Music Genre Classification Research Articles

Hierarchical quantum circuit representations for neural architecture search

Quantum circuit algorithms often require architectural design choices analogous to those made in constructing neural and tensor networks. These tend to be hierarchical, modular and exhibit repeating patterns. Neural Architecture Search (NAS) attempts to automate neural network design through learning network architecture and achieves state-of-the-art performance. We propose a framework for representing quantum circuit architectures using techniques from NAS, which enables search space design and architecture search. We use this framework to justify the importance of circuit architecture in quantum machine learning by generating a family of Quantum Convolutional Neural Networks (QCNNs) and evaluating them on a music genre classification dataset, GTZAN. Furthermore, we employ a genetic algorithm to perform Quantum Phase Recognition (QPR) as an example of architecture search with our representation. Finally, we implement the framework as an open-source Python package to enable dynamic circuit creation and facilitate circuit search space design for NAS.

A Comparison of Machine Learning Algorithms for Music Genre Recognition

Music information retrieval includes the classification of music genres as a key component. However, due to the complexity of music composition, existing artificial classification cannot achieve accurate results. In the music genre classification scenario, machine learning can effectively process large and complex data, bringing more accurate and personalized results for music recommendation. This article will start from three articles and use GTZAN as a data set to compare 3 traditional machine learning methods, including SVM, random forest and logistic regression, together with 2 deep learning models, involving convolutional and feedforward neural networks (CNN and FFNN) in music genres. classification accuracy. The results show that the classification accuracy will be affected by multiple factors such as the characteristics of the audio, whether data processing is performed and so on. In addition, the performance of deep learning has not been significantly better than traditional machine learning as expected. At the same time, the accuracy of CNN and FFNN is heavily dependent on whether the audio is processed into a spectrogram.

Comparison of Multiple Machine Learning Algorithms for Music Genre Classification

With the fast advance of the Internet and the continuous improvement of computer technology, speech recognition has been applied in many fields, and speech recognition has broad prospects for development. Music and audio classification technology can add category labels to music based on music content, which is of great significance in the research and application of efficient organization, retrieval and recommendation of music resources. In order to efficiently classify audio from massive online music data and help users to obtain the most suitable music style, a deep learning classification algorithm based on convolutional neural network (CNN) is proposed. To examine its effectiveness, it is compared with traditional machine learning algorithm. First, the original music data set was preprocessed and then feature extraction was carried out to obtain music features and transform them into spectral maps. Traditional machine learning model and deep learning component model were used for simulation experiments. The testing accuracy of the deep learning model is up to 92%, verifying the model's superiority.

Indian Film Songs Genre Wise Classification Based on Spectral Features

The classification of musical genres has received a lot of attention in recent years due to the several uses it can serve in music information retrieval. Additionally, since genres frequently co-occur in music tracks in Indian Film Songs, it is desirable to record and model the genre correlations in order to enhance the effectiveness of multi-label music genre classification. Present a novel multi-modal strategy that uses a music spectral feature-based classification method to address these problems. In addition, a genre correlations extraction module is offered to identify and model probable genre correlations based on the characteristics of the proposed approach.

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.

Music genre classification based on res-gated CNN and attention mechanism

Music genre classification based on res-gated CNN and attention mechanism

Content based music genre classification using temporal and spectral features

Content based music genre classification using temporal and spectral features

Analyzing the Performance of Machine Learning Models in Music Genre Classification

Abstract: Music genre classification is a fundamental task in the field of music information retrieval (MIR) and has gained significant attention in recent years due to the rapid growth of digital music collections. This research paper presents a comprehensive review of the application of machine learning techniques for music genre classification. We explore various methodologies, feature extraction techniques, and classification algorithms used in the domain, highlighting their strengths, limitations, and recent advancements. The objective of this paper is to provide researchers and practitioners with a comprehensive understanding of the current state-of- the-art approaches, challenges, and future directions in music genre classification using machine learning.

