A scheme for content based retrieval of music data in MIDI format

With phenomenal increases in the generation and storage of digital audio data in several applications, there is growing need for organizing audio data in databases and providing users with fast access to desired data. This paper presents a scheme for the content-based query and retrieval of audio data stored in MIDI format. This is based on extraction of melody from the MIDI files and suitably comparing with the melody of the query. The results of retrieval using the proposed algorithm are presented.<br /

Three Dimensional Continuous DP Algorithm for Multiple Pitch Candidates in Music Information Retrieval System

This paper threats theoretical and practical issues that implement a music information retrieval system based on query by humming. In order to extract accuracy features from the user's humming, we propose a new retrieval method based on multiple pitch candidates. Extracted multiple pitches have shown to be very important parameters in determining melodic similarity, but it is also clear that the confidence measures feature which are obtained from the power are important as well. Furthermore, we propose extending the traditional DP algorithm to three dimensions so that multiple pitch candidates can be treated. Simultaneously, at the melody representation technique, we propose the DP paths are changed dynamically to be able to take relative values so that they can respond to insert or omit notes

From Keyword Search to Exploration: How Result Visualization Aids Discovery on the Web

A key to the Web's success is the power of search. The elegant way in which search results are returned is usually remarkably effective. However, for exploratory search in which users need to learn, discover, and understand novel or complex topics, there is substantial room for improvement. Human computer interaction researchers and web browser designers have developed novel strategies to improve Web search by enabling users to conveniently visualize, manipulate, and organize their Web search results. This monograph offers fresh ways to think about search-related cognitive processes and describes innovative design approaches to browsers and related tools. For instance, while key word search presents users with results for specific information (e.g., what is the capitol of Peru), other methods may let users see and explore the contexts of their requests for information (related or previous work, conflicting information), or the properties that associate groups of information assets (group legal decisions by lead attorney). We also consider the both traditional and novel ways in which these strategies have been evaluated. From our review of cognitive processes, browser design, and evaluations, we reflect on the future opportunities and new paradigms for exploring and interacting with Web search results

MIRMaid: An interface for a content based Music Information Retrieval test-bed

Music Information Retrieval (MlR) is the interdisciplinary science of retrieving information from music and includes influences from different areas, like music perception and cognition, music analysis, signal processing, music indexing and information retrieval [Futrelle & Downie, 2003]. To produce the most efficient MlR systems, test-beds are commonly used to test different combinations of parameters against each other. The purpose of this dissertation was to investigate the composition of algorithms for MlR systems by constructing an interface that could form part of a test-bed. It differs from other interfaces and frameworks that are used in MlR test-beds because it is focused on small scale test-beds. MIRMaid is an acronym for Music Information Retrieval Modular aid and is an interface that allows different content based retrieval tasks to be compared against each other to find optimal combinations of retrieval parameters for specialised problem domains. The dissertation describes the process of how the MIRMaid interface was developed, modified and refined. A big challenge was to design the user experiments in a way that considered potential users of the interface while using the test subjects I had at my disposal. I decided to use the simplest queries to highlight basic similarities between novice and potential expert users. The performance of the interface was judged by user ratings on a questionnaire. The interface performed reasonably well with expert users and novice users. Despite these results there were a few interesting observations that were returned from the user experiments related to the experiment design and the task explanations. Some suggestions are also provided for extending the interface to allow it to be used with other types of data. The possibility is also investigated for using the interface as a tool for simplifying the process of integrating modules from different sources

Content-based music retrieval by acoustic query

Ph.DDOCTOR OF PHILOSOPH

Information Retrieval for Multivariate Research Data Repositories

In this dissertation, I tackle the challenge of information retrieval for multivariate research data by providing novel means of content-based access. Large amounts of multivariate data are produced and collected in different areas of scientific research and industrial applications, including the human or natural sciences, the social or economical sciences and applications like quality control, security and machine monitoring. Archival and re-use of this kind of data has been identified as an important factor in the supply of information to support research and industrial production. Due to increasing efforts in the digital library community, such multivariate data are collected, archived and often made publicly available by specialized research data repositories. A multivariate research data document consists of tabular data with $m$ columns (measurement parameters, e.g., temperature, pressure, humidity, etc.) and $n$ rows (observations). To render such data-sets accessible, they are annotated with meta-data according to well-defined meta-data standard when being archived. These annotations include time, location, parameters, title, author (and potentially many more) of the document under concern. In particular for multivariate data, each column is annotated with the parameter name and unit of its data (e.g., water depth [m]). The task of retrieving and ranking the documents an information seeker is looking for is an important and difficult challenge. To date, access to this data is primarily provided by means of annotated, textual meta-data as described above. An information seeker can search for documents of interest, by querying for the annotated meta-data. For example, an information seeker can retrieve all documents that were obtained in a specific region or within a certain period of time. Similarly, she can search for data-sets that contain a particular measurement via its parameter name or search for data-sets that were produced by a specific scientist. However, retrieval via textual annotations is limited and does not allow for content-based search, e.g., retrieving data which contains a particular measurement pattern like a linear relationship between water depth and water pressure, or which is similar to example data the information seeker provides. In this thesis, I deal with this challenge and develop novel indexing and retrieval schemes, to extend the established, meta-data based access to multivariate research data. By analyzing and indexing the data patterns occurring in multivariate data, one can support new techniques for content-based retrieval and exploration, well beyond meta-data based query methods. This allows information seekers to query for multivariate data-sets that exhibit patterns similar to an example data-set they provide. Furthermore, information seekers can specify one or more particular patterns they are looking for, to retrieve multivariate data-sets that contain similar patterns. To this end, I also develop visual-interactive techniques to support information seekers in formulating such queries, which inherently are more complex than textual search strings. These techniques include providing an over-view of potentially interesting patterns to search for, that interactively adapt to the user's query as it is being entered. Furthermore, based on the pattern description of each multivariate data document, I introduce a similarity measure for multivariate data. This allows scientists to quickly discover similar (or contradictory) data to their own measurements

