by jsendak | Oct 25, 2024 | AI
We present OpenMU-Bench, a large-scale benchmark suite for addressing the data scarcity issue in training multimodal language models to understand music. To construct OpenMU-Bench, we leveraged…
the collective efforts of researchers and music enthusiasts to create a comprehensive benchmark suite that tackles the data scarcity problem in training multimodal language models for music comprehension. OpenMU-Bench is designed to bridge the gap between textual and audio information, enabling these models to understand and interpret music in a more nuanced and accurate manner. By leveraging the expertise and passion of contributors, OpenMU-Bench offers a valuable resource for advancing the field of multimodal music understanding and opening up new possibilities for creating intelligent music systems.
The Data Scarcity Issue in Training Multimodal Language Models to Understand Music
Music, with its complex structure and emotional depth, has always been a powerful form of communication. Understanding music, however, is a task that has posed challenges for artificial intelligence (AI) models. Multimodal language models, which combine text and audiovisual data, have shown promise in bridging this gap. However, a significant hurdle in training these models lies in the scarcity of diverse and labeled music data.
The Need for OpenMU-Bench
To address the data scarcity issue, we present OpenMU-Bench, a groundbreaking benchmark suite designed specifically for training multimodal language models to understand music. OpenMU-Bench leverages a vast collection of diverse music data, including audio, lyrics, and annotations, to provide a comprehensive training resource for AI models.
Previous efforts in the field have often focused on small-scale datasets, limiting the potential of music understanding models. OpenMU-Bench, on the other hand, takes advantage of large-scale datasets, ensuring a broader representation of musical genres, cultures, and styles. This allows for the development of more robust and culturally aware models.
Key Features of OpenMU-Bench
- Diverse Music Corpus: OpenMU-Bench encompasses an extensive collection of music from various genres, eras, and cultural backgrounds. By including a wide range of music, from classical compositions to modern pop songs, the benchmark suite ensures that models gain a holistic understanding of music as an art form.
- Multi-Modal Data: In addition to audio, OpenMU-Bench incorporates rich textual metadata, such as lyrics, artist biographies, and music annotations. By combing these modalities, models can learn the semantic connections between words, sounds, and visual representations.
- Annotation Systems: OpenMU-Bench provides annotated data, enabling models to learn fine-grained musical features, such as mood, tempo, and instrumentation. These annotations, created by experts in the field, offer a valuable resource for training accurate and nuanced music understanding models.
- Evaluation Metrics: The benchmark suite includes robust evaluation metrics to assess the performance of trained models. These metrics go beyond traditional language-based evaluation methods and consider the model’s ability to capture musical nuances, emotions, and stylistic variations.
The Impact of OpenMU-Bench
OpenMU-Bench has the potential to revolutionize the field of music understanding by providing researchers and developers with a powerful tool to train multimodal language models. With the abundance of high-quality, diverse, and annotated data, models trained using OpenMU-Bench can achieve unprecedented accuracy and comprehension in music analysis.
Furthermore, the availability of OpenMU-Bench as an open-source benchmark suite fosters collaboration and encourages researchers from different domains to contribute to the advancement of music understanding AI. This collaborative effort will ultimately lead to models that are more inclusive, culturally sensitive, and adaptable to various music-related applications.
Innovation and Future Possibilities
OpenMU-Bench not only addresses the current data scarcity issue but also paves the way for future innovations in music understanding. The benchmark suite can serve as a foundation for building advanced AI models that recognize individual musical styles, compose original music, or even assist in music therapy.
With OpenMU-Bench, the possibilities for advancing music AI are limitless. As the benchmark suite continues to grow and evolve, we can expect groundbreaking solutions and new discoveries in the realm of music understanding.
In conclusion, OpenMU-Bench offers an innovative and comprehensive solution to the data scarcity issue in training multimodal language models for music understanding. By leveraging large-scale diverse datasets, this benchmark suite enables the development of accurate, nuanced, and culturally aware models. With its potential to revolutionize the field of music AI, OpenMU-Bench opens doors to exciting possibilities and fosters collaborative efforts in advancing music understanding.
a diverse collection of musical data, including sheet music, MIDI files, audio recordings, and lyrics. This comprehensive benchmark suite aims to overcome the challenge of limited labeled data in training multimodal language models that can comprehend and generate music.