Analysis of Application and Creation Skills of Story-Based MV Micro Video and Big Multimedia Data in Music Communication

MV is an art type that uses TV pictures to supplement information and content that cannot be covered by music. With the rapid development of micro video technology and network technology, micro video has been rapidly popularized. Combining MV and micro video with mobile devices and platforms, it breaks the limitations of traditional movies, TV, and animation and becomes a new highlight of cultural communication in the new era. This paper starts with the selection of MV songs, story script, content design, MV shooting and editing skills, and introduces the specific creation methods of story MV in combination with local culture and proposes a music genre classification algorithm model DCNN-SSA based on spectral space domain feature concern. DCNN-SSA model effectively marks genre features of different music Mel spectrograms in the spatial domain and changes the network structure, thus improving the feature extraction effect while ensuring the validity of the model, thus improving the accuracy of music genre classification.

Novel mathematical model for the classification of music and rhythmic genre using deep neural network

Music Genre Classification (MGC) is a crucial undertaking that categorizes Music Genre (MG) based on auditory information. MGC is commonly employed in the retrieval of music information. The three main stages of the proposed system are data readiness, feature mining, and categorization. To categorize MG, a new neural network was deployed. The proposed system uses features from spectrographs derived from short clips of songs as inputs to a projected scheme building to categorize songs into an appropriate MG. Extensive experiment on the GTZAN dataset, Indian Music Genre(IMG) dataset, Hindustan Music Rhythm (HMR) and Tabala Dataset show that the proposed strategy is more effective than existing methods. Indian rhythms were used to test the proposed system design. The proposed system design was compared with other existing algorithms based on time and space complexity.

Parallel attention of representation global time–frequency correlation for music genre classification

Parallel attention of representation global time–frequency correlation for music genre classification

Comparison of Music Genre Classification Results Using Multilayer Perceptron With Chroma Feature and Mel Frequency Cepstral Coefficients Extraction Features

The development of digital music, especially in genre classification has helped in the ease of studying and searching for a song. There are many ways that can be used to classify the songs/music into genres. Deep Learning is one of the Machine Learning implementation methods that can be used to classify the genre of music. The author managed to create a deep learning-based program using the MLP model with two extraction features, Chroma Feature and MFCC which can classify song/ music genres. Pre-processing of the song is done to take the features of the existing value then the value will be incorporated into the model to be trained and tested. The model was trained and tested with data of 3000 songs which were divided into 10 genres. The model was also tested using the Confusion Matrix with 600 songs of the total available data. The models with Chroma Features as extraction features have an accuracy rate of 53 %, while the MFCC extraction features have an accuracy rate of 80.2 %.

Automatic Classification of Music Genres Using the Deep Learning Approach

Abstract: Music is divided into arbitrary groups known as genres. Music genre classification is a challenging task due to the subjective and ambiguous nature of musical genres. The existing systems for music genre classification suffer from low accuracy and poor generalization of new data. Therefore, there is a need to develop a robust and accurate machine-learning model that can overcome these challenges and classify music audio files into different genres with high accuracy. The main aim of the Music genre classification project is to develop a user-friendly application that accepts audio files as input and classifies the audio file into a particular category of sound to which they belong (to predict its genre) using machine learning models. This application automates the process to reduce manual error and time. It will take an audio file as input and categorizes each file into a particular category like audio belonging to Disco, hip-hop, etc. The final classification is obtained from the collection of individual data. This machine learning model makes use of Support Vector Machine(SVM) and Logistic Regression models. Both models will be integrated into a website to make the project easily accessible.

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.

