Genre Recognition Research Articles

Efficient Music Genre Recognition Using ECAS-CNN: A Novel Channel-Aware Neural Network Architecture.

In the era of digital music proliferation, music genre classification has become a crucial task in music information retrieval. This paper proposes a novel channel-aware convolutional neural network (ECAS-CNN) designed to enhance the efficiency and accuracy of music genre recognition. By integrating an adaptive channel attention mechanism (ECA module) within the convolutional layers, the network significantly improves the extraction of key musical features. Extensive experiments were conducted on the GTZAN dataset, comparing the proposed ECAS-CNN with traditional convolutional neural networks. The results demonstrate that ECAS-CNN outperforms conventional methods across various performance metrics, including accuracy, precision, recall, and F1-score, particularly in handling complex musical features. This study validates the potential of ECAS-CNN in the domain of music genre classification and offers new insights for future research and applications.

Research on music genre recognition method based on deep learning

In this paper, we explore music genre recognition using deep learning methods, examining the application of feature extraction, model construction, and performance evaluation for different music genres. During the data preparation and preprocessing stages, data augmentation and normalization techniques were employed to enhance the model’s generalization capabilities. By constructing multilayer convolutional neural networks (CNNs) and recurrent neural networks (RNNs), we achieved automatic recognition of music genres. In the experimental results analysis, we compared the accuracy and training time of different models, validating the effectiveness of deep learning in the field of music genre recognition. The limitations of deep learning methods and future research directions are also discussed, providing a reference for further studies in music information processing. This study delves into the issue of music genre recognition and proposes a deep learning-based approach. This method leverages neural networks to extract features and learn from audio data, enabling accurate classification of different music genres. Extensive experiments have demonstrated that our method achieves highly satisfactory results in music genre recognition tasks. Furthermore, we optimized the deep learning models, improving their generalization capabilities and accuracy. Our research offers the music industry an efficient and accurate method for music genre recognition, providing new perspectives and technical support for research and applications in the music field.

A method of developing ensemble classifiers for recognizing audio data of various nature

The work developed a method of building ensemble classifiers for recognizing audio data of various nature. The method is a tentative date and requires the following steps. In the first step, the input datasets are selected, which are transformed and divided into training and test samples, respectively. RAVDESS datasets are chosen for the task of audio emotion recognition, music genre recognition is performed on the GTZAN dataset. In the second step, the following seven classifiers were created and investigated as elements of ensemble classifiers: K Nearest Neighbors, Support Vector Machine, Random Forest, XGBoost, Multilayer Perceptron, Convolutional Neural Network and Long Short-term Memory. In the process of training elementary classifiers, the corresponding hyperparameters were adjusted using the Grid Search approach. In the third step, elementary classifiers were combined using the stacking ensemble method with such types of aggregation as soft voting, hard voting, soft voting using the GOMPERTZ function. All possible ensemble combinations starting with three elementary classifiers for recognizing audio emotions and music genres were tested. Therefore, the total number of ensembles studied in the work was 297. The research results for the problem of audio emotion classification showed that the accuracy of recognition according to the Accuracy metric of the best ensemble classifier is 8.1% higher than that of the best elementary classifier in its composition, which is based on on MLP, and according to the F1 metric, this indicator is 8% higher. For the task of recognizing music genres, the corresponding indicators are higher by 5.6 % and 5.2 %, respectively.

Bangla song genre recognition using artificial neural network

<p>Music has a control over human moods and it can make someone calm or excited. It allows us to feel all emotions we experience. Nowadays, people are often attached with their phones and computers listening to music on Spotify, Soundcloud or any other internet platform. Music Information retrieval plays an important role for music recommendation according to lyrics, pitch, pattern of choices, and genre. In this study, we have tried to recognize the music genre for a better music recommendation system. We have collected an amount of 1820 Bangla songs from six different genres including Adhunik, Rock, Hip hop, Nazrul, Rabindra and Folk music. We have started with some traditional machine learning algorithms having K-Nearest Neighbor, Logistic Regression, Random Forest, Support Vector Machine and Decision Tree but ended up with a deep learning algorithm named Artificial Neural Network with an accuracy of 78% for recognizing music genres from six different genres. All mentioned algorithms are experimented with transformed mel-spectrograms and Mean Chroma Frequency Values of that raw amplitude data. But we found that music Tempo having Beats per Minute value with two previous features present better accuracy.</p>