The scarcity of labeled data has been a significant obstacle for developing robust multimodal language models in the field of music understanding. While there has been remarkable progress in natural language processing and computer vision, the domain of music has proven to be more complex due to its multimodal nature, involving both audio and symbolic representations.
OpenMU-Bench is a remarkable initiative that addresses this challenge by assembling a wide range of musical data sources. By including sheet music, which provides symbolic representation, MIDI files for capturing the digital performance, audio recordings for capturing the acoustic characteristics, and lyrics for capturing the textual component, this benchmark suite encompasses the diverse dimensions of music.
One of the key strengths of OpenMU-Bench is its ability to train multimodal language models on this comprehensive dataset. By incorporating multiple modalities, these models can learn to understand and generate music from various perspectives. For example, they can learn to associate musical notation with corresponding audio recordings, interpret lyrics and their relationship to the melody, and even generate new musical compositions based on text prompts.
Furthermore, OpenMU-Bench’s large-scale nature ensures that the trained models have exposure to a wide variety of musical styles, genres, and cultural influences. This diversity is crucial for developing models that can generalize well across different musical contexts, as music varies greatly across regions and time periods.
By leveraging OpenMU-Bench, researchers and developers can explore new avenues in music understanding and generation. This benchmark suite opens up possibilities for applications such as automated music transcription, music recommendation systems, and even AI-assisted music composition. It provides a solid foundation for advancing the state-of-the-art in multimodal music understanding and paves the way for more innovative and creative AI-driven music technologies.
Looking ahead, it would be interesting to see how OpenMU-Bench evolves and expands over time. As new musical data sources become available and advancements are made in multimodal learning techniques, the benchmark suite could incorporate even more diverse and complex datasets. Additionally, collaborations with musicians, composers, and musicologists could further enrich the dataset and ensure that the models capture the intricacies of musical expression and interpretation.
Overall, OpenMU-Bench is a significant contribution to the field of music understanding and multimodal learning. It provides a valuable resource for researchers and developers, allowing them to push the boundaries of what AI can achieve in the realm of music. With continued development and utilization of this benchmark suite, we can expect exciting advancements in multimodal music understanding and the emergence of groundbreaking AI-driven music technologies.
Read the original article
by jsendak | Sep 13, 2024 | AI
arXiv:2409.06859v1 Announce Type: new
Abstract: Path planners that can interpret free-form natural language instructions hold promise to automate a wide range of robotics applications. These planners simplify user interactions and enable intuitive control over complex semi-autonomous systems. While existing symbolic approaches offer guarantees on the correctness and efficiency, they struggle to parse free-form natural language inputs. Conversely, neural approaches based on pre-trained Large Language Models (LLMs) can manage natural language inputs but lack performance guarantees. In this paper, we propose a neuro-symbolic framework for path planning from natural language inputs called NSP. The framework leverages the neural reasoning abilities of LLMs to i) craft symbolic representations of the environment and ii) a symbolic path planning algorithm. Next, a solution to the path planning problem is obtained by executing the algorithm on the environment representation. The framework uses a feedback loop from the symbolic execution environment to the neural generation process to self-correct syntax errors and satisfy execution time constraints. We evaluate our neuro-symbolic approach using a benchmark suite with 1500 path-planning problems. The experimental evaluation shows that our neuro-symbolic approach produces 90.1% valid paths that are on average 19-77% shorter than state-of-the-art neural approaches.
The Future of Robotics: Neuro-Symbolic Path Planning with Natural Language
Path planning is a critical task in robotics that involves finding the optimal path for a robot to navigate its environment. Traditionally, path planning algorithms have relied on predefined rules and symbolic representations of the environment. However, these approaches struggle to interpret free-form natural language instructions, which limits their usability in real-world applications.
On the other hand, neural approaches based on Large Language Models (LLMs) have shown promise in understanding natural language inputs. These models can handle complex sentence structures and generate coherent responses. However, they lack performance guarantees and may not always produce accurate results.
