Towards efficient music genre classification using FastMap

Automatic genre classification aims to correctly categorize an unknown recording with a music genre. Recent studies use the Kullback-Leibler (KL) divergence to estimate music similarity then perform classification using k-nearest neighbours (k-NN). However, this approach is not practical for large databases. We propose an efficient genre classifier that addresses the scalability problem. It uses a combination of modified FastMap algorithm and KL divergence to return the nearest neighbours then use 1- NN for classification. Our experiments showed that high accuracies are obtained while performing classification in less than 1/20 second per track

Adaptive content mapping for internet navigation

The Internet as the biggest human library ever assembled keeps on growing. Although all kinds of information carriers (e.g. audio/video/hybrid file formats) are available, text based documents dominate. It is estimated that about 80% of all information worldwide stored electronically exists in (or can be converted into) text form. More and more, all kinds of documents are generated by means of a text processing system and are therefore available electronically. Nowadays, many printed journals are also published online and may even discontinue to appear in print form tomorrow. This development has many convincing advantages: the documents are both available faster (cf. prepress services) and cheaper, they can be searched more easily, the physical storage only needs a fraction of the space previously necessary and the medium will not age. For most people, fast and easy access is the most interesting feature of the new age; computer-aided search for specific documents or Web pages becomes the basic tool for information-oriented work. But this tool has problems. The current keyword based search machines available on the Internet are not really appropriate for such a task; either there are (way) too many documents matching the specified keywords are presented or none at all. The problem lies in the fact that it is often very difficult to choose appropriate terms describing the desired topic in the first place. This contribution discusses the current state-of-the-art techniques in content-based searching (along with common visualization/browsing approaches) and proposes a particular adaptive solution for intuitive Internet document navigation, which not only enables the user to provide full texts instead of manually selected keywords (if available), but also allows him/her to explore the whole database

A Content-Aware Interactive Explorer of Digital Music Collections: The Phonos Music Explorer

La tesi si propone di utilizzare le più recenti tecnologie del Music Information Retrieval (MIR) al fine di creare un esploratore interattivo di cataloghi musicali. Il software utilizza tecniche avanzate quali riduzione di dimensionalità  mediante FastMap, generazione e streaming over-the-network di contenuto audio, segmentazione e estrazione di descrittori da segnali audio. Inoltre, il software è in grado di adattare in real-time il proprio output sulla base di interazioni dell'utent

Visual mining in music collections with Emergent SOM

Different methods of organizing large collections of music with databionic mining techniques are described. The Emergent Self-Organizing Map is used to cluster and visualize similar artists and songs. The first method is the MusicMiner system that utilizes semantic descriptions learned from low level audio features for each song. The second method uses tags that have been assigned to music artists by the users of the social music platform Last.fm. For both methods we demonstrate the visualization capabilities of the U-Map. An intuitive browsing of large music collections is offered based on the paradigm of topographic maps. The semantic concepts behind the features enhance the interpretability of the maps

A novel interface for audio based sound data mining

In this paper, the design of a web interface for audio-based sound data mining is studied. The interface allows the user to explore a sound dataset without any written textual hint. Dataset sounds are grouped into semantic classes which are themselves clustered to build a semantic hierarchical struc-ture. Each class is represented by a circle distributed on a two dimensional space according to its semantic level. Sev-eral means of displaying sounds following this template are presented and evaluated with a crowdsourcing experiment

DelosDLMS: From the DELOS vision to the implementation of a future digital library management system

DelosDLMS is a novel digital library management system (DLMS) that has been developed as an integration effort within the DELOS Network of Excellence, a European Commission initiative funded under its fifth and sixth framework programs. In this paper, we describe DelosDLMS that takes into account the recommendations of several activities that were initiated by DELOS including the DELOS vision for digital libraries (DLs). A key aspect of DelosDLMS is its novel generic infrastructure that allows the generation of digital library systems out of a set of basic system services and DL services in a modular and extensible way. DL services like feature extraction, visualization, intelligent browsing, media-type-specific indexing, support for multilinguality, relevance feedback and many others can easily be incorporated or replaced. A further key aspect of DelosDLMS is its robustness against failures and its scalability for large collections and many parallel user requests. We discuss the current status of an effort to build DelosDLMS, a Digital Library Management System that integrates in various ways several components developed by DELOS members and showcases a great variety of functionality that is outlined as part of the DELOS visio

Exploring Music Collections by Browsing Different Views

The availability of large music collections calls for ways to efficiently access and explore them. We present a new approach which uses descriptors derived from audio analysis and meta-information to create different views of a collection. Such views can have a focus on timbre, rhythm, artist, style or other aspects of music. For each view the pieces of music are organized on a map in such a way that similar pieces are located close to each other. The maps are visualized using an Islands of Music metaphor where islands represent groups of similar pieces. The different maps are linked to each other using a new technique to align Self-Organizing Maps. The user is able to browse the collection and explore different aspects by gradually changing focus from one view to another. We demonstrate our approach on a small collection using a user defined view and two views generated from audio analysis, namely, beat periodicity as an aspect of rhythm and spectral information as an aspect of timbre

Data sensitive approximate query approaches in metric spaces

Ankara : The Department of Computer Engineering and the Graduate School of Engineering and Science of Bilkent University, 2011.Thesis (Master's) -- Bilkent University, 2011.Includes bibliographical references leaves 56-59.Similarity searching is the task of retrieval of relevant information from datasets. We are particularly interested in datasets that contain complex and unstructured data such as images, videos, audio recordings, protein and DNA sequences. The relevant information is typically defined using one of two common query types: a range query involves retrieval of all the objects within a specified distance to the query object; whereas a k-nearest neighbor query deals with obtaining k closest database objects to the query object. A variety of index structures based on the notion of metric spaces have been offered to process these two query types. The query performances of the proposed index structures have not been satisfactory particularly for high dimensional datasets. As a solution, various approximate similarity search methods offering the users a quality/time trade-off have been proposed. The rationale is that the users might be willing to tolerate query precision to retrieve query results relatively faster. The proposed approximate searching schemes usually have strong connections to the underlying data structures, making the comparison of the quality of the essence of their ideas difficult. In this thesis we investigate various approximation approaches to decrease the response time of similarity queries. These approaches use a variety of statistics about the dataset in order to obtain dynamic (at the time of querying) and specific guidance on the approximation for each query object individually. The experiments are performed on top of a simple underlying pivot-based index structure to minimize the effects of the index to our approximation schemes. The results show that it is possible to improve the performance/precision of the approximation based on data and query object sensitive guidance.Dilek, MerveM.S