Music Genre Classification Research Articles

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.

Comparative Analysis of Three Improved Deep Learning Architectures for Music Genre Classification

Among the many music information retrieval (MIR) tasks, music genre classification is noteworthy. The categorization of music into different groups that came to existence through a complex interplay of cultures, musicians, and various market forces to characterize similarities between compositions and organize collections is known as a music genre. The past researchers extracted various hand-crafted features and developed classifiers based on them. But the major drawback of this approach was the requirement of field expertise. However, in recent times researchers, because of the remarkable classification accuracy of deep learning models, have used similar models for MIR tasks. Convolutional Neural Net- work (CNN), Recurrent Neural Network (RNN), and the hybrid model, Convolutional - Recurrent Neural Network (CRNN), are such prominently used deep learning models for music genre classification along with other MIR tasks and various architectures of these models have achieved state-of-the-art results. In this study, we review and discuss three such architectures of deep learning models, already used for music genre classification of music tracks of length of 29-30 seconds. In particular, we analyze improved CNN, RNN, and CRNN architectures named Bottom-up Broadcast Neural Network (BBNN) [1], Independent Recurrent Neural Network (IndRNN) [2] and CRNN in Time and Frequency dimensions (CRNN- TF) [3] respectively, almost all of the architectures achieved the highest classification accuracy among the variants of their base deep learning model. Hence, this study holds a comparative analysis of the three most impressive architectural variants of the main deep learning models that are prominently used to classify music genre and presents the three architecture, hence the models (CNN, RNN, and CRNN) in one study. We also propose two ways that can improve the performances of the RNN (IndRNN) and CRNN (CRNN-TF) architectures.

Derin Öğrenme İle Türkçe Müziklerden Müzik Türü Sınıflandırması

In this study, an architecture called Convolutional Long Short-term memory deep neural network (CLDNN) based on deep learning, which has not been used before in this field, is used for music genre classification. In addition, a new Turkish Music Database consisting of 200 music belonging to various music genres has been created. The classification performance of the proposed architecture and commonly used machine learning methods has been evaluated on this database. In addition, new features are obtained by using Convolutional Neural Network (CNN), which is the first part of this architecture. Both Mel Frequency Cepstrum Coefficients (MFCC) and log mel filterbank energies were used as input to the Convolutional Neural Network to obtain these new features. In addition to these features, many standard features have been obtained by using various toolboxes. The most successful classification results for all methods are achieved when standard features are used together with new features. The best results among the compared classifiers were achieved with 99.5% by using the remaining part of the proposed architecture, Long Short Term Memory (LSTM), together with the Deep Neural Network (DNN) consisting of fully connected layers.

Where’d you get that idea? Determinants of creativity and impact in popular music

What are the determinants of creativity, novelty, and innovation? In this paper I explore this question through an analysis of data from the Song Explorer podcast, where composers describe how they created a specific song. I mine their accounts to classify their processes into seven different, but not mutually exclusive, theories of the creative process. The result of this exercise suggests that the recombination of existing songs is a major process for the creation of new successful songs. The second step uses the average number of daily YouTube views per day since the songs’ video release as a measure of the song’s impact, and tests how this impact is associated with the seven theories. For each song in the sample, I have one or more other songs which were explicitly indicated as an influence or inspiration. I use the music genre classification system Every Noise at Once, that provides a map of over 1800 genres and millions of songs to create a set of descriptive statistics of the similarity of each song to their inspiration-songs. These statistics are used to measure different recombination strategies in a regression that seeks to explain songs’ relative success, while controlling for other determinants, such as the artists’ established level of popularity. The results confirm the optimal differentiation hypothesis that the simultaneous presence of conventionality together with novelty, and not just one or the other, is a major determinant of creativity and success.