Peake’s Palette

Mervyn Peake’s 1947 novel Titus Groan continually defies genre recognition. Scholar Farah Mendlesohn, however, places it decisively in the category of immersive fantasy. Building on Mendlesohn’s categorization, I further illustrate Peake’s mastery of immersive fantasy in Titus Groan by analysing a symbol imbedded in the text: a green light. This article reaches across media to frame Peake’s descriptive strategies in the context of his role as a visual artist, particularly in his use of abstracted imagery to depict a collective mental and emotional experience between his characters. This, paired with prophetic language surrounding the green light, gestures to the presence of spiritual forces in Peake’s fictional world. I begin by outlining the art historical context in which Peake worked, concentrating on the philosophies of Peake’s artistic contemporaries and the connections between abstract art and notions of universal spiritual forces. I then closely analyse the green light in Titus Groan, linking Peake’s descriptive mode to these philosophies. Finally, I explore narrative evidence for connecting these abstract descriptions of the green light to a spiritual agent at work in the novel—an assertion which heightens an understanding of Peake’s meticulous worldbuilding. Pulling from fantasy scholarship, art historical writings, and inter-artistic theory, this cross-media study argues that Titus Groan, in addition to its oft-viewed physical setting and vibrant characters, houses an observable spiritual plane—into which the green light provides an entry point for the reader. The first study to identify Titus Groan’s green light as a repeated and significant symbol, this article bolsters an understanding of Peake as an immersive fantasy worldbuilder by focusing on the first book of the Gormenghast trilogy. With its narrow focus on Titus Groan and the symbol of the green light, this article alerts scholars to the existence of Peake’s unobserved worldbuilding exertions and forms a basis for further study of the trilogy.

Music Information Retrieval using Deep Learning Techniques

Music Information Retrieval (MIR) is gaining attention due to the surge in digital music and the need for efficient search and recommendation systems. Traditional MIR methods rely on hand-crafted features and rule-based systems, limiting their adaptability. Deep Learning (DL) shows promise in automatically extracting complex patterns from raw data. This paper offers an extensive overview of MIR tasks like classification, genre recognition, similarity search, and recommendation, along with DL models like Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), including LSTM and Transformer architectures, tailored for MIR. Challenges such as data scarcity, computational complexity, and interpretability persist, with proposed solutions like data augmentation, transfer learning, and attention mechanisms. Experimental results on benchmark datasets demonstrate DL's superiority in accuracy, scalability, and robustness over traditional methods. Practical examples highlight DL's potential to revolutionize music search, recommendation, and analysis. Emphasizing the importance of large annotated datasets for training high-quality DL models, strategies for data collection, labeling, and preprocessing are outlined. DL offers promising prospects for advancing MIR by addressing its inherent challenges and establishing new performance benchmarks. Further DL development is expected to drive innovation and enhance digital music consumption experiences through MIR systems. Keywords: Deep Learning, Convolution Neural Network (CNNs), Recurrent Neural Network (RNNs), Digital Music Consumption, Genre recognition.

고전소설에 나타난 삽입시 양상 연구

The purpose of this paper is to discuss the aspects of <Hasaenggiujeon> included in Shin Gwanghan's Gijaegii. As a research method, in order to examine the aspects of poetry inserted in classical novels, the author was considered and the genre recognition was considered in the literary style at the time, and six works inserted in <Hasaengkiwoojeon> were analyzed. The history of literature has developed from the lyrical genre to a major. Poetry inserted into classical novels shows the development of the narrative genre in an era where the lyric genre is active. Shin Gwang-han lived in the early Joseon Dynasty. At this time, it was an era centered on the lyric genre. <Hasaengkiwoojeon> belongs to a love story with a biographical character. In the story of a man and a woman meeting each other and overcoming obstacles and achieving love, the psychological description of the characters is very important. In the early Joseon Dynasty, narrative techniques representing the psychology of characters did not develop, and the role was replaced by inserted poems. It contributed to the completeness of the narrative by revealing the psychology of the character by taking advantage of the characteristics of the poem revealing the emotion.

Music Genre Recognition Based on Hybrid Feature Vector with Machine Learning Methods

Music genre recognition is one of the main problems in infotainment tools and music streaming service providers for different tasks such as music selection, classification, recommendation, and personal list creation. Automatic genre recognition systems can be useful for different music-based systems, especially different music platforms. Therefore, this study aimed to classify music genres using machine learning. In this context, GTZAN dataset consisting of 10 classes was used. In this dataset, data augmentation was applied by segmentation. Each record of 30 seconds was divided into 10 parts, increasing the number of samples in the dataset by a factor of 10. Then, features were extracted from the audio signals. The resulting features are chroma, harmony, mel frequency cepstral coefficients, perceptr, root mean square, roll-off, spectral centroid, tempo, and zero crossing rate. The types, variances, and averages of the obtained features were used. Thus, 57 features were obtained. This feature set was pre-processed by delimiting the decimal part, standardization, and label encoding. In the last step, classification was made with different machine learning methods and the results were compared. As a result of hyperparameter optimization in the Extra Tree model, 92.3% performance was achieved. Precision recall and f-score values are 92.4%, 92.3%, and 92.3%, respectively. As a result, an efficient and high-performance model in music genre recognition was created.

