arXiv:2410.23325v1 Announce Type: cross
Abstract: Vocal education in the music field is difficult to quantify due to the individual differences in singers’ voices and the different quantitative criteria of singing techniques. Deep learning has great potential to be applied in music education due to its efficiency to handle complex data and perform quantitative analysis. However, accurate evaluations with limited samples over rare vocal types, such as Mezzo-soprano, requires extensive well-annotated data support using deep learning models. In order to attain the objective, we perform transfer learning by employing deep learning models pre-trained on the ImageNet and Urbansound8k datasets for the improvement on the precision of vocal technique evaluation. Furthermore, we tackle the problem of the lack of samples by constructing a dedicated dataset, the Mezzo-soprano Vocal Set (MVS), for vocal technique assessment. Our experimental results indicate that transfer learning increases the overall accuracy (OAcc) of all models by an average of 8.3%, with the highest accuracy at 94.2%. We not only provide a novel approach to evaluating Mezzo-soprano vocal techniques but also introduce a new quantitative assessment method for music education.
Deep Learning in Vocal Education: A Novel Approach to Evaluating Mezzo-soprano Vocal Techniques
Vocal education in the music field has always been a challenging endeavor, primarily due to the individual differences in singers’ voices and the subjective nature of evaluating singing techniques. However, recent advancements in deep learning offer an exciting opportunity to revolutionize music education by providing a quantitative analysis of vocal techniques. In this article, we explore the application of deep learning models in vocal technique evaluation and introduce a new method for assessing Mezzo-soprano vocal techniques.
One of the key advantages of deep learning is its ability to handle complex data and extract meaningful patterns from it. By leveraging this capability, we can train deep learning models on a diverse range of vocal samples, allowing them to learn the intricate nuances and subtleties of Mezzo-soprano singing. To achieve this, we employ transfer learning, a technique that utilizes pre-trained models on large datasets such as ImageNet and Urbansound8k.
Transfer learning enables us to fine-tune the pre-trained models to specialize in evaluating Mezzo-soprano vocal techniques. By retraining the models on a dedicated dataset called the Mezzo-soprano Vocal Set (MVS), we address the challenge of limited samples for rare vocal types. The MVS contains carefully annotated vocal recordings of Mezzo-soprano singers, providing a rich source of training data for our deep learning models.
Our experimental results demonstrate the effectiveness of transfer learning in improving the precision of vocal technique evaluation. We observed an average increase of 8.3% in the overall accuracy (OAcc) of all models, with the highest accuracy reaching an impressive 94.2%. These findings highlight the potential of deep learning to enhance vocal education by offering a quantitative and objective assessment of Mezzo-soprano vocal techniques.
This research aligns with the broader field of multimedia information systems, where the integration of various disciplines is essential for developing innovative solutions. The concepts explored in this study draw upon the fields of deep learning, where neural networks are trained on large datasets, and vocal education, where subjective assessments are traditionally used. By combining these disciplines, we create a multidisciplinary approach that bridges the gap between quantitative analysis and artistic expression.
Furthermore, this work has implications for other domains such as animations, artificial reality, augmented reality, and virtual realities, where realistic and expressive virtual characters are essential. The use of deep learning models for vocal technique evaluation can contribute to the development of more realistic and human-like virtual characters, enhancing the immersive experience in these virtual environments.
In conclusion, the application of deep learning in vocal education, particularly in evaluating Mezzo-soprano vocal techniques, offers promising avenues for advancing music education. By leveraging transfer learning and constructing dedicated datasets, we can improve the precision of vocal technique assessment and introduce a new quantitative assessment method. This research not only expands our understanding of deep learning but also demonstrates its potential to transform the field of music education and its interconnectedness with multimedia information systems.
