Application of Music Industry Based on the Deep Neural Network

Fan, Minglei

doi:10.1155/2022/4068207

Cited by 7 publications

(4 citation statements)

References 30 publications

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“…There are just one or two hidden layers in shallow neural networks [42]. The neural network shown in the picture below has three layers: an input layer, a hidden layer, and an output layer.…”

Section: ) Shallow Neural Network Modelmentioning

confidence: 99%

BMNet-5: A Novel Approach of Neural Network to Classify the Genre of Bengali Music Based on Audio Features

et al. 2022

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Music genre classification (MGC) is the process of putting genre labels on music by analyzing the sounds or words. With the rapid growth of music data repositories, MGC can be used in a lot of ways to organize and manage music recommendation systems, advertising, and streaming services. But there have been a lot of works on classifying English music using different statistical and machine learning methods, but there hasn't been much progress in classifying Bengali music. Also, Deep Learning (DL) methods have been used in a few important ways to classify different types of music. The content and uniqueness of Bengali music make it much more interesting. Also, there is still a lot to learn about how to use the DL approach in Bengali music. So, Bengali music genre classification is a pretty new area of research in the field of Deep Learning. In this paper, we developed a unique technique called BMNet-5 to perform a multiclass classification of Bangla music genres such as "Bangla Adhunik," "Bangla Hip-Hop," "Bangla Band Music," "Nazrulgeeti," "Palligeeti," and "Rabindra Sangeet." We show the effectiveness of the suggested technique by extracting features from a dataset of 1742 Bangla music pieces and evaluating the automated classification judgments. The proposed BMNet-5 is based on a neural network designed to predict music genre from audio inputs. Our suggested model outperformed the corresponding previous research with an accuracy of 90.32%. The BMNet-5 model is then tested for performance consistency using K-fold cross validation with varying k values. Finally, we use the suggested model to train the interpretable SHAP model for all the genre of the Bangla music dataset, and the development of an explainable outcome may have a significant advantage.

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Section: ) Shallow Neural Network Modelmentioning

confidence: 99%

BMNet-5: A Novel Approach of Neural Network to Classify the Genre of Bengali Music Based on Audio Features

et al. 2022

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“…The classification of music features can be classified by artificial feelings, mainly through listening perception, and the characteristics of music are tone, timbre, and loudness. The other is divided into different features by Li et al [20] and others according to music features [21,22].…”

Section: Music Feature Selectionmentioning

confidence: 99%

The Application of Minority Music Style Recognition Based on Deep Convolution Loop Neural Network

Fan¹

2022

Wireless Communications and Mobile Computing

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In recent years, with the development of Internet and digital audio technology, music information retrieval has gradually become a research hotspot. Due to the rise of deep learning and machine learning in recent years, as well as the rapid improvement of computer software and hardware performance, it has laid a good foundation for identifying different genres of music. Among them, the application of minority music style recognition is also an important research direction. At present, the application performance of minority music style recognition based on deep convolution loop neural network is poor. Because convolution loop neural networks (CNNs) have strong ability to capture information features, this paper uses CNN to extract various features from music signals and classify them. Firstly, the original music signal spectrum is separated into time characteristic harmonic component and frequency characteristic impact component by using the harmonic/percussive sound separation (HPSS) algorithm. Combined with the original spectra as the input of CNN, the network structure of CNN is designed, and the influence of different parameters in the network structure on the recognition rate is studied. Experiments on minority music data sets, compared with other scholars’ music recognition methods, it shows that this method can effectively improve the recognition of minority music styles using a single feature.

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“…In contrast, compared with machine learning–based methods, deep learning–based methods for music sentiment classification are more suitable for data-rich music classification, as they eliminate the costs in audio feature extraction. The most prevalent method in this field today is convolutional neural networks (CNNS; Vargas et al , 2021; Sheykhivand et al , 2020; Tanberk et al , 2021; Liu et al , 2021; Song et al , 2020; Fan, 2022), which are suitable for spatial data processing. Moreover, CNNs consist of a convolutional layer for extracting local features and a pooling layer for dimensionality reduction.…”

Section: Introductionmentioning

confidence: 99%

Music sentiment classification based on an optimized CNN-RF-QPSO model

Tian

Yin

Gan

2023

DTA

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PurposeMusic sentiment analysis helps to promote the diversification of music information retrieval methods. Traditional music emotion classification tasks suffer from high manual workload and low classification accuracy caused by difficulty in feature extraction and inaccurate manual determination of hyperparameter. In this paper, the authors propose an optimized convolution neural network-random forest (CNN-RF) model for music sentiment classification which is capable of optimizing the manually selected hyperparameters to improve the accuracy of music sentiment classification and reduce labor costs and human classification errors.Design/methodology/approachA CNN-RF music sentiment classification model is designed based on quantum particle swarm optimization (QPSO). First, the audio data are transformed into a Mel spectrogram, and feature extraction is conducted by a CNN. Second, the music features extracted are processed by RF algorithm to complete a preliminary emotion classification. Finally, to select the suitable hyperparameters for a CNN, the QPSO algorithm is adopted to extract the best hyperparameters and obtain the final classification results.FindingsThe model has gone through experimental validations and achieved a classification accuracy of 97 per cent for different sentiment categories with shortened training time. The proposed method with QPSO achieved 1.2 and 1.6 per cent higher accuracy than that with particle swarm optimization and genetic algorithm, respectively. The proposed model had great potential for music sentiment classification.Originality/valueThe dual contribution of this work comprises the proposed model which integrated two deep learning models and the introduction of a QPSO into model optimization. With these two innovations, the efficiency and accuracy of music emotion recognition and classification have been significantly improved.

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Application of Music Industry Based on the Deep Neural Network

Cited by 7 publications

References 30 publications

BMNet-5: A Novel Approach of Neural Network to Classify the Genre of Bengali Music Based on Audio Features

BMNet-5: A Novel Approach of Neural Network to Classify the Genre of Bengali Music Based on Audio Features

The Application of Minority Music Style Recognition Based on Deep Convolution Loop Neural Network

Music sentiment classification based on an optimized CNN-RF-QPSO model

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