A Study on Music Genre Classification using Machine Learning

Artificial Intelligence (AI) and Machine Learning can be cited as one of the greatest technological advancements in this century. They are revolutionizing the fields of computing, finance, healthcare, agriculture, music, space and tourism. Powerful models have achieved excellent performance on a myriad of complex learning tasks. One such subset of AI is audio analysis. It entails music information retrieval, music generation and music classification. Music data is one the most abstruse type of source data present, mainly because it is a tough work to extract meaningful correlating features from it. Hence a myriad of algorithms ranging from classical to hybrid neural networks have been tried on music data for a getting a good accuracy. This paper studies the various methods that can be used for music genre classification and compares between them. The accuracies we obtained on a small sample of the Free Music Archive (FMA) dataset were: 46% using Support Vector Classifier (SVC), 40% using Logistic Regression, 67% using Artificial Neural Network (ANN), 77% using Convolutional Neural Networks (CNN), 90% using Convolution-Recurrent Neural Network (CRNN), 88% using Parallel Convolution-Recurrent Neural Network (PCRNN), 73% without using Ensemble technique and 85% using Ensemble technique of Ada Boost. We defined SVC as our baseline model which had 46% accuracy, and we defined the succeeding models to achieve accuracy greater than that. ANN gave us 67% on the test dataset, which was surpassed by CNN at 77%. We noticed image based features worked better at classifying the labels than normal extracted features from the audio. A combination of CNN and RNN worked the best for the dataset, with a series CRNN model giving the best accuracy. Succeeding that, we tried to fit an ensemble model onto our dataset and analyzed its workings. This paper presents a comprehensive study of the various methods that can be used for music genre classification, with a focus on some parallel models and ensembling techniques.

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.

A Study on an Effective Teaching of AI using Google Colab-Based DCGAN Deep Learning Model Building for Music Data Analysis and Genre Classification

This paper deals with an effective teaching method of deep learning using theory and Python in the University. Currently, AI and related technology penetrate into all areas such as manufacturing, fashion, design, medical, novel, agriculture, as well as picture and engineering areas. These AI technologies are strongly connected with the education of universities and K-12. There are two categories of AI-related education. The first one is AI-supported education; another thing is education (teaching and learning) to understand AI. In any case, AI and its application method should be taught with theory and performed with S/W. This paper provides a method on how teachers of universities can teach deep learning well with S/W (Python) matching theory. To present the characteristics of deep learning, this paper uses DCGAN and suggests a teaching method with Google Colab easily. This paper analyzes the dataset with visuals and classifies genres to show characteristics between music and the function of deep learning for students' understanding using DCGAN and the music dataset. The results classify music genres by deep learning well.

Predicting song genre with deep learning

Purpose Genre classification of songs based on lyrics is a challenging task even for humans, however, state-of-the-art natural language processing has recently offered advanced solutions to this task. The purpose of this study is to advance the understanding and application of natural language processing and deep learning in the domain of music genre classification, while also contributing to the broader themes of global knowledge and communication, and sustainable preservation of cultural heritage. Design/methodology/approach The main contribution of this study is the development and evaluation of various machine and deep learning models for song genre classification. Additionally, we investigated the effect of different word embeddings, including Global Vectors for Word Representation (GloVe) and Word2Vec, on the classification performance. The tested models range from benchmarks such as logistic regression, support vector machine and random forest, to more complex neural network architectures and transformer-based models, such as recurrent neural network, long short-term memory, bidirectional long short-term memory and bidirectional encoder representations from transformers (BERT). Findings The authors conducted experiments on both English and multilingual data sets for genre classification. The results show that the BERT model achieved the best accuracy on the English data set, whereas cross-lingual language model pretraining based on RoBERTa (XLM-RoBERTa) performed the best on the multilingual data set. This study found that songs in the metal genre were the most accurately labeled, as their text style and topics were the most distinct from other genres. On the contrary, songs from the pop and rock genres were more challenging to differentiate. This study also compared the impact of different word embeddings on the classification task and found that models with GloVe word embeddings outperformed Word2Vec and the learning embedding layer. Originality/value This study presents the implementation, testing and comparison of various machine and deep learning models for genre classification. The results demonstrate that transformer models, including BERT, robustly optimized BERT pretraining approach, distilled bidirectional encoder representations from transformers, bidirectional and auto-regressive transformers and XLM-RoBERTa, outperformed other models.