arXiv:2410.23724v1 Announce Type: new
Abstract: This chapter provides an overview of research works that present approaches with some degree of cross-fertilisation between Computational Argumentation and Machine Learning. Our review of the literature identified two broad themes representing the purpose of the interaction between these two areas: argumentation for machine learning and machine learning for argumentation. Across these two themes, we systematically evaluate the spectrum of works across various dimensions, including the type of learning and the form of argumentation framework used. Further, we identify three types of interaction between these two areas: synergistic approaches, where the Argumentation and Machine Learning components are tightly integrated; segmented approaches, where the two are interleaved such that the outputs of one are the inputs of the other; and approximated approaches, where one component shadows the other at a chosen level of detail. We draw conclusions about the suitability of certain forms of Argumentation for supporting certain types of Machine Learning, and vice versa, with clear patterns emerging from the review. Whilst the reviewed works provide inspiration for successfully combining the two fields of research, we also identify and discuss limitations and challenges that ought to be addressed in order to ensure that they remain a fruitful pairing as AI advances.
Cross-Fertilisation between Computational Argumentation and Machine Learning
This chapter provides a comprehensive overview of research works that explore the interconnections between Computational Argumentation and Machine Learning. Both fields are highly multi-disciplinary, drawing from various branches of computer science, philosophy, and cognitive science, among others. By examining the literature, the authors identify two overarching themes: argumentation for machine learning and machine learning for argumentation.
Argumentation for Machine Learning
The first theme, argumentation for machine learning, focuses on how the principles and techniques of computational argumentation can improve machine learning systems. This includes using argumentation frameworks to explain the decisions made by machine learning algorithms and provide interpretability to their outputs. By generating arguments and counterarguments, the transparency and trustworthiness of machine learning systems can be enhanced, which is crucial for domains where explainability is required, such as healthcare or legal settings. Additionally, argumentation can help identify biases and inconsistencies in the training data or model, leading to fairer and more robust machine learning systems.
Machine Learning for Argumentation
The second theme, machine learning for argumentation, explores how machine learning techniques can be leveraged to complement and enhance computational argumentation frameworks. This involves using machine learning algorithms to analyze large-scale argumentation corpora and extract patterns, trends, and semantic relationships. By automatically classifying arguments, identifying fallacies, or predicting the outcomes of argumentation processes, machine learning can speed up and improve argumentation-based systems. Furthermore, machine learning can assist in the automatic generation of arguments, helping users construct persuasive arguments or counterarguments in support of their claims.
Spectrum of Works and Types of Interaction
Within these two themes, the authors systematically evaluate a spectrum of works across various dimensions. They consider the type of learning employed, such as supervised learning, reinforcement learning, or unsupervised learning, as well as the form of argumentation framework used, such as formal logic-based frameworks or probabilistic graphical models. By analyzing these dimensions, the authors gain insights into the suitability of different forms of argumentation for supporting specific types of machine learning, and vice versa.
Furthermore, the authors identify and categorize three types of interaction between argumentation and machine learning:
Synergistic Approaches: In these approaches, argumentation and machine learning components are tightly integrated, forming an inseparable whole. This integration allows for bidirectional interactions, where the argumentation framework informs the learning process, and learned models influence the argumentation process. Synergistic approaches exhibit a high level of synergy between the two fields and often result in more valuable and robust systems.
Segmented Approaches: Here, the argumentation and machine learning components are interleaved, with the outputs of one becoming the inputs of the other. This interleaving allows for iterative refinement and improvement of both argumentation and learning. By continuously exchanging information and feedback, segmented approaches can achieve better performance and adaptability.
Approximated Approaches: These approaches involve one component shadowing the other at a chosen level of detail. For instance, a complex argumentation framework might be approximated by a simpler model that captures essential aspects. This approximation allows for scalability and efficiency in processing large-scale argumentation datasets, while still preserving the integrity and reliability of the argumentation process.
Conclusion and Future Directions
This review of the literature provides valuable insights into the integration of computational argumentation and machine learning. It highlights the benefits and potentials of combining these two fields, showcasing successful examples of cross-fertilisation. However, it also acknowledges limitations and challenges that need to be addressed to ensure the continued success of this interdisciplinary synergy as AI advances.
