Music Genre Classification Research Articles

Establish a novel framework for enhancing minority music genre identification

The objective of this study is to develop a new framework based on Waterwheel Plant optimization for improving minority music genre classification using Layer-tuned Long Short-Term Memory (WP-LT-LSTM). Chinese minority music includes various musical styles of different ethnic groups in China. It depends on the specific instrumentalities, the distribution of pitch classes and rhythms, and the culture. Specifically, the proposed framework will enhance the ability to efficiently detect under represented music genres, which could have applicability for cultural sustainability and more personalized music recommendation services. For this, we collected a dataset that includes a wide range of different minority music samples in audio format. These include genre labels, artist information and audio features necessary for the training of our suggested model. Using K-fold cross validation to enhances the accuracy. Min-max Normalization is used on the obtained data to perform pre-processing. To extract the important features from the processed data, we used Mel-frequency cepstral coefficients (MFCCs). In our proposed model, the WP algorithm dynamically adjusts LT-LSTM’s internal parameters, enhancing model adaptability. LT-LSTM processes sequential audio data, capturing temporal dependencies crucial for genre classification in minority music genres. The implemented model is executed in Python software. It evaluates the model’s performance across a range of parameters throughout the result analysis phase. We also performed comparison studies using standard methods. The results collected indicate the excellence and effectiveness of the proposed framework for music genre identification.

Efficient Music Genre Recognition Using ECAS-CNN: A Novel Channel-Aware Neural Network Architecture.

In the era of digital music proliferation, music genre classification has become a crucial task in music information retrieval. This paper proposes a novel channel-aware convolutional neural network (ECAS-CNN) designed to enhance the efficiency and accuracy of music genre recognition. By integrating an adaptive channel attention mechanism (ECA module) within the convolutional layers, the network significantly improves the extraction of key musical features. Extensive experiments were conducted on the GTZAN dataset, comparing the proposed ECAS-CNN with traditional convolutional neural networks. The results demonstrate that ECAS-CNN outperforms conventional methods across various performance metrics, including accuracy, precision, recall, and F1-score, particularly in handling complex musical features. This study validates the potential of ECAS-CNN in the domain of music genre classification and offers new insights for future research and applications.

Research on music genre recognition method based on deep learning

In this paper, we explore music genre recognition using deep learning methods, examining the application of feature extraction, model construction, and performance evaluation for different music genres. During the data preparation and preprocessing stages, data augmentation and normalization techniques were employed to enhance the model’s generalization capabilities. By constructing multilayer convolutional neural networks (CNNs) and recurrent neural networks (RNNs), we achieved automatic recognition of music genres. In the experimental results analysis, we compared the accuracy and training time of different models, validating the effectiveness of deep learning in the field of music genre recognition. The limitations of deep learning methods and future research directions are also discussed, providing a reference for further studies in music information processing. This study delves into the issue of music genre recognition and proposes a deep learning-based approach. This method leverages neural networks to extract features and learn from audio data, enabling accurate classification of different music genres. Extensive experiments have demonstrated that our method achieves highly satisfactory results in music genre recognition tasks. Furthermore, we optimized the deep learning models, improving their generalization capabilities and accuracy. Our research offers the music industry an efficient and accurate method for music genre recognition, providing new perspectives and technical support for research and applications in the music field.

Audio feature enhancement based on quaternion filtering and deep hashing

This paper aims to solve the problems of difficult convergence of audio model training, large data demand, and large dimensionality of storage space for audio-generated feature vectors. To this end, this paper proposes the use of quaternion Gabor filtering to suppress the background information of the spectrogram and reduce the interference of the data for the case of insufficient data alignment between audio data and image data after shifting the domain. In addition, different scales of window lengths and frame shifts are used to capture the connections between different vocal objects. To address the problem that the generated feature vectors are large dimensional, we use a deep hash module to map high-dimensional features to low-dimensional features and use a probability function to make the learned samples more consistent with the overall distribution. In the experimental evaluation, the proposed method was evaluated on the environmental sound classification dataset and the music genre classification dataset. The proposed method uses only a common backbone network and improves the accuracy of audio recognition.