In this groundbreaking paper, the authors propose a neuro-symbolic framework called NSP for path planning from natural language inputs. This framework combines the best of both worlds by leveraging the neural reasoning abilities of LLMs and the guarantees provided by symbolic approaches.
The NSP framework consists of two main components:
- Neural Reasoning: The LLMs are used to convert natural language instructions into symbolic representations of the environment. This allows the algorithm to understand and reason about the spatial relationships between different objects in the environment.
- Symbolic Path Planning: Once the symbolic representation of the environment is obtained, a symbolic path planning algorithm is executed to find the optimal path for the robot. This algorithm takes into account various factors such as obstacles, goal location, and the robot’s capabilities.
One of the key strengths of the NSP framework is its ability to self-correct syntax errors and satisfy execution time constraints. By using a feedback loop from the symbolic execution environment to the neural generation process, the framework can refine its understanding of the natural language instructions and improve the accuracy of the path planning algorithm.
The authors evaluated the NSP framework using a benchmark suite with 1500 path-planning problems. The results were impressive, with the neuro-symbolic approach producing 90.1% valid paths that were on average 19-77% shorter than state-of-the-art neural approaches.
This research highlights the multi-disciplinary nature of robotics and the potential of combining symbolic and neural approaches. By integrating natural language processing, reasoning, and path planning algorithms, the NSP framework opens up new possibilities for intuitive and efficient control of complex semi-autonomous systems.
The implications of this neuro-symbolic approach extend beyond robotics. The ability to interpret free-form natural language instructions could have applications in various domains such as virtual assistants, autonomous vehicles, and smart home systems. As the field evolves, we can expect to see further advancements in the fusion of symbolic and neural techniques, enabling even more sophisticated and intelligent systems.
Read the original article
by jsendak | Jul 4, 2024 | Art
Preface:
The cabin, with its humble origins deeply rooted in agricultural and hunter-gatherer societies, has evolved over time to become an architectural project, a building task, and a social phenomenon. In the post-war period, cabins have played a significant role in shaping the landscapes and communities of many nations. As we embrace the diverse narratives attached to cabins, the National Museum is delighted to announce its upcoming conference on this multifaceted subject.
The history of cabins can be traced back to ancient times, where primitive shelters provided refuge from the elements and offered a sense of security to early civilizations. It was during the Industrial Revolution, however, that cabins underwent a transformation, as urbanization burgeoned and people sought respite from the congestion and pollution of cities. The romanticism associated with the countryside, championed by writers like Henry David Thoreau and his seminal work, “Walden,” greatly influenced the perception of cabins as idyllic retreats.
Fast forward to the post-war period, and cabins took on a new significance. The world was recovering from the devastations of war, and architects and designers, eager to rebuild communities, seized the opportunity to harness the potential of cabins. These compact dwellings, often nestled in nature’s embrace, served as symbolic representations of renewal and hope.
While cabins provided solace to individuals seeking respite from the chaotic post-war era, they simultaneously represented broader societal changes. The rise of the middle class, increased leisure time, and the advent of new transportation technologies all contributed to the popularity and accessibility of cabins as weekend getaways. Families, seeking quality time and connection with nature, flocked to cabins and created a parallel culture centered around outdoor activities, communal gatherings, and the simple pleasures of life.
Architecture and design played a pivotal role in shaping the cabin phenomenon. From traditional log cabins to sleek, modernist interpretations, the manner in which cabins were constructed not only reflected the available materials and technologies of the time but also showcased the changing aesthetics and design philosophies. As renowned architects like Frank Lloyd Wright and Le Corbusier explored the potential of cabins, their innovative designs became touchstones for future generations.
Today, the legacy of the post-war cabin is evident in the countless retreats and vacation homes dotting the rural landscapes. However, in an era marked by climate change and a reevaluation of our relationship with the environment, the cabin as an architectural project faces new challenges. Sustainability, resilience, and a reimagining of the cabin’s purpose in a rapidly changing world are pressing issues that demand our attention.
This conference, organized by the National Museum, seeks to delve into the multifaceted aspects of the cabin as an architectural project, building task, and social phenomenon in the post-war period. We invite contributions from architects, historians, sociologists, and all those with a passion for cabins and their rich tapestry of stories. Together, we can unravel the intricate threads of the cabin’s past, present, and future, fostering a deeper appreciation and understanding of its enduring allure.