The multi-disciplinary nature of the concepts discussed in the content demonstrates the interconnectedness of computer science, philosophy, cognitive science, and other relevant disciplines. This interconnectedness emphasizes the importance of collaboration and knowledge-sharing across these domains to drive advancements in both computational argumentation and machine learning.
As future directions, researchers could explore more advanced forms of synergistic approaches, finding new ways to tightly integrate argumentation and machine learning methods. Additionally, the development of hybrid models combining formal argumentation frameworks with deep learning techniques could further enhance the interpretability and performance of machine learning systems.
Moreover, addressing challenges such as the scalability of argumentation frameworks, handling uncertain or incomplete data, and developing robust methods for bias detection and mitigation will be critical for the continued progress of this interplay between argumentation and machine learning.
Overall, this review provides a solid foundation for researchers and practitioners to understand the current state of cross-fertilisation between computational argumentation and machine learning and inspires future work towards advancing these fields together in a harmonious manner.
arXiv:2410.23307v1 Announce Type: new
Abstract: Teleparallel description of gravity theories where the gravity is mediated through the tetrad field and consequent torsion provide an alternative route to explain the late time cosmic speed up issue. Generalization of the teleparallel gravity theory with different functional forms of the torsion scalar $T$ leads to $f(T)$ gravity. The role of scalar field played in addressing issues in cosmology and astrophysics has developed an interest in the inclusion of a scalar field along with an interaction potential in the action. Such a generalized gravity theory is dubbed as $f(T,phi)$ theory. We have explored such a gravity theory to reconstruct the interaction potential of the scalar field required for an extended matter bounce scenario. The cosmological implications of the reconstructed scalar field potential are studied considering two viable and well known functional forms of $f(T,phi)$. The energy conditions of these model are discussed to assess the viability of the cosmological models.
Recent research has explored the concept of teleparallel gravity theories as an alternative explanation for the late-time cosmic speed up issue. These theories involve the use of the tetrad field and consequent torsion to mediate gravity. A specific type of teleparallel gravity theory, known as $f(T)$ gravity, has been developed by generalizing the functional form of the torsion scalar $T$.
In this study, a further generalization of the teleparallel gravity theory is considered, incorporating a scalar field $phi$ and an interaction potential. This theory, known as $f(T,phi)$ theory, is explored to reconstruct the interaction potential of the scalar field that is required for an extended matter bounce scenario.
The reconstructed scalar field potentials are then analyzed in terms of their cosmological implications. Two viable and well-known functional forms of $f(T,phi)$ are considered, and the energy conditions of these models are discussed to assess their viability.
Roadmap for the future
1. Further exploration of $f(T)$ gravity
One potential future direction is to continue studying the properties and implications of $f(T)$ gravity theories. This could involve investigating different functional forms of the torsion scalar $T$ and analyzing their effects on the late-time cosmic speed up issue.
2. Investigation of additional scalar field potentials
Expanding on the current study, future research could explore alternative interaction potentials for the scalar field $phi$ in the $f(T,phi)$ theory. This could involve considering different functional forms and analyzing their impact on cosmological models.
3. Experimental confirmation
Another important step is to seek experimental confirmation or observational evidence for the predictions made by the $f(T,phi)$ theory. This could involve analyzing observational data, conducting laboratory experiments, or utilizing other experimental techniques to test the validity of the reconstructed scalar field potential.
4. Assessing the viability of cosmological models
Continued analysis of the energy conditions and other criteria for assessing the viability of cosmological models is crucial. Future research could focus on refining these assessments and applying them to a broader range of models to gain a better understanding of the viability of the $f(T,phi)$ theory.
Challenges and opportunities on the horizon
While the $f(T,phi)$ theory shows promise in addressing the late-time cosmic speed up issue and offering an alternative description of gravity, there are several challenges and opportunities to consider.