A Deep Learning Approach for Automatic Music Genre Classification

A Deep Learning Approach for Automatic Music Genre Classification

A Hybrid Parallel Computing Architecture Based on CNN and Transformer for Music Genre Classification

Music genre classification (MGC) is the basis for the efficient organization, retrieval, and recommendation of music resources, so it has important research value. Convolutional neural networks (CNNs) have been widely used in MGC and achieved excellent results. However, CNNs cannot model global features well due to the influence of the local receptive field; these global features are crucial for classifying music signals with temporal properties. Transformers can capture long-range dependencies within an image thanks to adopting the self-attention mechanism. Nevertheless, there are still performance and computational cost gaps between Transformers and existing CNNs. In this paper, we propose a hybrid architecture (CNN-TE) based on CNN and Transformer encoder for MGC. Specifically, we convert the audio signals into mel spectrograms and feed them into a hybrid model for training. Our model employs a CNN to initially capture low-level and localized features from the spectrogram. Subsequently, these features are processed by a Transformer encoder, which models them globally to extract high-level and abstract semantic information. This refined information is then classified using a multi-layer perceptron. Our experiments demonstrate that this approach surpasses many existing CNN architectures when tested on the GTZAN and FMA datasets. Notably, it achieves these results with fewer parameters and a faster inference speed.

Music genre classification: Machine Learning on GTZAN

Music genre classification: Machine Learning on GTZAN

An improved random forest model for music genre classification algorithm based on sparrow search algorithm

In this study, a random forest model optimised based on the sparrow search algorithm is explored for the music genre classification problem. By introducing the sparrow search algorithm, the model is able to search the feature space more comprehensively during the training process, which improves the learning ability of complex data relationships. After 15 iterations of optimisation, the fitness value of the model is improved from 0.2002 to 0.197, showing better convergence. The performance on the training set shows that the model predicts 2,543 music genres with an accuracy of 99.59% and only 10 incorrect predictions, which verifies its excellent performance on the training data. While on the test set, the model's prediction accuracy for 623 music genres is 79.78%, which is slightly lower than the result of the training set, but still reflects good generalisation ability. The results of this paper imply that the improved random forest model based on the sparrow search algorithm has potential application prospects in the music genre classification task. By optimising the parameter settings and feature selection, we not only improve the prediction accuracy and generalisation ability of the model, but also provide new ideas for solving other complex data classification problems. In the future, we can further expand the sample size, try different feature combinations, and conduct in-depth research by combining more domain knowledge in order to further improve the model performance and promote the development of related fields.

The implementation of a proposed deep-learning algorithm to classify music genres

Abstract To improve the classification effect of music genres in the digital music era, the article employs deep-learning algorithms to improve the performance of the classification of music genres. An auxiliary (estimated) model is constructed to estimate the amount of unmeasured data in the dual-rate system to enhance the recognition effect of music features. Moreover, a dual-rate output error model to identify such impacts is proposed to eliminate the impact of corrupt data caused by the estimation, which eventually leads to the further improvement of the proposed model called dual-rate multi-innovation forgetting gradient algorithm based on the auxiliary model. In addition, the article employs linear time-varying forgetting factors to improve the stability of the system, advances the recognition effect of music features through enhancement processing, and combines a deep-learning algorithm to construct a classification system of music genres. The result shows that the classification of the music genre system based on a deep-learning algorithm has a good music genre classification effect.

Rigdelet neural network and improved partial reinforcement effect optimizer for music genre classification from sound spectrum images

In this paper, a new approach has been introduced for classifying the music genres. The proposed approach involves transforming an audio signal into a unified representation known as a sound spectrum, from which texture features have been extracted using an enhanced Rigdelet Neural Network (RNN). Additionally, the RNN has been optimized using an improved version of the partial reinforcement effect optimizer (IPREO) that effectively avoids local optima and enhances the RNN's generalization capability. The GTZAN dataset has been utilized in experiments to assess the effectiveness of the proposed RNN/IPREO model for music genre classification. The results show an impressive accuracy of 92 % by incorporating a combination of spectral centroid, Mel-spectrogram, and Mel-frequency cepstral coefficients (MFCCs) as features. This performance significantly outperformed K-Means (58 %) and Support Vector Machines (up to 68 %). Furthermore, the RNN/IPREO model outshined various deep learning architectures such as Neural Networks (65 %), RNNs (84 %), CNNs (88 %), DNNs (86 %), VGG-16 (91 %), and ResNet-50 (90 %). It is worth noting that the RNN/IPREO model was able to achieve comparable results to well-known deep models like VGG-16, ResNet-50, and RNN-LSTM, sometimes even surpassing their scores. This highlights the strength of its hybrid CNN–Bi-directional RNN design in conjunction with the IPREO parameter optimization algorithm for extracting intricate and sequential auditory data.

