“…The experimental data are from the dance action capture database provided by Carnegie Mellon University Laboratory, and the length of each music segment is about 5 seconds. Using the methods of this study and references [ 17 ] and [ 19 ], the optimization experiment of dance action matching in music choreography is carried out, and the matching degrees (%) of the three methods are compared. Figure 7 shows the comparison results of dance movements in music arrangement.…”

Section: Simulation Analysis Of Matching Optimization Based On Multif...mentioning

confidence: 99%

Optimization Simulation of Dance Technical Movements and Music Matching Based on Multifeature Fusion

Dong

2022

Computational Intelligence and Neuroscience

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Music and dance videos have been popular among researchers in recent years. Music is one of the most important forms of human communication; it carries a wealth of emotional information, and it is studied using computer tools. In the feature engineering process, most present machine learning approaches suffer from information loss or insufficient extracted features despite the relevance of computer interface and multimedia technologies in sound and music matching tasks. Multifeature fusion is widely utilized in education, aerospace, intelligent transportation, biomedicine, and other fields, and it plays a critical part in how humans get information. In this research, we offer an effective simulation method for matching dance technique movements with music based on multifeature fusion. The initial step is to use music beat extraction theory to segment the synchronized dance movements and music data, then locate mutation points in the music, and dynamically update the pheromones based on the merits of the dance motions. The audio feature sequence is obtained by extracting audio features from the dancing video’s accompanying music. Then, we combine the two sequences to create an entropy value sequence based on audio variations. By comparing the consistency of several approaches for optimizing dance movement simulation trials, the optimized simulation method described in this research has an average consistency of 87%, indicating a high consistency. As a result, even though the background and the subject are readily confused, the algorithm in this research can keep a consistent recognition rate for more complicated dance background music, and the approach in this study can still guarantee a certain accuracy rate.

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“…This makes the problem richer and more suitable for deep models, which require larger amounts of data. Different types of deep learning models have been applied to audio classification problems, such as Deep Belief Networks (DBNs), Restricted Boltzmann Machines (RBMs), Convolutional neural networks (CNNs) [24] and Recurrent neural networks (RNNs) [25]- [27].…”

Section: Fully Connected Neural Network (Fcnns)mentioning

confidence: 99%

Web-Based Music Genre Classification for Timeline Song Visualization and Analysis

Castillo¹,

Flores²

2021

IEEE Access

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This paper presents a web application that retrieves songs from YouTube and classifies them into music genres. The tool explained in this study is based on models trained using the musical collection data from Audioset. For this purpose, we have used classifiers from distinct Machine Learning paradigms: Probabilistic Graphical Models (Naive Bayes), Feed-forward and Recurrent Neural Networks and Support Vector Machines (SVMs). All these models were trained in a multi-label classification scenario. Because genres may vary along a song's timeline, we perform classification in chunks of ten seconds. This capability is enabled by Audioset, which offers 10-second samples. The visualization output presents this temporal information in real time, synced with the music video being played, presenting classification results in stacked area charts, where scores for the top-10 labels obtained per chunk are shown. We briefly explain the theoretical and scientific basis of the problem and the proposed classifiers. Subsequently, we show how the application works in practice, using three distinct songs as cases of study, which are then analyzed and compared with online categorizations to discuss models performance and music genre classification challenges.

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Multi-Level Local Feature Coding Fusion for Music Genre Recognition

Cited by 30 publications

References 39 publications

The Use of Deep Learning-Based Intelligent Music Signal Identification and Generation Technology in National Music Teaching

The Use of Deep Learning-Based Intelligent Music Signal Identification and Generation Technology in National Music Teaching

Optimization Simulation of Dance Technical Movements and Music Matching Based on Multifeature Fusion

Web-Based Music Genre Classification for Timeline Song Visualization and Analysis

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