Semantic Similarity of Spatial Scenes

The formalization of similarity in spatial information systems can unleash their functionality and contribute technology not only useful, but also desirable by broad groups of users. As a paradigm for information retrieval, similarity supersedes tedious querying techniques and unveils novel ways for user-system interaction by naturally supporting modalities such as speech and sketching. As a tool within the scope of a broader objective, it can facilitate such diverse tasks as data integration, landmark determination, and prediction making. This potential motivated the development of several similarity models within the geospatial and computer science communities. Despite the merit of these studies, their cognitive plausibility can be limited due to neglect of well-established psychological principles about properties and behaviors of similarity. Moreover, such approaches are typically guided by experience, intuition, and observation, thereby often relying on more narrow perspectives or restrictive assumptions that produce inflexible and incompatible measures. This thesis consolidates such fragmentary efforts and integrates them along with novel formalisms into a scalable, comprehensive, and cognitively-sensitive framework for similarity queries in spatial information systems. Three conceptually different similarity queries at the levels of attributes, objects, and scenes are distinguished. An analysis of the relationship between similarity and change provides a unifying basis for the approach and a theoretical foundation for measures satisfying important similarity properties such as asymmetry and context dependence. The classification of attributes into categories with common structural and cognitive characteristics drives the implementation of a small core of generic functions, able to perform any type of attribute value assessment. Appropriate techniques combine such atomic assessments to compute similarities at the object level and to handle more complex inquiries with multiple constraints. These techniques, along with a solid graph-theoretical methodology adapted to the particularities of the geospatial domain, provide the foundation for reasoning about scene similarity queries. Provisions are made so that all methods comply with major psychological findings about people’s perceptions of similarity. An experimental evaluation supplies the main result of this thesis, which separates psychological findings with a major impact on the results from those that can be safely incorporated into the framework through computationally simpler alternatives

Music information retrieval: conceptuel framework, annotation and user behaviour

Understanding music is a process both based on and influenced by the knowledge and experience of the listener. Although content-based music retrieval has been given increasing attention in recent years, much of the research still focuses on bottom-up retrieval techniques. In order to make a music information retrieval system appealing and useful to the user, more effort should be spent on constructing systems that both operate directly on the encoding of the physical energy of music and are flexible with respect to users’ experiences. This thesis is based on a user-centred approach, taking into account the mutual relationship between music as an acoustic phenomenon and as an expressive phenomenon. The issues it addresses are: the lack of a conceptual framework, the shortage of annotated musical audio databases, the lack of understanding of the behaviour of system users and shortage of user-dependent knowledge with respect to high-level features of music. In the theoretical part of this thesis, a conceptual framework for content-based music information retrieval is defined. The proposed conceptual framework - the first of its kind - is conceived as a coordinating structure between the automatic description of low-level music content, and the description of high-level content by the system users. A general framework for the manual annotation of musical audio is outlined as well. A new methodology for the manual annotation of musical audio is introduced and tested in case studies. The results from these studies show that manually annotated music files can be of great help in the development of accurate analysis tools for music information retrieval. Empirical investigation is the foundation on which the aforementioned theoretical framework is built. Two elaborate studies involving different experimental issues are presented. In the first study, elements of signification related to spontaneous user behaviour are clarified. In the second study, a global profile of music information retrieval system users is given and their description of high-level content is discussed. This study has uncovered relationships between the users’ demographical background and their perception of expressive and structural features of music. Such a multi-level approach is exceptional as it included a large sample of the population of real users of interactive music systems. Tests have shown that the findings of this study are representative of the targeted population. Finally, the multi-purpose material provided by the theoretical background and the results from empirical investigations are put into practice in three music information retrieval applications: a prototype of a user interface based on a taxonomy, an annotated database of experimental findings and a prototype semantic user recommender system. Results are presented and discussed for all methods used. They show that, if reliably generated, the use of knowledge on users can significantly improve the quality of music content analysis. This thesis demonstrates that an informed knowledge of human approaches to music information retrieval provides valuable insights, which may be of particular assistance in the development of user-friendly, content-based access to digital music collections