Theoretical challenges: Developing a deeper theoretical understanding of the $f(T,phi)$ theory and its implications is essential for further progress. This may involve confronting the theory with other fundamental principles and theories in physics to ensure its consistency.
Experimental challenges: Testing the predictions of the $f(T,phi)$ theory requires advanced experimental techniques and observational data. Experimentalists and observational astronomers will need to collaborate closely with theorists to design and carry out experiments that can confirm or refute the predictions of the theory.
Bridging the gap between theory and observation: Establishing a clear connection between the theoretical framework of the $f(T,phi)$ theory and observational data is essential for its acceptance within the scientific community. Efforts should be made to communicate the theory’s predictions in a way that observational astronomers can test and verify.
Interdisciplinary collaboration: The study of $f(T,phi)$ theory requires collaboration among researchers in different disciplines, including theoretical physics, cosmology, and observational astronomy. Encouraging interdisciplinary collaboration and communication is crucial for making progress in this field.
In conclusion, the $f(T,phi)$ theory offers a potential avenue for explaining the late-time cosmic speed up issue and provides an alternative description of gravity. Future research should focus on further exploring the theory, investigating alternative scalar field potentials, seeking experimental confirmation, and refining the assessments of cosmological models. However, several challenges, including theoretical and experimental hurdles, must be overcome to advance the understanding and acceptance of the $f(T,phi)$ theory.
The article discusses the importance of trajectory prediction in autonomous driving systems and introduces a novel scheme called AiGem (Agent-Interaction Graph Embedding) for predicting traffic vehicle trajectories.
Overview of AiGem
AiGem follows a four-step approach to predict trajectories:
Formulating the Graph: AiGem represents historical traffic interactions as a graph. At each time step, spatial edges are created between the agents, and the spatial graphs are connected in chronological order using temporal edges.
Generating Graph Embeddings: AiGem applies a depthwise graph encoder network to the spatial-temporal graph to generate graph embeddings. These embeddings capture the representation of all nodes (agents) in the graph.
Decoding States: The graph embeddings of the current timestamp are used by a sequential Gated Recurrent Unit decoder network to obtain decoded states.
Trajectory Prediction: The decoded states serve as inputs to an output network consisting of a Multilayer Perceptron, which predicts the trajectories.
Advantages of AiGem
According to the results, AiGem outperforms state-of-the-art deep learning algorithms for longer prediction horizons. This suggests that AiGem is capable of accurately predicting traffic vehicle trajectories for extended periods of time.
Expert Analysis
AiGem introduces an innovative approach to trajectory prediction by leveraging graph embedding techniques. By representing traffic interactions as a graph and using a depthwise graph encoder network, AiGem captures the spatial and temporal relationships between agents. This enables the system to learn and predict complex trajectories in a more accurate manner.
The sequential Gated Recurrent Unit decoder network further enhances the prediction process by leveraging the decoded states from the graph embeddings. This sequential information helps capture the dynamics and evolution of the traffic scenario, leading to more accurate trajectory predictions.
The use of a Multilayer Perceptron in the output network allows for efficient mapping of the decoded states to the predicted trajectories. The MLP can capture non-linear relationships, enabling better trajectory predictions even over longer horizons.
AiGem’s superior performance compared to existing deep learning algorithms for longer prediction horizons suggests its potential to be integrated into real-world autonomous driving systems. By accurately predicting traffic vehicle trajectories, autonomous agents can make better decisions, leading to improved safety and efficiency on the roads.
Future Directions
While AiGem shows promising results, there are several avenues for future research and improvement. One potential direction is the exploration of alternative graph embedding techniques that may capture additional information or improve computational efficiency.
Furthermore, expanding the dataset used for training and evaluation could enhance the generalizability of AiGem. Including a wider range of traffic scenarios, road conditions, and driving styles can help the system adapt to various real-world driving environments.
