Automatic Music Genre Classification Research Articles

Music Genre Classification by Machine Learning Techniques

Abstract: Music plays a very important role in people’s lives. Music brings like-minded people together and is the glue that holds communities together. Communities can be recognized by the type of songs that they compose, or even listen to the purpose of our project and research is to find a better machine learning algorithm than the pre-existing models that predicts the genre of songs. Genres can be defined as categorical labels created by humans to identify or characterize the style of music. The concept of automatic music genre classification has become very popular in recent years as a result of the rapid growth of the digital entertainment industry. offering the potential to enhance a wide range of visual-based applications.

An Automatic Classification Method for Multiple Music Genres by Integrating Emotions and Intelligent Algorithms

ABSTRACT In this paper, we present an approach to improve the effectiveness of automatic classification of music genres by integrating emotion and intelligent algorithms. We propose an automatic recognition and classification algorithm for music spectra, which takes into account emotional cues that can be extracted from music to improve classification accuracy. To achieve this goal, we set different weight coefficients, which are continuously adjusted based on the convergence process of the previous iteration. The size of each weighting coefficient is adaptively controlled to reduce the number of iterations of the reconstruction process, thereby reducing the algorithm’s computational complexity and speeding up its convergence. We conducted several experiments to evaluate the effectiveness of our proposed method. The experimental results demonstrate that the automatic classification method of music genres, which integrates emotion and intelligent algorithms, can significantly improve the accuracy of automatic music genre classification. Moreover, our approach reduces the algorithm’s computational complexity, resulting in a faster convergence speed. Our proposed approach provides a promising solution for automatic music genre classification that takes into account emotional cues. The integration of emotion and intelligent algorithms can help achieve higher accuracy and reduce computational complexity, making the proposed method applicable in various scenarios.

Song Classification using Machine Learning

Abstract: The classification of music by genre is crucial in the modern world since the number of music tracks, both online and offline, is growing quickly. We must appropriately index them in order to have greater access to them. To retrieve music from a vast collection, automatic music genre classification is crucial. The majority of the current methods for categorising music genres rely on machine learning. We give a music dataset with ten distinct genres in this article. The system is trained and classified using a Deep Learning technique. Convolution neural networks are employed in this instance for training and classification. For audio analysis, feature extraction is the most important step. For sound samples, the Mel Frequency Cepstral Coefficient (MFCC) is employed as a feature vector. The suggested technique uses feature vector extraction to categorise music into different genres. Our findings indicate that our system's accuracy level is approximately 76%, which will significantly increase and facilitate the automatic classification of musical genres.

Locally Activated Gated Neural Network for Automatic Music Genre Classification

Automatic music genre classification is a prevailing pattern recognition task, and many algorithms have been proposed for accurate classification. Considering that the genre of music is a very broad concept, even music within the same genre can have significant differences. The current methods have not paid attention to the characteristics of large intra-class differences. This paper presents a novel approach to address this issue, using a locally activated gated neural network (LGNet). By incorporating multiple locally activated multi-layer perceptrons and a gated routing network, LGNet adaptively employs different network layers as multi-learners to learn from music signals with diverse characteristics. Our experimental results demonstrate that LGNet significantly outperforms the existing methods for music genre classification, achieving a superior performance on the filtered GTZAN dataset.

Automatic Music Genre Classification based on CNN and LSTM

Various applications of machine learning are discovered and receiving more and more attention contemporarily. The music industry has benefited from the incorporation of artificial intelligence, especially the field of music classification, as machines are able to organize big data in a more efficient manner than the traditional human expertises. This paper compares two machine learning models, the Convolutional Neural Network model (CNN), and the Long Short Term Memory model (LSTM), from their architectures, functionality, to classification accuracy based on empirical data. The models were trained on two datasets, GTZAN and FMA. The result indicates that the CNN model achieved a 56.0% and 50.5% accuracy for the two datasets respectively, outperforming the LSTM model, which had a 42.0% and 33.5% accuracy. The paper aims to analyze the two models’ capability for music classification and determine which model is better suited for the task. These results shed light on guiding further exploration of computer music.

