Music Genre Classification System Research Articles

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

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

Implementation of Deep Learning Models on an SoC-FPGA Device for Real-Time Music Genre Classification

Deep neutral networks (DNNs) are complex machine learning models designed for decision-making tasks with high accuracy. However, DNNs require high computational power and memory, which limits such models to fitting on edge devices, resulting in unnecessary processing delays and high energy consumption. Graphical processing units (GPUs) offer reliable hardware acceleration, but their bulky sizes prevent their utilization in portable equipment. System-on-chip field programmable gated arrays (SoC-FPGAs) provide considerable computational power with low energy consumption, making them ideal for edge computing applications, owing to their innovative, flexible, and small design. In this paper, we implement a deep-learning-based music genre classification system on a SoC-FPGA board, evaluate the model’s performance, and provide a comparative analysis across different platforms. Specifically, we compare the performance of long short-term memory (LSTM), convolutional neural networks (CNNs), and a hybrid model (CNN-LSTM) on an Intel Core i7-8550U by Intel Cooperation. The models are fed an acoustic feature called the Mel-frequency cepstral coefficient (MFCC) for training and testing (inference). Then, by using the advanced Vitis AI tool, a deployable version of the model is generated. The experimental results show that the execution speed is increased by 80%, and the throughput rises four times when the CNN-based music genre classification system is implemented on SoC-FPGA.

CLBP Texture Descriptor in Multipartite Complex Network Configuration for Music Genre Classification

Music genres define the characteristics that musical pieces must have to belong to a given class. These characteristics are reflected in the audio signal and, consequently, in the image that represents its spectral content: the spectrogram. In this paper, we propose a Music Genre Classification (MGC) system based on representation with complex networks of CLBP (Completed Local Binary Pattern) texture descriptor codes extracted from spectrograms: mel-spectrogram and gammatonegram. Complex networks were generated using CLBP codes in multipartite configuration: mono, bi, and tripartite networks; where the three node types are signal (CLBP-S), magnitude (CLBP-M) and central (CLBP-C) codes. The networks were mined using conventional, textural, and multipartite topological measures. In order to test the proposed MGC, we used the GTZAN dataset and defined several experiments using combinations of multipartite measures: 1) monopartite, 2) mono and bipartite, and 3) mono, bi and tripartite. All experiments were performed for each spectrogram individually and jointly. In the machine learning stage, we used the ensemble classifier Bagging with Random Forest, and 10-fold cross-validation repeated 100 times. As a main result, it was found that the bipartite measures related to CLBP-C decrease the performance, but the tripartites increased it. Moreover, in most experiments using only gammatonogram the performance was better. Consequently, the experiment using tripartite measures extracted from the gammatonegram revealed a satisfactory result, indicating that the proposed MGC is promising.

Machine Learning Based Music Genre Classification and Recommendation System

Music has an important role in our life. It is also known that music helps to relax and strengthen the human spirit. The widespread use of the Internet has led to significant changes and developments in the music industry. The increase and widespread use of online music listening and sales platforms, the constant updating of these platforms and the classification of music genres can be given as examples of these developments. Music genre classification is an important step for the music recommendation system. In order for music to be classified by individuals require to listen to many songs and choose their genre. This is a difficult process and waste of time. In this paper, it is aimed to classify music according to its genres by using machine learning algorithms and to suggest similar types of music to the user. For this purpose, the features of the music files were extracted with digital signal processing techniques, and the music genres were automatically detected by using machine learning algorithms with the obtained features and a recommendation system was developed. The GTZAN dataset was chosen to be used in the study. Eight different machine learning models were trained in the Jupyter Notebook environment and the findings were compared. For this purpose, the data set was split into two sets as 80% training and 20% testing, and the accuracy of the models was evaluated. Among the tested models, the most successful result was obtained with the XGBoost algorithm with an accuracy rate of 91,792%.

The Classification of Music and Art Genres under the Visual Threshold of Deep Learning.

Wireless networks are commonly employed for ambient assisted living applications, and artificial intelligence-enabled event detection and classification processes have become familiar. However, music is a kind of time-series data, and it is challenging to design an effective music genre classification (MGC) system due to a large quantity of music data. Robust MGC techniques necessitate a massive amount of data, which is time-consuming, laborious, and requires expert knowledge. Few studies have focused on the design of music representations extracted directly from input waveforms. In recent times, deep learning (DL) models have been widely used due to their characteristics of automatic extracting advanced features and contextual representation from actual music or processed data. This paper aims to develop a novel deep learning-enabled music genre classification (DLE-MGC) technique. The proposed DLE-MGC technique effectively classifies the music genres into multiple classes by using three subprocesses, namely preprocessing, classification, and hyperparameter optimization. At the initial stage, the Pitch to Vector (Pitch2vec) approach is applied as a preprocessing step where the pitches in the input musical instrument digital interface (MIDI) files are transformed into the vector sequences. Besides, the DLE-MGC technique involves the design of a cat swarm optimization (CSO) with bidirectional long-term memory (BiLSTM) model for the classification process. The DBTMPE technique has gained a moderately increased accuracy of 94.27%, and the DLE-MGC technique has accomplished a better accuracy of 95.87%. The performance validation of the DLE-MGC technique was carried out using the Lakh MIDI music dataset, and the comparative results verified the promising performance of the DLE-MGC technique over current methods.

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.

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.

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.

Where’d You Get that Idea? Determinants of Creativity and Impact in Popular Music

What are the determinants of creativity, innovation and impact? 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 considers what kind of recombinations are associated with high impact. 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 1,800 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 then used as explanatory variables in a regression that seeks to explain impact (YouTube views per day since the songs' video release), while controlling for other determinants of song impact, 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 impact.

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.

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.

Music-genre classification system based on spectro-temporal features and feature selection

An automatic classification system of the music genres is proposed. Based on the timbre features such as mel-frequency cepstral coefficients, the spectro-temporal features are obtained to capture the temporal evolution and variation of the spectral characteristics of the music signal. Mean, variance, minimum, and maximum values of the timbre features are calculated. Modulation spectral flatness, crest, contrast, and valley are estimated for both original spectra and timbre-feature vectors. A support vector machine (SVM) is used as a classifier where an elaborated kernel function is defined. To reduce the computational complexity, an SVM ranker is applied for feature selection. Compared with the best algorithms submitted to the music information retrieval evaluation exchange (MIREX) contests, the proposed method provides higher accuracy at a lower feature dimension for the GTZAN and ISMIR2004 databases.