Performance Analysis of Deep Learning and Machine Learning Methods for Music Genre Classification System

Classification plays a crucial role in numerous applications within the music industry, spanning from content management to personalized playlists and music recommendation systems. While previous research has explored various machine learning frameworks for this purpose, such as support vector machines (SVM), a comprehensive comparison analysis of convolutional neural networks (CNN) and k-nearest neighbor (KNN) remains unknown. This study aims to address this gap by analyzing and contrasting the performance of SVM, KNN, and CNN in music genre classification. Each algorithm was carefully trained using different music genres from a protected dataset, employing feature extraction methods to capture the appropriate qualities of audio signals. The models underwent extensive training with a limited number of samples, and their performance was evaluated using industry standards such as accuracy, precision, recall, and F1 scores. Experimental results included SVM, KNN, and CNN for music genre designation. This study contributes significantly to existing literature by providing a comparative analysis of these algorithms. The findings highlight the strengths and limitations of each approach, offering guidance for researchers and practitioners in choosing the most suitable approach for their specific needs. The insights gained from this research have the potential to enhance music genre classification systems, ultimately improving the user experience across various music-related contexts.

Analysis of Building the Music Feature Extraction Systems: A Review

Music genre classification is a basic method for sound processing in the field of music retrieval. The application of machine learning has become increasingly popular in automatically classifying music genres. Therefore, in recent years, many methods have been studied and developed to solve this problem. In this article, an overview on the process and some music feature extraction methods is presented. Here, the feature extraction method using Mel Frequency Cepstral Coefficients (MFCC) is discussed in detail. Some typical results in using Mel Frequency Cepstral Coefficients for improving accuracy in the classification process are introduced and discussed. Therefore, the feature extraction method using MFCC has shown its suitability due to high accuracy and has much potential for further research and development.

Application of Deep Learning Model for Music Genre Classification

A musical genre is a term used to classify specific musical styles in accordance with a sense of their significance or a set of guidelines. There are many techniques to classify music into different genres, but attempts to automate this process using machine learning have received much less attention from the media. Blues, Classical, Country, Disco, Hiphop, Jazz, Metal, Pop, Reggae and Rock are the most common musical genres. Objective: This study develops and creates a model that can classify music into their respective genre. The dataset, which contains 1000 songs divided into 10 sections with 100 songs each, was obtained from the open-source service "Kaggle" and utilized for the classification. To train and test the model, the dataset was then converted into a JSON format, the new dataset was then splitted into 30% for testing the model and 70% for the training of the model. This model was created using the Convolutional Neural Network (CNN) machine learning technique. The result shows that the model was able to correctly classify 7 audios out of 10 audios and correctly classify them into their original genre. The classifier achieved an accuracy score of about 73%. In conclusion, the study provided a model that will aid in the classification of various types of music into their appropriate genres. This will be highly useful for record companies and other music retailers to group songs for search and retrieval.

Music Genre Classification Based on VMD-IWOA-XGBOOST

Music genre classification is significant to users and digital platforms. To enhance the classification accuracy, this study proposes a hybrid model based on VMD-IWOA-XGBOOST for music genre classification. First, the audio signals are transformed into numerical or symbolic data, and the crucial features are selected using the maximal information coefficient (MIC) method. Second, an improved whale optimization algorithm (IWOA) is proposed for parameter optimization. Third, the inner patterns of these selected features are extracted by IWOA-optimized variational mode decomposition (VMD). Lastly, all features are put into the IWOA-optimized extreme gradient boosting (XGBOOST) classifier. To verify the effectiveness of the proposed model, two open music datasets are used, i.e., GTZAN and Bangla. The experimental results illustrate that the proposed hybrid model achieves better performance than the other models in terms of five evaluation criteria.