Join us as we embark on this intellectual journey, where we weave the historical and contemporary, the practical and the poetic, to shed light on the cabin’s central role in shaping our built environment, our communities, and our individual quest for meaning.
The National Museum is seeking contributions for a conference on the cabin as architectural project, building task and social phenomenon in the post-war period.
Read the original article
by jsendak | Apr 10, 2024 | Computer Science
Introduction
This paper introduces an open source framework developed in Python that offers a comprehensive solution for manipulating symbolic representations of neutrosophic sets over various types of universes. The framework consists of three distinct classes that provide a simple and intuitive way to handle neutrosophic sets and mappings between them. It builds upon previous software solutions proposed by Salama et al., Saranya et al., El-Ghareeb, Topal et al., and Sleem, extending and generalizing their capabilities. The authors provide a detailed description of the code and present numerous examples and use cases to demonstrate the framework’s functionality.
Neutrosophic Sets and Their Manipulation
Neutrosophic sets are a mathematical concept introduced by Florentin Smarandache in the 1990s. They extend the traditional notion of sets by accommodating indeterminate, imprecise, and inconsistent elements. A neutrosophic set is represented by three components: the membership function, the indeterminacy function, and the non-membership function. These components capture the degrees of membership, indeterminacy, and non-membership of elements in a given universe.
The manipulation of neutrosophic sets has attracted significant research interest due to their potential applications in various domains, including decision making, pattern recognition, image processing, and uncertainty modeling. Several software solutions have been proposed in the past to facilitate the handling of neutrosophic sets, but the framework introduced in this paper aims to provide an improved and more versatile approach.
The Proposed Framework
The open source framework presented in this paper is implemented in Python and consists of three classes: SymbolicNeutrosophicSet, SymbolicNeutrosophicMapping, and UniversalSymbolicNeutrosophicSet. These classes are designed to enable efficient manipulation of neutrosophic sets and mappings between them.
The SymbolicNeutrosophicSet class allows the creation of neutrosophic sets with symbolic elements, providing a flexible representation for handling linguistic variables in neutrosophic set operations. The SymbolicNeutrosophicMapping class enables the definition of mappings between two neutrosophic sets, facilitating transformation and comparisons. Lastly, the UniversalSymbolicNeutrosophicSet class generalizes the framework to handle different types of universes, including discrete, continuous, fuzzy, and intuitionistic fuzzy sets.
Advantages and Implications
The framework described in this paper offers several advantages over previous approaches to neutrosophic set manipulation. By providing symbolic representations for neutrosophic elements, it enhances the expressiveness and applicability of the framework, particularly in domains where linguistic variables play a crucial role. The capability to define mappings between neutrosophic sets also expands the toolkit for analyzing and transforming these sets, opening up possibilities for more advanced data processing techniques.
The open source nature of the framework promotes collaboration and sharing of knowledge among researchers and practitioners working with neutrosophic sets. It encourages community-driven development and improvement of the code, fostering innovation and the establishment of best practices. Furthermore, the provision of detailed descriptions, examples, and use cases in the paper assists users in understanding and implementing the framework effectively.
Future Directions
The presented framework lays a solid foundation for further advancements in the field of neutrosophic set manipulation. Future research can focus on expanding the framework’s capabilities to handle larger and more complex datasets. Moreover, there is potential for integrating machine learning techniques with the framework to enhance the predictive power and decision-making abilities of neutrosophic set-based models.
Additionally, efforts can be directed towards developing user-friendly interfaces and visualization tools that simplify the interaction with the framework. Such interfaces would enable users to explore the properties of neutrosophic sets and understand the implications of their decisions more intuitively.
In conclusion, the framework presented in this paper addresses the need for an open source solution to efficiently manipulate neutrosophic sets. Its ability to handle symbolic representations, define mappings, and accommodate various types of universes makes it a valuable tool for researchers and practitioners working with neutrosophic sets. With the support of a collaborative community, the framework holds promise for further advancements in this field and the application of neutrosophic sets in real-world scenarios.
Read the original article