Distance Metric Learnt Kernel-Based Music Classification Using Timbral Descriptors

Automatic music genre classification based on distance metric learning (DML) is proposed in this paper. Three types of timbral descriptors, namely, mel-frequency cepstral coefficient (MFCC) features, modified group delay features (MODGDF) and low-level timbral feature sets are combined at the feature level. We experimented with k nearest neighbor (kNN) and support vector machine (SVM)-based classifiers for standard and DML kernels (DMLK) using GTZAN and Folk music dataset. Standard kernel-based kNN and SVM-based classifiers report classification accuracy (in%) of 79.03 and 90.16, respectively, on GTZAN dataset and 86.60 and 92.26, respectively, for Folk music dataset, with the best performing RBF kernel. A further improvement was observed when DML kernels were used in place of standard kernels in the kernel kNN and SVM-based classifiers with an accuracy of 84.46%, 92.74% (GTZAN), 90.00 and 96.23 (Folk music dataset) for DMLK-kNN and DMLK-SVM, respectively. The results demonstrate the potential of DML kernels in music genre classification task.

Music Genre Classification for Indian Music Genres

Abstract: Due to the enormous expansion in the accessibility of music data, music genre classification has taken on new significance in recent years. In order to have better access to them, we need to correctly index them. Automatic music genre classification is essential when working with a large collection of music. For the majority of contemporary music genre classification methodologies, researchers have favoured machine learning techniques. In this study, we employed two datasets with different genres. A Deep Learning approach is utilised to train and classify the system. A convolution neural network is used for training and classification. In speech analysis, the most crucial task is to perform speech analysis is feature extraction. The Mel Frequency Cepstral Coefficient (MFCC) is utilised as the main audio feature extraction technique. By extracting the feature vector, the suggested method classifies music into several genres. Our findings suggest that our system has an 80% accuracy level, which will substantially improve on further training and facilitate music genre classification. Keywords: Music Genre Classification, CNN, KNN, Music information retrieval, feature extraction, spectrogram, GTZAN dataset, Indian music genre dataset.

Deep Convolutional Neural Network for musical genre classification via new Self Adaptive Sea Lion Optimization

Automatic Music Genre Classification (MGC) is said to be a basic element for retrieving the music information. In fact, music genre labels are very useful to organize albums, songs, and artists in border groups that share similar characteristics. Henceforth, a precise and effective MGC system is required to enhance the retrieved music genres. This paper tactics to propose a new music genre classification model that includes two major processes: Feature extraction and Classification. In the feature extraction phase, features like “non-negative matrix factorization (NMF) features, Short-Time Fourier Transform (STFT) features and pitch features” are extracted. The extracted features are then subjected to a classification process via Deep Convolutional Neural Network (DCNN) model. In order to improve the classification accuracy, the DCNN model is trained using a new Self Adaptive SA-SLnO (SA-SLnO) model through optimizing the weight. Finally, the performance of adopted work is evaluated over other existing approaches with respect to error analysis and statistical measures, respectively.

Automatic Music Genre Classification and Its Relation with Music Education

Because the classification saves time in the learning process and enables this process to take place more easily, its contribution to music learning cannot be denied. One of the most valid and effective methods in music classification is music genre classification. Given the rapid progress of music production in the world and the significant increase in the number of data, the process of classifying music genres has now become too complex to be done by humans. Considering the successful results of deep neural networks in this field, the aim is to develop a deep learning algorithm that can classify 10 different music genres. To reveal the efficiency of the model by comparing it with others, we make the classification using the GTZAN dataset, which was previously used in many studies and retains its validity. In this article, we use a convolutional neural network (CNN) to classify music genres, taking into account the previous successful results. Unlike previous studies in which CNN was used as a classifier, we represent music segments in the dataset by mel frequency cepstral coefficients (MFCC) instead of using visual features or representations. We obtain MFCCs by preprocessing the music pieces in the dataset, then train a CNN model with the acquired MFCCs and determine the success of the model with the testing data. As a result of this study, we develop a model that is successful in classifying music genres by using smaller data than previous studies.

Texture selection for automatic music genre classification

Music Genre Classification is the problem of associating genre-related labels to digitised music tracks. It has applications in the organisation of commercial and personal music collections. Often, music tracks are described as a set of timbre-inspired sound textures. A subset of the sound textures is often selected to represent the entire track. In this paper, we evaluate the impact of texture selection on automatic music genre classification. Although previous work has selected textures by linear downsampling, no extensive work has been done to evaluate how texture selection benefits music genre classification. We also present a novel texture selector based on K-Means aimed to identify diverse sound textures within each track. Our results show that capturing texture diversity within tracks is important towards improving classification performance. Our results also indicate that our K-Means based texture selector is able to achieve significant improvements over the baseline with fewer textures per track than the other texture selectors evaluated. We also show that using multiple texture representations allows further opportunities for feature selection to improve classification performance.

