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 game-factors approach to cognitive benefits from video-game training: A meta-analysis.

This current study is a meta-analysis conducted on 63 studies on video-game based cognitive interventions (118 investigations, N = 2,079), which demonstrated a moderate and significant training effect on overall gains in cognition, g = 0.25, p < .001. Significant evidence of transfer was found to overall cognition, as well as to attention/perception and higher-order cognition constructs. Examination of specific gameplay features however showed selective and differential transfer to these outcome measures, whereas the genre labels of "action", "strategy", "casual", and "non-casual" were not similarly predictive of outcomes. We therefore recommend that future video-game interventions targeting cognitive enhancements should consider gameplay feature classification approach over existing genre classification, which may provide more fruitful training-related benefits to cognition.

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.

Multilabel Genre Prediction Using Deep-Learning Frameworks

In this study, transfer learning has been used to overcome multilabel classification tasks. As a case study, movie genre classification by using posters has been chosen. Six state-of-the-art pretrained models, VGG16, ResNet, DenseNet, Inception, MobileNet, and ConvNeXt, have been employed for this experiment. The movie posters have been obtained from Internet Movie Database (IMDB). The dataset has been divided using an iterative stratification technique. A sequence of dense layers has been added on top of each model and these models have been trained and fine-tuned. All the results of the models compared considered accuracy, loss, Hamming loss, F1-score, precision, and AUC metrics. When the metrics used were evaluated, the most successful result regarding accuracy has been obtained from the modified DenseNet architecture at 90%. Also, the ConvNeXt, which is the newest model among all, performed quite satisfactorily, reaching over 90% accuracy. This study uses an iterative stratification method to split an unbalanced dataset which provides more reliable results than the classical splitting method which is the common method in the literature. Also, the feature extraction capabilities of the six pretrained models have been compared. The outcome of this study shows promising results regarding multilabel classification. As for future work, it is planned to enhance this study by using natural language processing and ensemble methods.

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.

Oktoechos Classification and Generation of Liturgical Music using Deep Learning Frameworks

An important feature of the music repertoire of the Syrian tradition is the system of classifying melodies into eight tunes,&amp;nbsp; called ’oktoe\={c}hos’.&amp;nbsp; In oktoe\={c}hos tradition, liturgical hymns are sung in eight modes or eight colours (known as eight ’niram’ in Indian tradition). In this paper, recurrent neural network (RNN) models are&amp;nbsp; used for&amp;nbsp; oktoe\={c}hos genre classification with the help of musical texture features (MTF) and i-vectors.The performance of the proposed approaches is evaluated using a newly created corpus of liturgical music in the South Indian language, Malayalam. Long short-term memory (LSTM)-based and gated recurrent unit(GRU)-based experiments report the average classification accuracy of&amp;nbsp; 83.76\%&amp;nbsp; and 77.77\%, respectively, with a significant margin over the i-vector-DNN framework.&amp;nbsp; &amp;nbsp;The experiments demonstrate the potential of RNN models in learning temporal information through MTF in recognizing eight modes of oktoe\={c}hos system. Furthermore, since the Greek liturgy and Gregorian chant also share similar musical traits with Syrian tradition, the musicological insights observed can potentially be applied to those traditions. Generation of oktoe\={c}hos genre music style has also been discussed using an encoder-decoder framework. The quality of the generated files is evaluated using a&amp;nbsp; perception test.

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

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

Le storie sulla morte di Ippolito Nievo: tra fatti, cospirazione e finzione

The article aims to describe the stories about Ippolito Nievo’s death, as depicted in various genres of texts from the 1960s to the present. These texts exhibit a combination of essayistic and narrative elements, incorporating both factual information and the authors’ imaginative interpretations. The popularity of the topic is closely tied to the prevalence of conspiracy theories, which have gained significant traction in recent decades. Furthermore, it should probably be placed within the Risorgimento revisionism current that radicalized in the seventies of the last century and represents a broader trend of revising official historical accounts. Among the texts identified, four were selected for an in-depth analysis both from the point of view of the content and from the point of view of the authors’ approach and their formal choices. These texts do not lend themselves to a simple classification of the literary genre, falling somewhere along the continuum between a narrative essay and a novel.

Content based music genre classification using temporal and spectral features

Content based music genre classification using temporal and spectral features

Жанровая классификация англоязычного электронного гипертекста как объекта лингвосемиотического исследования

The study aims to present the most complete genre classification of the English-language electronic hypertext, which is considered in the work as an object of linguosemiotic research. The paper examines the background of the issue, analyses publications related to the topic and presents the theoretical basis of the study. The analysis of the theoretical basis allowed the authors to conclude that the creation of a genre classification of electronic texts and communication on the Internet based on uniform criteria is difficult and, thus, the work elaborated the genre stratification criteria specifically for the English-language electronic hypertext: a written version of the electronic hypertext, structural complexity, hypertextuality, creolised and multimedia nature, communicative purpose, thematic attribution, linguostylistic features. The criteria developed by the authors are based on the previously proposed ones, but they relate exclusively to electronic hypertexts and not to computer-mediated communication and are characterised by complexity. The scientific novelty of the study lies in analysing the existing genres of the English-language electronic hypertext and constructing their detailed classification, since this issue has not yet been resolved in modern linguistics. As a result, the authors developed a genre classification of the English-language electronic hypertext: official; publicistic; scientific: a) encyclopedic and lexicographic, b) educational; literary; entertaining; social.