Additionally, incorporating real-time sensor data from the autonomous car, such as lidar or camera inputs, could further refine trajectory predictions. By incorporating live data, the system can respond to dynamic changes in the environment and improve prediction accuracy.
In conclusion, AiGem presents a novel scheme for traffic vehicle trajectory prediction in autonomous driving systems. Its graph embedding approach, sequential decoding, and MLP-based trajectory prediction contribute to its superior performance. With further research and improvements, AiGem has the potential to enhance the safety and efficiency of autonomous driving systems.
Title: Uncovering the Future Trends in the Industry of Art and Archaeology
Introduction:
The art and archaeology industry has always been a fascinating realm, offering a glimpse into our rich history and cultural heritage. As we move into the future, advancements in technology, changing consumer preferences, and a growing consciousness for preservation are shaping the potential trends in this industry. In this article, we will dive into some of the key points from the text and explore the future possibilities and recommendations for the industry.
1. Advancements in Technology:
Technology has significantly revolutionized the art and archaeology industry, and this trend is set to continue in the future. With the rise of virtual reality (VR) and augmented reality (AR), museum experiences are becoming more immersive and accessible. Visitors can explore ancient artifacts digitally, providing a deeper understanding and engagement. In addition, 3D scanning and printing technologies enable the replication of delicate artifacts, ensuring their preservation and widening their accessibility for educational purposes.
Recommendation: Embrace and invest in cutting-edge technologies to enhance the museum experience. The integration of VR and AR in exhibits, as well as the utilization of 3D scanning and printing, will attract a wider audience and foster a deeper appreciation for art and archaeology.
2. Sustainable Practices:
As societies become more environmentally conscious, the art and archaeology industry must adapt to sustainable practices. The excavation process and preservation techniques often involve the use of chemicals and materials harmful to the environment. Future trends will focus on incorporating eco-friendly alternatives in conservation and restoration methods.
Recommendation: Collaborate with conservation experts and researchers to develop sustainable techniques for excavation, preservation, and restoration. Investing in research and development for eco-friendly materials will not only preserve artifacts but also contribute to the overall sustainability of the industry.
3. Accessibility and Inclusivity:
In the future, the art and archaeology industry will prioritize making historical artifacts and knowledge accessible to a wider audience. This means removing barriers such as physical limitations, language barriers, and geographical distances. Online platforms and digital archives will play a crucial role in democratizing access to art and archaeological discoveries.
Recommendation: Develop comprehensive online platforms and digital archives to allow individuals from around the globe to virtually explore artifacts and participate in interactive educational programs. Translate information into multiple languages and incorporate audio descriptions for visually impaired individuals, ensuring inclusivity for all.
4. Ethical Collection and Repatriation:
The issue of ethical collection and repatriation of cultural artifacts has gained prominence in recent years and will continue to shape the future of the industry. Awareness and demands for the return of stolen or illegally acquired artifacts will push museums and collectors to assess the provenance of their collections. Transparency and collaboration with source countries will be essential in resolving historical injustices.
Recommendation: Conduct thorough due diligence on the provenance of artifacts in collections and demonstrate a commitment to ethical practices. Encourage collaboration between source countries, museums, and collectors to facilitate the repatriation of cultural heritage items that have rightful ownership claims.
Conclusion:
The future of the art and archaeology industry holds exciting possibilities driven by advancements in technology, sustainable practices, accessibility, and ethics. Embracing emerging technologies, investing in sustainability, prioritizing inclusivity, and upholding ethical collection practices will shape an industry that continues to captivate and educate future generations. By reflecting on these trends and adopting the recommended approaches, the industry can unlock its full potential and pave the way for a dynamic and responsible future.
References:
– “Art and Augmented Reality” – MuseumNext
– “Sustainable Conservation” – International Council of Museums
– “Accessibility in Museums” – Museum of Modern Art
– “Ethics and Repatriation” – UNESCO Declaration on the Intentional Destruction of Cultural Heritage