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.

Multimodal knowledge graph construction of Chinese traditional operas and sentiment and genre recognition

The advancement of digital technologies promotes the documentation of traditional operas, leaving a large amount of data but in a state of fragmentation. Constructing a knowledge graph (KG) is an effective way to realize the knowledge integration and reduce fragmentation, which can help the public understand traditional operas. However, constructed KGs in cultural heritage are mainly unimodal, lacking the ability to give the public a comprehensive perception, especially when they do not have related in-depth knowledge. In this paper, we take Chinese nation-level traditional operas as an example and construct a traditional opera ontology OpeOnto including classes with deep semantics (topic, sentiment). Then, we adopt an OpeOnto-driven way to construct multimodal knowledge graph OpeMKG including images and music links from several data resources. Aimed at analysing sentiments in OpeMKG and the automatic genre recognition of works for the preparation of automatic updating of OpeMKG, we develop a novel unified sentiment and genre recognition model (SGRM) for traditional operas with multimodal fusion and multi-task learning (MTL). The proposed model is examined on the built dataset of traditional operas and experimental results demonstrate its superiority compared with several state-of-the-art baselines.

Kiñit classification in Ethiopian chants, Azmaris and modern music: A new dataset and CNN benchmark.

In this paper, we create EMIR, the first-ever Music Information Retrieval dataset for Ethiopian music. EMIR is freely available for research purposes and contains 600 sample recordings of Orthodox Tewahedo chants, traditional Azmari songs and contemporary Ethiopian secular music. Each sample is classified by five expert judges into one of four well-known Ethiopian Kiñits, Tizita, Bati, Ambassel and Anchihoye. Each Kiñit uses its own pentatonic scale and also has its own stylistic characteristics. Thus, Kiñit classification needs to combine scale identification with genre recognition. After describing the dataset, we present the Ethio Kiñits Model (EKM), based on VGG, for classifying the EMIR clips. In Experiment 1, we investigated whether Filterbank, Mel-spectrogram, Chroma, or Mel-frequency Cepstral coefficient (MFCC) features work best for Kiñit classification using EKM. MFCC was found to be superior and was therefore adopted for Experiment 2, where the performance of EKM models using MFCC was compared using three different audio sample lengths. 3s length gave the best results. In Experiment 3, EKM and four existing models were compared on the EMIR dataset: AlexNet, ResNet50, VGG16 and LSTM. EKM was found to have the best accuracy (95.00%) as well as the fastest training time. However, the performance of VGG16 (93.00%) was found not to be significantly worse (P < 0.01). We hope this work will encourage others to explore Ethiopian music and to experiment with other models for Kiñit classification.

Singer and Genre recognition using deep learning

In this paper, we try to solve the two mostly occurred problems which are genre recognition and singer identification from common pieces of music. For that, we use a system that helps us to achieve our target that is based on a music information retrieval system, in which the system can process our files and the system can generate the desired results. The system is divided into different phases and for the music recognition, first, there is a need to separate the vocal features of the singer from the music clip because, with the background music, it’s slightly difficult to analyze the vocal of the singer, then extract music features, here we work on some of the music features like tempo, RMS, song duration, frequency, dynamic range, and tonality. The RNN (recurrent neural network) has been used for training the model and testing our data set. After the training of the models, the test classifier with different unknown audio files, and the results are 91% accurate for singer identification and 90% for music genre recognition. The system will identify the singer as well as the genre of the song from the same segment of the file. For the data set, the training data used 10 different Indian and Pakistani male and female singers’ audio files in Sindhi, Hindi, and Urdu language for six types of genres like rock, pop, jazz, disco, hip-hop, and classic. The songs were sung by only one singer, the group singing and live concerts music are limitations of our system.

Few-Shot Multi-Agent Perception with Ranking-Based Feature Learning.

In this article, we focus on performing few-shot learning (FSL) under multi-agent scenarios in which participating agents only have scarce labeled data and need to collaborate to predict labels of query observations. We aim at designing a coordination and learning framework in which multiple agents, such as drones and robots, can collectively perceive the environment accurately and efficiently under limited communication and computation conditions. We propose a metric-based multi-agent FSL framework which has three main components: an efficient communication mechanism that propagates compact and fine-grained query feature maps from query agents to support agents; an asymmetric attention mechanism that computes region-level attention weights between query and support feature maps; and a metric-learning module which calculates the image-level relevance between query and support data fast and accurately. Furthermore, we propose a specially designed ranking-based feature learning module, which can fully utilize the order information of training data by maximizing the inter-class distance, while minimizing the intra-class distance explicitly. We perform extensive numerical studies and demonstrate that our approach can achieve significantly improved accuracy in visual and acoustic perception tasks such as face identification, semantic segmentation, and sound genre recognition, consistently outperforming the state-of-the-art baselines by 5%-20%.

