Music Genre Classification Research Articles

Retracted] Automatic Classification Method of Music Genres Based on Deep Belief Network and Sparse Representation

Aiming at the problems of poor classification effect, low accuracy, and long time in the current automatic classification methods of music genres, an automatic classification method of music genres based on deep belief network and sparse representation is proposed. The music signal is preprocessed by framing, pre‐emphasis, and windowing, and the characteristic parameters of the music signal are extracted by Mel frequency cepstrum coefficient analysis. The restricted Boltzmann machine is trained layer by layer to obtain the connection weights between layers of the depth belief network model. According to the output classification, the connection weights in the model are fine‐tuned by using the error back‐propagation algorithm. Based on the deep belief network model after fine‐tuning training, the structure of the music genre classification network model is designed. Combined with the classification algorithm of sparse representation, for the training samples of sparse representation music genre, the sparse solution is obtained by using the minimum norm, the sparse representation of test vector is calculated, the category of training samples is judged, and the automatic classification of music genre is realized. The experimental results show that the music genre automatic classification effect of the proposed method is better, the classification accuracy rate is higher, and the classification time can be effectively shortened.

Music Genre Classification Using Neural Network

Music Genre classification on Neural Network is presented in this article. The research work uses spectrogram images generated from the songs timeslices and given as input to NN to do classification of songs to their respective musical genre. The research work focuses on analyzing the parameters of the model. Using two different datasets and implementing NN technique we have achieved an optimized result. The Convolutional Neural Network model presented in this article classifies 10 classes of Music Genres with the improved accuracy.

Music Genre Classification Using Deep Learning with KNN

Music genre is a conventional category that predicts the genre of music belonging to tradition or set of conventions. A music platform, with total assets of $26 billion, is ruling the music streaming stage today. At present, it has a huge number of tunes and it is information base and claims to have the right music score for everybody. Like, Spotify, Amazon music, Wynk has put a great deal in examination to further develop the manner in which clients find and pay attention to music. AI is at the centre of their examination. From NLP to Collaborative sifting to Deep Learning, All music platforms utilizes them all. Tunes are examined dependent on their advanced marks for certain elements, including rhythm, acoustics, energy, danceability, and so forth, to answer that incomprehensible old first-date inquiry. Organizations these days use music arrangement, either to have the option to put suggestions to their clients (like Spotify, Soundcloud) or just as an item (for instance, Shazam). Deciding music sorts is the initial phase toward that path. AI procedures have ended up being very fruitful in removing patterns and examples from a huge information pool. Similar standards are applied in Music Analysis moreover. Machine learning techniques are achieved in some recent years and rarely in deep learning. Most of the current music genre classification uses Machine learning techniques. In this, we present a music dataset which includes many genres like Rock, Pop, folk, Classical and many genres. A Deep learning approach is used in order to train and classify the system using KNN.

Music Genre Classification and Recommendation

There are various sorts to group the music. Classes are for the most part various classifications wherein music is partitioned. In this day and age as music industry develops quickly, there are various kinds of music sorts made. It is essential to classify the music into these classifications, yet it is mind boggling task. In past times this is done physically and prerequisite for programmed framework for type grouping emerges. As a rule, AI techniques are utilized to group music types and profound learning strategy is utilized to prepare the model yet in this undertaking, we will utilize neural organization strategies for the characterization.

Using Machine Learning to Classify Music Genre

Abstract: As Plato once rightfully said, ‘Music gives a soul to the universe, wings to the mind, flight to the imagination and life to everything.’ Music has always been an important art form, and more so in today’s science-driven world. Music genre classification paves the way for other applications such as music recommender models. Several approaches could be used to classify music genres. In this literature, we aimed to build a machine learning model to classify the genre of an input audio file using 8 machine learning algorithms and determine which algorithm is the best suitable for genre classification. We have obtained an accuracy of 91% using the XGBoost algorithm. Keywords: Machine Learning, Music Genre Classification, Decision Trees, K Nearest Neighbours, Logistic regression, Naïve Bayes, Neural Networks, Random Forest, Support Vector Machine, XGBoost

Lyric-Based Classification of Music Genres Using Hand-Crafted Features

The classification of music genres has been studied using various auditory, linguistic, and metadata features. Classification using linguistic features typically results in lower accuracy than classifiers built with auditory features. In this paper, we hand-craft features unused in previous lyrical classifiers such as rhyme density, readability, and the occurrence of profanity. We use these features to train traditional machine learning models for lyrical classification across nine popular music genres and compare their performance. The features that contribute the most towards this classification problem, and the genres that are easiest to predict, are identified. The experiments are conducted on a set of over 20,000 lyrics. A final accuracy of 56.14% was achieved when predicting across the nine genres, improving upon accuracies obtained in previous studies.

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.