К вопросу о содержательно-жанровой классификации аниме и манги

К вопросу о содержательно-жанровой классификации аниме и манги

Darijan Božič’s Musical Theatre Opus in the Context of the Slovenian Musical Theatre Scene: An Escape into the LiteraryDramatic in Opposition to the Desire for the Institutional

Among Slovenian modernist composers, Darijan Božič dedicated the largest part of his work to musical theatre. The genre classifications of his works show that the composer constantly searched for a new form of musical theatre. It would appear that he finally settled on a medium that could almost be designated as a “radio play”, with an emphasis usually on the spoken word, accompanied by occasional interventions of instrumental music. Over time, these interventions appeared to lose their modernist edge (harmonic clusters) and approach postmodernism by juxtaposing diverse elements. Thus, a characteristic of Božic’s works is their double imbalance: the language strongly dominates the music, while at the same time, the composer’s desire to implement avant-garde theatrical procedures in an institutional opera house seems unusual.

Darijan Božič’s Musical Theatre Opus in the Context of the Slovenian Musical Theatre Scene: An Escape into the LiteraryDramatic in Opposition to the Desire for the Institutional

Among Slovenian modernist composers, Darijan Božič dedicated the largest part of his work to musical theatre. The genre classifications of his works show that the composer constantly searched for a new form of musical theatre. It would appear that he finally settled on a medium that could almost be designated as a “radio play”, with an emphasis usually on the spoken word, accompanied by occasional interventions of instrumental music. Over time, these interventions appeared to lose their modernist edge (harmonic clusters) and approach postmodernism by juxtaposing diverse elements. Thus, a characteristic of Božic’s works is their double imbalance: the language strongly dominates the music, while at the same time, the composer’s desire to implement avant-garde theatrical procedures in an institutional opera house seems unusual.

Darijan Božič’s Musical Theatre Opus in the Context of the Slovenian Musical Theatre Scene: An Escape into the LiteraryDramatic in Opposition to the Desire for the Institutional

Among Slovenian modernist composers, Darijan Božič dedicated the largest part of his work to musical theatre. The genre classifications of his works show that the composer constantly searched for a new form of musical theatre. It would appear that he finally settled on a medium that could almost be designated as a “radio play”, with an emphasis usually on the spoken word, accompanied by occasional interventions of instrumental music. Over time, these interventions appeared to lose their modernist edge (harmonic clusters) and approach postmodernism by juxtaposing diverse elements. Thus, a characteristic of Božic’s works is their double imbalance: the language strongly dominates the music, while at the same time, the composer’s desire to implement avant-garde theatrical procedures in an institutional opera house seems unusual.

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.

Darijan Božič’s Musical Theatre Opus in the Context of the Slovenian Musical Theatre Scene: An Escape into the LiteraryDramatic in Opposition to the Desire for the Institutional

Among Slovenian modernist composers, Darijan Božič dedicated the largest part of his work to musical theatre. The genre classifications of his works show that the composer constantly searched for a new form of musical theatre. It would appear that he finally settled on a medium that could almost be designated as a “radio play”, with an emphasis usually on the spoken word, accompanied by occasional interventions of instrumental music. Over time, these interventions appeared to lose their modernist edge (harmonic clusters) and approach postmodernism by juxtaposing diverse elements. Thus, a characteristic of Božic’s works is their double imbalance: the language strongly dominates the music, while at the same time, the composer’s desire to implement avant-garde theatrical procedures in an institutional opera house seems unusual.

Classifico: Music Genre Prediction System using CNN

Classifico is a web-based music genre classifier that quickly and accurately categorizes musical genres. Users can input audio and receive genre predictions through an intuitive web interface. Powered by a trained machine learning model, Classifico analyzes audio features to provide reliable genre classifications. It enhances the music listening experience by enabling efficient exploration of different genres.

Hindi podcast genre prediction using support vector classifier

AbstractIndia experienced a 23% rise in podcast listening after the Covid‐19 pandemic. The pandemic and screen fatigue led people to seek their favourite old audio podcasts. Podcast genre classification allows listeners to compile a playlist of their favourite tracks; it also helps podcast streaming services provide recommendations to users based on the genre of the podcasts they enjoy. Since the COVID‐19 pandemic, the need for educational content in all forms, including podcasts, has skyrocketed, making it even more crucial to anticipate the genre of educational podcasts. Educational podcasts are a sub‐genre of the broader education genre and typically involve audio recordings of discussions, lectures, or interviews on educational topics. Education podcast genre prediction is required to efficiently classify and arrange educational content and make it simpler for listeners to access and absorb pertinent information. This study focuses on Podcast Genre Prediction, specifically for the Hindi language. In this study, our developed PodGen dataset was used, which consists of 550 five‐minute podcasts with 26,867 sentences, where every podcast was manually annotated into one of the four genre categories (Horror, Motivational, Crime, and Romance). The performance comparison of state‐of‐the‐art machine learning techniques on the PodGen dataset was used to demonstrate accuracy. The best performance on testing data was observed in the Support Vector Classifier model with balanced accuracy: 82.42%, precision (weighted): 83.09%, recall (weighted): 82.42%, and F1 score (weighted): 82.39%.