Granular methods in automatic music genre classification: a case study

Classification of music files by using the characteristics of the songs based on its genre is a very popular application of machine learning. The focus of this work is on automatic music genre classification based on granular computing methods (fuzzy rough, rough and near sets). We have proposed a modified form of supervised learning algorithm based on tolerance near sets (TCL 2.0) with a goal of exploring the scalability of the learning algorithm to a well researched music database composed of several genres. In the tolerance near set method, tolerance classes are directly induced from the dataset using the tolerance level e and a distance function. We have compared the tolerance-based near set algorithm to a family of nearest neighbour (NN) algorithms based on fuzzy rough methods (FRNN) available in the WEKA platform. In terms of performance, the classification accuracy of TCL 2.0 is identical to the Bayesian Networks (BN) Algorithm, and comparable with the Sequential Minimal Optimization (SMO) Algorithm. However, the average classification accuracy of FRNN algorithms and the classical rough sets algorithm is better than TCL 2.0, BN and SMO algorithms. For this dataset, any accuracy over 90% is considered a very good classification accuracy which is achieved by all tested classifiers in this work.

Genre Classification for Brazilian Music using Independent and Discriminant Features

Digital music files are largely available both online and in private local collections. These databases may comprise hundreds or thousands of files, which in some cases may not carry tagged information about their content, making the search for the desired audio files very time consuming. An important task in this context is to organize the available database according to the prevailing musical genre. The purpose of this work is to develop an automatic music genre classification system able to identify international music genres (i.e. pop, rock, classic, soul, funk) and also typical Brazilian rhythms such as Samba, Forr\'o and Brazilian Popular Music. The proposed signal processing chain comprises two stages. Initially, audio signal features are computed and their relevance for music genre identification estimated. Independent component analysis is applied to reduce mutual redundancy among the audio attributes. In the following, different classifiers based on neural networks and support vector machines are applied for music genre identification. The proposed system efficiency is evaluated using an experimental dataset.

Automatic music genre classification based on musical instrument track separation

The aim of this article is to investigate whether separating music tracks at the pre-processing phase and extending feature vector by parameters related to the specific musical instruments that are characteristic for the given musical genre allow for efficient automatic musical genre classification in case of database containing thousands of music excerpts and a dozen of genres. Results of extensive experiments show that the approach proposed for music genre classification is promising. Overall, conglomerating parameters derived from both an original audio and a mixture of separated tracks improve classification effectiveness measures, demonstrating that the proposed feature vector and the Support Vector Machine (SVM) with Co-training mechanism are applicable to a large dataset.

Turkish Music Genre Classification using Audio and Lyrics Features

Music Information Retrieval (MIR) has become a popular research area in recent years. In this context, researchers have developed music information systems to find solutions for such major problems as automatic playlist creation, hit song detection, and music genre or mood classification. Meta-data information, lyrics, or melodic content of music are used as feature resource in previous works. However, lyrics do not often used in MIR systems and the number of works in this field is not enough especially for Turkish. In this paper, firstly, we have extended our previously created Turkish MIR (TMIR) dataset, which comprises of Turkish lyrics, by including the audio file of each song. Secondly, we have investigated the effect of using audio and textual features together or separately on automatic Music Genre Classification (MGC). We have extracted textual features from lyrics using different feature extraction models such as word2vec and traditional Bag of Words. We have conducted our experiments on Support Vector Machine (SVM) algorithm and analysed the impact of feature selection and different feature groups on MGC. We have considered lyrics based MGC as a text classification task and also investigated the effect of term weighting method. Experimental results show that textual features can also be effective as well as audio features for Turkish MGC, especially when a supervised term weighting method is employed. We have achieved the highest success rate as 99,12\% by using both audio and textual features together.