Automated Determination of Music Genre and Linguistic Categorization

The main objective of the project is to build a system that allows users to predict genre as well as language out of some music . The Automated determination of musical genre and linguistic categorization is a machine learning based prediction model which is used to predict the genres and languages of an audio . Musical genres are categorical labels created by humans to characterize piece of music. A musical genre is characterized by common characteristics typically related to the instrumentation, rhythmic structure and harmonic content of the music. The performance and relative importance of the proposed features are investigated by training the statistical pattern recognition classifiers using real world audio collections and using machine learning algorithm.In this project, we built systems called music genre and language classification and the effects of different feature extraction methods and their various combinations were also investigated. This task is of great interest in music information retrieval and recommendation systems. In recent years, several machine learning algorithms have been developed to tackle this task, using features such spectral, temporal, and tonal characteristics of audio signals. Music genre prediction and language classification is a task that involves automatically predicting a piece of music into a specific genre category as well as language based on its audio features. Keywords-machine-learning,music,music genre and language classification

Music Genre Classification

The Music Genre Classification model automatically divides music into different genres using a small number of audio files and a range of musical attributes. This topic is highly relevant to the field of music information retrieval since it provides a way to organize and analyze large amounts of music files. For MGC, standard machine learning techniques such as SVM, KNN, Decision trees, and neural networks can be applied. These algorithms are trained to recognize different musical qualities and traits, which allows them to categorize the audio files into different genres. Numerous applications show that deep learning algorithms—such as CNN, ANN, and others—perform better than conventional machine learning algorithms. Consequently, the CNN method is adjusted to perform the categorization of music files. This classifies musical genres using deep learning methods from CNN. To evaluate the effectiveness of the MGC algorithms, accuracy is used. Moreover, the impact of different algorithms on MGC performance can be compared and studied. It can be applied to automated music recommendation systems, music production, and music education.

Enhancing Music Genre Classification through Multi-Algorithm Analysis and User-Friendly Visualization

The aim of this study is to teach an algorithm how to recognize different types of music. Users will submit songs for analysis. Since the algorithm hasn't heard these songs before, it needs to figure out what makes each song unique. It does this by breaking down the songs into different parts and studying things like rhythm, melody, and tone via supervised learning because the program learns from examples that are already labelled. One important thing to consider when classifying music is its genre, which can be quite complex. To ensure accuracy, we use five different algorithms, each working independently, to analyze the songs. This helps us get a more complete understanding of each song's characteristics. Therefore, our goal is to correctly identify the genre of each submitted song. Once the analysis is done, the results are presented using a graphing tool, making it easy for users to understand and provide feedback.

Course genres classification of music e-learning platform based on deep learning big data intelligent processing algorithm

At present, the growth trend of digital music information is showing an exponential form. Various songs can be classified according to their unique genres and styles, forming various “genres”. So, how to effectively manage the massive amount of music data has become a problem that needs to be solved to promote the balanced development of different types and genres of music. On this basis, this article adopts deep learning technology to study the classification methods of music genres oriented towards big data. Firstly, this article studies LSTM based on long and short term memory networks and compares its advantages and disadvantages with convolutional neural network CNN. Secondly, this article calculates the drift patterns of big data intelligent processing algorithms and conducts relevant performance tests on big data intelligent processing algorithms to verify the effectiveness of the proposed algorithms. Finally, this article addresses the problems in current music genre classification research by introducing Mel filters into the extraction of sound features to further improve the efficiency and speed of the model. And deep learning technology is adopted to construct a music genre classification model based on big data intelligent analysis, further promoting the effective improvement of music genre classification effectiveness.

Comparison Among AlexNet, GoogLeNet and ResNet-18 in Autiomatic Music Genre Classification

This paper compared the performance among three famous convolutional neural networks in classifying music genres. Being carried out on a prominent dataset named Free Music Archive, the experiments show that ResNet-18 performs much better than AlexNet and GoogLeNet in classifying the music genres in a relatively small dataset. Meanwhile, the classification accuracy of each model for each music genre was also recorded. It indicates that different models could be expert in identifying distinct genres. Several genres, including blues, hip-hop and international, were not closely related to the change of models. In general, ResNet-18 reached the highest average classification accuracy at approximate 80%, while AlexNet did best in finding hip-hop music and GoogLeNet had relatively less difference in recognition rates for every genre. Those findings can serve as a reference in future music genre classification tasks and personalized music recommendation based on big data.

Classification and study of music genres with multimodal Spectro-Lyrical Embeddings for Music (SLEM)

Classification and study of music genres with multimodal Spectro-Lyrical Embeddings for Music (SLEM)