DBTMPE: Deep Bidirectional Transformers-Based Masked Predictive Encoder Approach for Music Genre Classification

Music is a type of time-series data. As the size of the data increases, it is a challenge to build robust music genre classification systems from massive amounts of music data. Robust systems require large amounts of labeled music data, which necessitates time- and labor-intensive data-labeling efforts and expert knowledge. This paper proposes a musical instrument digital interface (MIDI) preprocessing method, Pitch to Vector (Pitch2vec), and a deep bidirectional transformers-based masked predictive encoder (MPE) method for music genre classification. The MIDI files are considered as input. MIDI files are converted to the vector sequence by Pitch2vec before being input into the MPE. By unsupervised learning, the MPE based on deep bidirectional transformers is designed to extract bidirectional representations automatically, which are musicological insight. In contrast to other deep-learning models, such as recurrent neural network (RNN)-based models, the MPE method enables parallelization over time-steps, leading to faster training. To evaluate the performance of the proposed method, experiments were conducted on the Lakh MIDI music dataset. During MPE training, approximately 400,000 MIDI segments were utilized for the MPE, for which the recovery accuracy rate reached 97%. In the music genre classification task, the accuracy rate and other indicators of the proposed method were more than 94%. The experimental results indicate that the proposed method improves classification performance compared with state-of-the-art models.

Music similarity measurement and recommendation system using convolutional neural networks

Due to the massive and growing volume of music on the internet, and the lack of proper management on this massive volume, similarity and music recommendation systems have been designed. The music similarity system, which is the basis of the recommendation system, can automatically generate a user's playlist according to the similar features of each piece of music. In this paper, we designed a desirable music genre classification using convolutional neural network for extracting high-level features from intermediate networks layers. For similarity measurement, we considered cosine similarity and Euclidean distances between feature vectors. We applied this automatic recommendation system on three databases with different genres and showed that the recommender achieves significant accuracy in 10-Best results.

Indonesian Music Classification on Folk and Dangdut Genre Based on Rolloff Spectral Feature Using Support Vector Machine (SVM) Algorithm

Music Genre Classification is one of the interesting digital music processing topics. Genre is a category of artistry, in this case, especially music, to characterize and categorize music is now available in various forms and sources. One of the applications is in determining the music genre classification on folk songs and dangdut songs.The main problem in the classification music genre is to find a combination of features and classifiers that can provide the best result in classifying music files into music genres. So we need to develop methods and algorithms that can classify genres appropriately. This problem can be solved by using the Support Vector Machine (SVM). The genre classification process begins by selecting the song file that will be classified by the genre, then the preprocessing process, the collection features by utilizing feature extraction, and the last process is Support Vector Machine (SVM) classification process to produce genre types from selected song files. The final result of this research is to classify Indonesian folk music genre and dangdut music genre along with the 83.3% accuracy values that indicate the level of system relevance to the results of music genre classification and to provide genre labels on music files as to facilitate the management and search of music files.

Deep Learning Based Music Genre Classification Using Spectrogram

Music genre classification is a vital activity that involves categorizing music genres from audio data. In the field of music information retrieval, music genre classification is frequently utilized. The proposed framework deals with three main steps: data preprocessing, feature extraction, and classification. Convolutional neural network (CNN) is the method used to tackle music genre classification. The proposed system uses feature values of spectrograms generated from slices of songs as the input into a CNN to classify the songs into their music genres. A recommendation system is also implemented after the classification process. The recommendation system aims to recommend songs on each user’s preferences and interests. Extensive experiments carried out on the GTZAN dataset show the effectiveness of the proposed system with respect to other methods.

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

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.

Music Genre Classification using Neural Networks with Data Augmentation

Music Genre Classification using Neural Networks with Data Augmentation

Trajectory of Fifths in Music Data Mining

This article presents a new concept of a music piece’s content representation using the so-called trajectory of fifths. The herein proposed approach is an enhancement to the music data mining techniques based on the analysis of the music signature. Locations of consecutive points of the trajectory of fifths reflect the variability of the music signature over time. This is because their coordinates are defined by the resultant vectors representing consecutive music signatures. If properly quantified, the trajectories of fifths can be a source of valuable information about the variability of harmonic structure of a given piece of music. This article shows the potential applicability of the trajectory of fifths in the processes of music tonality (major/minor key) evaluation as well as music genre classification. The experiments which have been carried out, backed by statistical analysis, confirm the usability of the proposed approach.

