A Novel Approach to Extending Music Using Latent Diffusion

Using deep learning to synthetically generate music is a research domain that has gained more attention from the public in the past few years. A subproblem of music generation is music extension, or the task of taking existing music and extending it. This work proposes the Continuer Pipeline, a novel technique that uses deep learning to take music and extend it in 5 second increments. It does this by treating the musical generation process as an image generation problem; we utilize latent diffusion models (LDMs) to generate spectrograms, which are image representations of music. The Continuer Pipeline is able to receive a waveform as an input, and its output will be what the pipeline predicts the next five seconds might sound like. We trained the Continuer Pipeline using the expansive diffusion model functionality provided by the HuggingFace platform, and our dataset consisted of 256x256 spectrogram images representing 5-second snippets of various hip-hop songs from Spotify. The musical waveforms generated by the Continuer Pipeline are currently at a much lower quality compared to human-generated music, but we affirm that the Continuer Pipeline still has many uses in its current state, and we describe many avenues for future improvement to this technology

Method for Automatic Level Matching in a Local Network, in Particular a Multicomputer Arrangement, Comprising a Bus System Having Lightwaves Guides, for the Purpose of Collision Recognition

A method is disclosed for automatic level matching in a local network, particularly for a multicomputer arrangement, comprising an optical bus system, for the purpose of collision recognition. Given a required level matching, the process is executed such that a fundamental phase is provided in which level matching devices respectively individually assigned to the computers are synchronized with one another. A first matching phase is provided in which all level matching devices simultaneously execute a process for setting a reference voltage to the lowest received level, whereby the sum of all attenuation components of the signal path at the receiving side of the appertaining computer is taken into consideration. A second matching phase is provided in which all level matching devices successively execute a process for setting the transmission level of their own transmitter such that the emitted light power at its own receiver leads to the receiving power registered as lowest, whereby the sum of all attenuation components of the signal path at the transmitting side of the appertaining computer is taken into consideration

Precise Environmental Searches: Integrating Hierarchical Information Search with EnviroDaemon

Information retrieval has evolved from searches of references, to abstracts, to documents. Search on the Web involves search engines that promise to parse full-text and other files: audio, video, and multimedia. With the indexable Web at 320 million pages and growing, difficulties with locating relevant information have become apparent. The most prevalent means for information retrieval relies on syntax-based methods: keywords or strings of characters are presented to a search engine, and it returns all the matches in the available documents. This method is satisfactory and easy to implement, but it has some inherent limitations that make it unsuitable for many tasks. Instead of looking for syntactical patterns, the user often is interested in keyword meaning or the location of a particular word in a title or header. This paper describes some precise search approaches in the environmental domain that locate information according to syntactic criteria, augmented by the utilization of information in a certain context. The main emphasis of this paper lies in the treatment of structured knowledge, where essential aspects about the topic of interest are encoded not only by the individual items, but also by their relationships among each other. Examples for such structured knowledge are hypertext documents, diagrams, logical and chemical formulae. Benefits of this approach are enhanced precision and approximate search in an already focused, context-specific search engine for the environment: EnviroDaemon

Reverse Engineering of Computer-Based Navy Systems

The financial pressure to meet the need for change in computer-based systems through evolution rather than through revolution has spawned the discipline of reengineering. One driving factor of reengineering is that it is increasingly becoming the case that enhanced requirements placed on computer-based systems are overstressing the processing resources of the systems. Thus, the distribution of processing load over highly parallel and distributed hardware architectures has become part of the reengineering process for computer-based Navy systems. This paper presents an intermediate representation (IR) for capturing features of computer-based systems to enable reengineering for concurrency. A novel feature of the IR is that it incorporates the mission critical software architecture, a view that enables information to be captured at five levels of granularity: the element/program level, the task level, the module/class/package level, the method/procedure level, and the statement/instruction level. An approach to reverse engineering is presented, in which the IR is captured, and is analyzed to identify potential concurrency. Thus, the paper defines concurrency metrics to guide the reengineering tasks of identifying, enhancing, and assessing concurrency, and for performing partitioning and assignment. Concurrency metrics are defined at several tiers of the mission critical software architecture. In addition to contributing an approach to reverse engineering for computer-based systems, the paper also discusses a reverse engineering analysis toolset that constructs and displays the IR and the concurrency metrics for Ada programs. Additionally, the paper contains a discussion of the context of our reengineering efforts within the United States Navy, by describing two reengineering projects focused on sussystems of the AEGIS Weapon System

Electronic Enterprise Engineering - An Outline of an Architecture

In this paper we put forth a vision for organizations to fully embrace computer support. We propose a business-process oriented architecture for Electronic Enterprise Engineering (EEE) that will enable enterprises to manage and evolve all technological and organizational processes effectively; integrate and manage all enterprise information electronically; and empower knowledge workers at all levels with broad decision support capabilities. Our goal is for the EEE architecture to empower an enterprise to make the best use of its informational assets to operate effectively in this new era of electronic commerce. As part of this project we are developing a standard-based, customizable, integrated tool set called the Support Environment for Enterprise Engineering (SEEE). This paper presents the current SEEE architecture and shouts how it supports the three EEE goals

Neural Networks and Structured Knowledge: Knowledge Representation and Reasoning

This collection of articles is the first of two parts of a special issue on Neural Networks and Structured Knowledge. The contributions to the first part shed some light on the issues of knowledge representation and reasoning with neural networks. Their scope ranges from formal models for mapping discrete structures like graphs or logical formulae onto different types of neural networks, to the construction of practical systems for various types of reasoning. In the second part to follow, the emphasis will be on the extraction of knowledge from neural networks, and on applications of neural networks and structured knowledge to practical tasks

Neural Networks and Structured Knowledge: Rule Extraction and Applications

As the second part of a special issue on Neural Networks and Structured Knowledge, the contributions collected here concentrate on the extraction of knowledge, particularly in the form of rules, from neural networks, and on applications relying on the representation and processing of structured knowledge by neural networks. The transformation of the low-level internal representation in a neural network into higher-level knowledge or information that can be interpreted more easily by humans and integrated with symbol-oriented mechanisms is the subject of the first group of papers. The second group of papers uses specific applications as starting point, and describes approaches based on neural networks for the knowledge representation required to solve crucial tasks in the respective application

Integrating Symbol-Oriented and Sub-Symbolic Reasoning Methods into Hybrid Systems

Knowledge representation and reasoning methods in artificial intelligence almost exclusively rely on symbol-oriented methods: Statements describing aspects and objects of the system to be modeled are represented through symbols (mostly text strings), and these symbols are stored in a computer, and manipulated according to the inference rules prescribed by the reasoning method. This works reasonably well in situations where knowledge is available in explicit form, typically through experts or written documents. In situations where knowledge is only available implicitly, e.g. in large data sets, other methods, often based on statistical approaches, have been used more successfully. Many of these methods are based on neural network techniques, which typically represent and process knowledge at a level below symbols; this is often referred to as sub-symbolic representation. This contribution discusses approaches to integrate symbol-oriented reasoning methods with sub-symbolic ones into hybrid systems