Fast Recognition using Bar-based Data Mapping for Gamelan Music Genre Classification

This research aims to develop a gamelan music genre classifier based on the musical mode system determined based on the dominant notes in a certain order. Only experts can discriminate the musical mode system of compositions. The Feed Forward Neural Networks method was used to classify gamelan compositions into three musical mode systems. The challenge is to recognize the musical mode system of compositions between the initial melody without having to analyze the entire melody using a small amount of data for the dataset. Instead of conducting a melodic extraction from audio signal data, the text-based skeletal melody data, which is a form of extracted melodic features, are used for the dataset. Unique corpuses are controlled based on the cardinality of the one-to-many relationship, and a data mapping technique based on the bars is used to increase the number of corpuses. The results show that the proposed method is suitable to solve the specified problems, where the accuracy in recognizing the class of unseen compositions between the initial melody achieves at 86.7%.

Time Signature Detection: A Survey.

This paper presents a thorough review of methods used in various research articles published in the field of time signature estimation and detection from 2003 to the present. The purpose of this review is to investigate the effectiveness of these methods and how they perform on different types of input signals (audio and MIDI). The results of the research have been divided into two categories: classical and deep learning techniques, and are summarized in order to make suggestions for future study. More than 110 publications from top journals and conferences written in English were reviewed, and each of the research selected was fully examined to demonstrate the feasibility of the approach used, the dataset, and accuracy obtained. Results of the studies analyzed show that, in general, the process of time signature estimation is a difficult one. However, the success of this research area could be an added advantage in a broader area of music genre classification using deep learning techniques. Suggestions for improved estimates and future research projects are also discussed.

3D-DCDAE: Unsupervised Music Latent Representations Learning Method Based on a Deep 3D Convolutional Denoising Autoencoder for Music Genre Classification

With unlabeled music data widely available, it is necessary to build an unsupervised latent music representation extractor to improve the performance of classification models. This paper proposes an unsupervised latent music representation learning method based on a deep 3D convolutional denoising autoencoder (3D-DCDAE) for music genre classification, which aims to learn common representations from a large amount of unlabeled data to improve the performance of music genre classification. Specifically, unlabeled MIDI files are applied to 3D-DCDAE to extract latent representations by denoising and reconstructing input data. Next, a decoder is utilized to assist the 3D-DCDAE in training. After 3D-DCDAE training, the decoder is replaced by a multilayer perceptron (MLP) classifier for music genre classification. Through the unsupervised latent representations learning method, unlabeled data can be applied to classification tasks so that the problem of limiting classification performance due to insufficient labeled data can be solved. In addition, the unsupervised 3D-DCDAE can consider the musicological structure to expand the understanding of the music field and improve performance in music genre classification. In the experiments, which utilized the Lakh MIDI dataset, a large amount of unlabeled data was utilized to train the 3D-DCDAE, obtaining a denoising and reconstruction accuracy of approximately 98%. A small amount of labeled data was utilized for training a classification model consisting of the trained 3D-DCDAE and the MLP classifier, which achieved a classification accuracy of approximately 88%. The experimental results show that the model achieves state-of-the-art performance and significantly outperforms other methods for music genre classification with only a small amount of labeled data.

A Middle-Level Learning Feature Interaction Method with Deep Learning for Multi-Feature Music Genre Classification

Nowadays, music genre classification is becoming an interesting area and attracting lots of research attention. Multi-feature model is acknowledged as a desirable technology to realize the classification. However, the major branches of multi-feature models used in most existed works are relatively independent and not interactive, which will result in insufficient learning features for music genre classification. In view of this, we exploit the impact of learning feature interaction among different branches and layers on the final classification results in a multi-feature model. Then, a middle-level learning feature interaction method based on deep learning is proposed correspondingly. Our experimental results show that the designed method can significantly improve the accuracy of music genre classification. The best classification accuracy on the GTZAN dataset can reach 93.65%, which is superior to most current methods.

Research on Architecture for Long-tailed Genre Computer Intelligent Classification with Music Information Retrieval and Deep Learning

In this paper, we propose a Musical Attention Network (MAN) architecture for long-tailed, imbalanced music genre classification which is often ignored and quite prevalent in Music Information Retrieval (MIR). Here, the challenge is to classify the genre of long-tailed music accurately. Inspired by the recent progress in NLP, the proposed model can take advantage of genre correlations to better identify informative segments. Comprehensive experimental results demonstrate our model brings significant improvement comparing with other music genre classification models on a large-scaled benchmark dataset.

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.