Classification of Uzbek music folklore genre

This article provides information on Uzbek musical folklore, ceremonial songs, local styles. The article notes that the convergence of the musical traditions of Fergana - Tashkent, Bukhara -Samarkand, Surkhandarya - Kashkadarya and Khorezm as the leading principles. The views of the scholars are well described. In particular, labor rituals, one of the oldest examples of folk music, are used in some forms of human physical activity, such as plowing, sowing, grinding grain in a ash mill, spinning on a wheel. It is known that the art of glorious maqom is the most valuable and honorable treasure of our national spiritual property. Its original specimens have been used for generations. Even when it was officially banned for large-scale propaganda, the maqom quo remained practical. In addition, the predominance of melody in “yor-yor” depends on its performers. For example, if the free verses of the song “yor-yor” are brought to the bridegroom by the bridegroom's wife or a relative of the bride, then the bride's good qualities, good manners, and perfection are praised.

APPLICATION OF DEEP LEARNING ALGORITHMS IN MUSICOLOGY: AN OVERVIEW

Nowadays, music could be an exceptionally important and perhaps indivisible portion of people's way of life. Music involves various aspects right from generation, its genre detection to its transcription. Composing music may be an exceptionally intrusive challenge that tests the composer’s inventive capacity, whether it is a human or a computer. In spite of the fact that there have been many arguments on the matter, nearly all of the music is some regurgitation or change of a sonic idea created before. Thus, with sufficient information and the proper algorithm, deep learning ought to be able to create music that would sound human. There are numerous genres in musicology which are very different from each other, coming about in individuals having diverse inclinations in musicology. It is a critical and an important issue to classify music and to suggest individuals modern music in applications and stages. Automated music transcription is considered by many researchers to be a key empowering innovation in audio processing. Be that as it may, the execution of translation frameworks is still altogether underneath that of a human master, and correctness detailed in later a long time appears to have come to a constraint, in spite of the fact that the field is dynamic. In the paper, we try to analyse different algorithms enabling music generation, genre classification and music transcription.

SOME NEW RESULTS ON AUTOMATIC IDENTIFICATION OF VIETNAMESE FOLK SONGS CHEO AND QUANHO

Vietnamese folk songs are very rich in genre and content. Identifying Vietnamese folk tunes will contribute to the storage and search for information about these tunes automatically. The paper will present an overview of the classification of music genres that have been performed in Vietnam and abroad. For two types of very popular folk songs of Vietnam such as Cheo and Quan ho, the paper describes the dataset and GMM (Gaussian Mixture Model) to perform the experiments on identifying some of these folk songs. The GMM used for experiment with 4 sets of parameters containing MFCC (Mel Frequency Cepstral Coefficients), energy, first derivative and second derivative of MFCC and energy, tempo, intensity, and fundamental frequency. The results showed that the parameters added to the MFCCs contributed significantly to the improvement of the identification accuracy with the appropriate values of Gaussian component number M. Our experiments also showed that, on average, the length of the excerpts was only 29.63% of the whole song for Cheo and 38.1% of the whole song for Quan ho, the identification rate was only 3.1% and 2.33% less than the whole song for Cheo and Quan ho respectively.

Musical Genre Classification using Convolutional Neural Networks

Music has likewise been separated into Genres and sub sorts on the premise on music. To show that, we contrast the outcomes acquired and a Convolutional Neural Network (CNN). Experiments were conducted on Marsyas databases with distinct characteristics for genre classification. The proposed CNN results in better accuracy in music genre classification.