Comparison of classification of musical genre obtained by subjective tests and decision algorithms

The aim of the study is to conduct subjective tests on audio excerpt assignment to music genre and to carry out automatic classification of musical genres with the use of decision algorithms. First, the musicology background of classifying music into styles and genres is discussed. Then, an online survey is created to perform subjective tests with a group of listeners, whose task is assigning audio samples to selected music genres. Next, a set of music descriptors is proposed and all music excerpts are parametrized. For checking parameter redundancy the Principal Component Analysis (PCA) is performed. The created database containing feature vectors is then utilized for automatic music genre classification. Two classifiers, namely: Belief Networks and SMO (Sequential Minimal Optimization Algorithm) are employed for the purpose of music genre classification. The last step of this study is to compare the efficiency of the listeners classification with the automatic music genre classification system designed by the authors. Conducted tests show to what extent listeners’ assignment and the automatic classification results agree. It is also observed that very known performers are often rated without problems. Contrarily, songs of less known artists are more difficult to assign to the given genre.

Musical Genre Classification Using SVM and Audio Features

The need of advance Music Information Retrieval increases as well as a huge amount of digital music files distribution on the internet. Musical genres are the main top-level descriptors used to organize digital music files. Most of work in labeling genre done manually. Thus, an automatic way for labeling a genre to digital music files is needed. The most standard approach to do automatic musical genre classification is feature extraction followed by supervised machine-learning. This research aims to find the best combination of audio features using several kernels of non-linear Support Vector Machines (SVM). The 31 different combinations of proposed audio features are dissimilar compared in any other related research. Furthermore, among the proposed audio features, Linear Predictive Coefficients (LPC) has not been used in another works related to musical genre classiffication. LPC was originally used for speech coding. An experimentation in classifying digital music file into a genre is carried out. The experiments are done by extracting feature sets related to timbre, rhythm, tonality and LPC from music files. All possible combination of the extracted features are classified using three different kernel of SVM classifier that are Radial Basis Function (RBF), polynomial and sigmoid. The result shows that the most appropriate kernel for automatic musical genre classification is polynomial kernel and the best combination of audio features is the combination of musical surface, Mel-Frequency Cepstrum Coefficients (MFFC), tonality and LPC. It achieves 76.6 % in classification accuracy.

Importance of audio feature reduction in automatic music genre classification

Multimedia database retrieval is rapidly growing and its popularity in online retrieval systems is consequently increasing. Large datasets are major challenges for searching, retrieving, and organi...

A Study on Music Genre Recognition and Classification Techniques

Automatic classification of music genre is widely studied topic in music information retrieval (MIR) as it is an efficient method to structure and organize the large numbers of music files available on the Internet. Generally, the genre classification process of music has two main steps: feature extraction and classification. The first step obtains audio signal information, while the second one classifies the music into various genres according to extracted features. In this paper, we present a study on techniques for automatic music genre recognition and classification. We first describe machine learning based chord recognition methods, such as hidden Markov models, neural networks, dynamic Bayesian network and rule-based methods, and template matching methods. We then explain supervised, unsupervised and semi-supervised classification methods classifying music genres. Finally, we briefly describe the proposed method for automatic classification of music genres, which consists of three steps: chord labeling, genre matching and classification.

Automatic Music Genre Classification of Audio Signals with Machine Learning Approaches

AbstractMusical genre classification is put into context by explaining about the structures in music and how it is analyzed and perceived by humans. The increase of the music databases on the personal collection and the Internet has brought a great demand for music information retrieval, and especially automatic musical genre classification. In this research we focused on combining information from the audio signal than different sources. This paper presents a comprehensive machine learning approach to the problem of automatic musical genre classification using the audio signal. The proposed approach uses two feature vectors, Support vector machine classifier with polynomial kernel function and machine learning algorithms. More specifically, two feature sets for representing frequency domain, temporal domain, cepstral domain and modulation frequency domain audio features are proposed. Using our proposed features SVM act as strong base learner in AdaBoost, so its performance of the SVM classifier cannot improve using boosting method. The final genre classification is obtained from the set of individual results according to a weighting combination late fusion method and it outperformed the trained fusion method. Music genre classification accuracy of 78% and 81% is reported on the GTZAN dataset over the ten musical genres and the ISMIR2004 genre dataset over the six musical genres, respectively. We observed higher classification accuracies with the ensembles, than with the individual classifiers and improvements of the performances on the GTZAN and ISMIR2004 genre datasets are three percent on average. This ensemble approach show that it is possible to improve the classification accuracy by using different types of domain based audio features.

Automatic Music Genre Classification of Audio Signals with Machine Learning Approaches

Automatic Music Genre Classification of Audio Signals with Machine Learning Approaches