Musical Tones Classification using Machine Learning

Abstract: In today's scenario, most things are digitized. Music is one of the most famous forms of art, learned by different people and taught by several musicians. Therefore, music information retrieval (MIR) and its applications are gaining popularity following advances in machine learning technology. Various applications such as genre recognition, song recognition, automatic score generation, music transcription, tempo, beat type, etc. have been developed to achieve this goal. However, little research has been done to identify musical instruments. The new method proposed here develops a machine learning model in combination with the Mel-frequency-cepstral-coefficient by extracting audio features directly from the raw audio dataset. A total of 600 audio files containing sound samples from 6 different instruments are used for system training and instrument prediction.

Genre Recognition from Symbolic Music with CNNs: Performance and Explainability

In this work, we study the use of convolutional neural networks for genre recognition in symbolically represented music. Specifically, we explore the effects of changing network depth, width and kernel sizes while keeping the number of trainable parameters and each block’s receptive field constant. We propose an architecture for handling MIDI data that makes use of multiple resolutions of the input, called Multiple Sequence Resolution Network (MuSeReNet). These networks accept multiple inputs, each at half the original sequence length, representing information at a lower resolution. Through our experiments, we outperform the state-of-the-art for MIDI genre recognition on the topMAGD and MASD datasets. Finally, we adapt various post hoc explainability methods to the domain of symbolic music and attempt to explain the predictions of our best performing network.

Cover-based multiple book genre recognition using an improved multimodal network

Cover-based multiple book genre recognition using an improved multimodal network

Editorial: JCMS Special Issue of the first Conference on AI Music Creativity

The International conference on AI Music Creativity (AIMC, https://aimusiccreativity.org/) is the merger of the international workshop on Musical Metacreation MUME (https://musicalmetacreation.org/) and the conference series on&amp;nbsp;Computer Simulation of Music Creativity&amp;nbsp;(CSMC, https://csmc2018.wordpress.com/). This special issue gathers selected papers from the first edition of the conference along with paper versions of two of its keynotes.This special issue contains six papers that apply novel approaches to the generation and classification of music. Covering several generative musical tasks such as composition, rhythm generation, orchestration, as well as some machine listening task of tempo and genre recognition, these selected papers present state of the art techniques in Music AI. The issue opens up with an ode on computer Musicking, by keynote speaker Alice Eldridge, and Johan Sundberg's use of analysis-by-synthesis for musical applications.

Construction of Intelligent Recognition and Learning Education Platform of National Music Genre Under Deep Learning.

In order to study the application of the deep learning (DL) method in music genre recognition, this study introduces the music feature extraction method and the deep belief network (DBN) in DL and proposes the parameter extraction feature and the recognition classification method of an ethnic music genre based on the DBN with five kinds of ethnic musical instruments as the experimental objects. A national musical instrument recognition and classification network structure based on the DBN is proposed. On this basis, a music library classification retrieval learning platform has been established and tested. The results show that, when the DBN only contains one hidden layer and the number of neural nodes in the hidden layer is 117, the basic convergence accuracy is approximately 98%. The first hidden layer has the greatest impact on the prediction results. When the input sample feature size is one-third of the number of nodes in the first hidden layer, the network performance is basically convergent. The DBN is the best way for softmax to identify and classify national musical instruments, and the accuracy rate is 99.2%. Therefore, the proposed DL algorithm performs better in identifying music genres.

Application of Multilevel Local Feature Coding in Music Genre Recognition

When the current method is used to recognize music genre style, the extracted features are not fused, which leads to poor recognition effectiveness. Therefore, the application research based on multilevel local feature coding in music genre recognition is proposed. Features of music are extracted from timbre, rhythm, and pitch, and the extracted features are fused based on D-S evidence theory. The fused music features are input into the improved deep learning network, and the storage system structure is determined from the advantages of cloud storage availability, manageability, and expansibility. It is divided into four modules: storage layer, management layer, structure layer, and access layer. The model of music genre style recognition is constructed to realize the application research based on multilevel local feature coding in music genre recognition. The experimental results show that the recognition accuracy of the proposed method is always at a high level, and the mean square error positively correlated with the number of beats. After segmentation, the waveform is denser, which has a good application effect.

The collection of theater music data and genre recognition under the internet of things and deep belief network

The collection of theater music data and genre recognition under the internet of things and deep belief network