Bottom-up broadcast neural network for music genre classification

Music genre classification based on visual representation has been successfully explored over the last years. Recently, there has been increasing interest in attempting convolutional neural networks (CNNs) to achieve the task. However, most of the existing methods employ the mature CNN structures proposed in image recognition without any modification, which results in the learning features that are not adequate for music genre classification. Faced with the challenge of this issue, we fully exploit the low-level information from spectrograms of audio and develop a novel CNN architecture in this paper. The proposed CNN architecture takes the multi-scale time-frequency information into considerations, which transfers more suitable semantic features for the decision-making layer to discriminate the genre of the unknown music clip. The experiments are evaluated on the benchmark datasets including GTZAN, Ballroom, and Extended Ballroom. The experimental results show that the proposed method can achieve 93.9%, 96.7%, 97.2% classification accuracies respectively, which to the best of our knowledge, are the best results on these public datasets so far. It is notable that the trained model by our proposed network possesses tiny size, only 0.18M, which can be applied in mobile phones or other devices with limited computational resources. Codes and model will be available at https://github.com/CaifengLiu/music-genre-classification .

Using machine learning to evaluate the fidelity of acoustic simulations

There has been a longstanding tradeoff in evaluating the quality or fidelity of sound recordings: subjective listening tests are time consuming and expensive, but objective measures often fail to capture the nuances of human perception. The research presented here seeks to address this problem by investigating the use of machine learning to evaluate the fidelity of acoustic simulations. To begin, we created a dataset of recordings representing varying levels of audio fidelity. Participants listened to each of the recordings and subjectively classified the perceived fidelity. Various audio features were extracted from the recordings, including several psychoacoustic sound quality metrics and other features commonly used in speech recognition/assessment and music genre classification. These features were input to various machine learning algorithms to test which best modeled the human classifications. A logistic regression model was initially determined to be the most advantageous, dependent on using binary classification. Introducing a reference sound, and calculating each feature relative to the reference, significantly improved accuracy.

Machine Learning Evaluation for Music Genre Classification of Audio Signals

Music genre classification has its own popularity index in the present times. Machine learning can play an important role in the music streaming task. This research article proposes a machine learning based model for the classification of music genre. The evaluation of the proposed model is carried out while considering different music genres as in blues, metal, pop, country, classical, disco, jazz and hip-hop. Different audio features utilized in this study include MFCC (Mel Frequency Spectral Coefficients), Delta, Delta-Delta and temporal aspects for processing the data. The implementation of the proposed model has been done in the Python language. The results of the proposed model reveal an accuracy SVM accuracy of 95%. The proposed algorithm has been compared with existing algorithms and the proposed algorithm performs better than the existing ones in terms of accuracy.

Neuro-genetic programming for multigenre classification of music content

A machine learning approach based on hybridization of genetic programming and neural networks is used to derive mathematical models for music genre classification. We design three multi-label classifiers with different trade-offs between complexity and accuracy, which are able to identify the degree of belonging of music content to ten different music genres. Our approach is innovative as it entirely relies on simple analytical functions and a reduced number of features. Resulting classifiers have an extremely low computational complexity and are suitable to be easily integrated in low-cost embedded systems for real-time applications. The GTZAN dataset is used for model training and to evaluate the accuracy of the proposed classifiers. Despite of the reduced number of features used in our approach, the accuracy of our models is found to be similar to that of more complex music genre classification tools previously published in the literature.

Music genre classification and music recommendation by using deep learning

Today, music is a very important and perhaps inseparable part of people's daily life. There are many genres of music and these genres are different from each other, resulting in people to have different preferences of music. As a result, it is an important and up-to-date issue to classify music and to recommend people new music in music listening applications and platforms. Classifying music by their genre is one of the most useful techniques used to solve this problem. There are a number of approaches for music classification and recommendation. One approach is based on the acoustic characteristics of music. In this study, a music genre classification system and music recommendation engine, which focuses on extracting representative features that have been obtained by a novel deep neural network model, have been proposed. Acoustic features extracted from these networks have been utilised for music genre classification and music recommendation on a data set.