Music Genre Classification using Deep Learning

Many subjects are affected by digital music production., including music genre prediction. Machine learning techniques were used to classify music genres in this research. Deep neural networks (DNN) have recently been demonstrated to be effective in a variety of classification tasks. Including music genre classification. In this paper, we propose two methods for boosting music genre classification with convolutional neural networks: 1) using a process inspired by residual learning to combine peak- and average pooling to provide more statistical information to higher level neural networks; and 2) To bypass one or more layers, use shortcut connections. To perform classification, the KNN output is fed into another deep neural network. Our preliminary experimental results on the GTZAN data set show that the above two methods, especially the second one, can effectively improve classification accuracy when compared to two different network topologies.

Music Genre Classification using Optimized Sequential Neural Network

Music makes up a huge portion of the contents stored and used over the internet, with several sites and applications developed solely to provide music-related services to their users/ customers.Some of the most challenging tasks in this scenario would include music classification based on languages and genres, playlist suggestions based on music history, song suggestions based on playlist contents, top genres / songs based on listeners' rating, likes, number of streams, song loops, popularity of artists based on number of songs released per year, hit songs per year, etc. One of the most important stages to solve the above-mentioned challenges would be music genre classification. It would be impractical to analyze each and every song in a given database to identify and classify music genres, even though human beings are better at performing such tasks. Hence, useful Machine Learning algorithms and Deep Learning approaches may be used for accomplishing such tasks with ease. A thorough analysis to understand the different uses of Machine Learning and Deep Learning algorithms and relevance of such algorithms with respect to situations would be made to highlight and contrast the advantages and disadvantages of each approach. The outcomes of the optimized models would be visualized and comparedto the expected outcomes for better perception.

The Effect of Feature Selection on Music Genre Classification

One of the things that affects classification results is the correlation of features to the class of a data. This research was conducted to determine the effect of the reduction of features (independent variable) that have the weakest correlation or have a distant relationship with the class (dependent variable). Bivariate Pearson Correlation is used as a feature selection method and K-Nearest Neighbor is used as a classification method. Results of the test showing that, 75.1% average accuracy was obtained for classification without feature selection, while using feature selection, average accuracy was obtained in the range of 75% - 79.3%. The average accuracy obtained by the selection of features tends to be higher compared to the accuracy obtained without selection of features.

MUSIC GENRE CLASSIFICATION USING NEURAL NETWORKS

In recent years, the complexity of making music has lessened, resulting in many individuals making music and submitting it to streaming media. Because of the huge music streaming media, people are spending a lot of time seeking for certain songs. As a result, the capacity to swiftly categorise music genres has become increasingly important. As machine learning and deep learning technologies progress, convolutional neural networks (CNN) are being employed in several fields, and several CNN-based versions have emerged one after the other. Traditional music genre classification necessitates professional abilities to manually extract features from time series data. We developed a music genre categorization model using CNN's audio advantages and features to save users time while searching for different types of music. During the pre-processing, Librosa is used to convert the original audio files into Mel spectrums. The Mel spectrum is transformed and supplied into the suggested CNN model for training. On the GTZAN dataset, the 10 classifiers' decisions are subjected to a majority vote, with an average accuracy of 84 percent. Music genre categorization using neural networks (NNs) has seen some modest success in recent years. The success of song libraries, machine learning techniques, input formats, and the types of NNs utilised has all been mixed. This article looks at some of the machine learning approaches utilised in this sector. It also involves research on musical genre classification. Images of spectrograms produced from time slices of songs are fed into a neural network (NN) to classify the songs into different musical genres.

Efficient Robust Music Genre Classification with Depthwise Separable Convolutions and Source Separation

Given recent advances in deep music source separation, a feature representation method is proposed that combines source separation with a state-of-the-art representation learning technique that is suitably repurposed for computer audition (i.e. machine listening). A depthwise separable convolutional neural network is trained on a challenging electronic dance music (EDM) data set and its performance is compared to convolutional neural networks operating on both source separated and standard spectrograms. It is shown that source separation improves classification performance in a limited-data setting compared to the standard single spectrogram approach.

Melodic Phrase Attention Network for Symbolic Data-based Music Genre Classification (Student Abstract)

Compared with audio data-based music genre classification, researches on symbolic data-based music are scarce. Existing methods generally utilize manually extracted features, which is very time-consuming and laborious, and use traditional classifiers for label prediction without considering specific music features. To tackle this issue, we propose the Melodic Phrase Attention Network (MPAN) for symbolic data-based music genre classification. Our model is trained in three steps: First, we adopt representation learning, instead of the traditional musical feature extraction method, to obtain a vectorized representation of the music pieces. Second, the music pieces are divided into several melodic phrases through melody segmentation. Finally, the Melodic Phrase Attention Network is designed according to music characteristics, to identify the reflection of each melodic phrase on the music genre, thereby generating more accurate predictions. Experimental results show that our proposed method is superior to baseline symbolic data-based music genre classification approaches, and has achieved significant performance improvements on two large datasets.

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.