ISMIR 2004 audio description contest (original) (raw)

The 2005 Music Information retrieval Evaluation Exchange (MIREX 2005): Preliminary Overview

2005

This paper is an extended abstract which provides a brief preliminary overview of the 2005 Music Information Retrieval Evaluation eXchange (MIREX 2005). The MIREX organizational framework and infrastructure are outlined. Summary data concerning the 10 evaluation contests is provided. Key issues affecting future MIR evaluations are identified and discussed. The paper concludes with a listing of targets items to be undertaken before MIREX 2006 to ensure the ongoing success of the MIREX framework.

New Developments in Music Information Retrieval

The digital revolution has brought about a massive increase in the availability and distribution of music-related documents of various modalities comprising textual, audio, as well as visual material. Therefore, the development of techniques and tools for organizing, structuring, retrieving, navigating, and presenting music-related data has become a major strand of research—the field is often referred to as Music Information Retrieval (MIR). Major challenges arise because of the richness and diversity of music in form and content leading to novel and exciting research problems. In this article, we give an overview of new developments in the MIR field with a focus on content-based music analysis tasks including audio retrieval, music synchronization, structure analysis, and performance analysis.

Content-Based Music Information Retrieval (CB-MIR) and Its Applications toward the Music Industry

ACM Computing Surveys, 2018

A huge increase in the number of digital music tracks has created the necessity to develop an automated tool to extract the useful information from these tracks. As this information has to be extracted from the contents of the music, it is known as content-based music information retrieval (CB-MIR). In the past two decades, several research outcomes have been observed in the area of CB-MIR. There is a need to consolidate and critically analyze these research findings to evolve future research directions. In this survey article, various tasks of CB-MIR and their applications are critically reviewed. In particular, the article focuses on eight MIR-related tasks such as vocal/non-vocal segmentation, artist identification, genre classification, raga identification, query-by-humming, emotion recognition, instrument recognition, and music clip annotation. The fundamental concepts of Indian classical music are detailed to attract future research on this topic. The article elaborates on the...

Music information analysis and retrieval techniques

Archives of Acoustics, 2008

This paper presents the main issues related to music information retrieval (MIR) domain. MIR is a multi-discipline area. Within this domain, there exists a variety of approaches to musical instrument recognition, musical phrase classification, melody classification (e.g. queryby-humming systems), rhythm retrieval, high-level-based music retrieval such as looking for emotions in music or differences in expressiveness, music search based on listeners' preferences, etc. The key-issue lies, however, in the parameterization of a musical event. In this paper some aspects related to MIR are shortly reviewed in the context of possible and current applications to this domain.

Music Information Retrieval: An Inspirational Guide to Transfer from Related Disciplines

Multimodal Music Processing (Schloss Dagstuhl, Germany, 2012), M. Müller and M. Goto, Eds., vol. Seminar, 2012

The emerging field of Music Information Retrieval (MIR) has been influenced by neighboring domains in signal processing and machine learning, including automatic speech recognition, image processing and text information retrieval. In this contribution, we start with concrete examples for methodology transfer between speech and music processing, oriented on the building blocks of pattern recognition: preprocessing, feature extraction, and classification/decoding. We then assume a higher level viewpoint when describing sources of mutual inspiration derived from text and image information retrieval. We conclude that dealing with the peculiarities of music in MIR research has contributed to advancing the state-of-the-art in other fields, and that many future challenges in MIR are strikingly similar to those that other research areas have been facing.

MUSART: Music retrieval via aural queries

Ann Arbor, 2001

MUSART is a research project developing and studying new techniques for music information retrieval. The MUSART architecture uses a variety of representations to support multiple search modes. Progress is reported on the use of Markov modeling, melodic contour, and phonetic streams for music retrieval. To enable large-scale databases and more advanced searches, musical abstraction is studied. The MME subsystem performs theme extraction, and two other analysis systems are described that discover structure in audio representations of music. Theme extraction and structure analysis promise to improve search quality and support better browsing and "audio thumbnailing." Integration of these components within a single architecture will enable scientific comparison of different techniques and, ultimately, their use in combination for improved performance and functionality.

An architecture for effective music information retrieval

Journal of the Association for Information Science and Technology, 2004

We have explored methods for music information retrieval for polyphonic music stored in the MIDI format. These methods use a query, expressed as a series of notes that are intended to represent a melody or theme, to identify similar pieces. Our work has shown that a three-phase architecture is appropriate for this task, in which the first phase is melody extraction, the second is standardisation, and the third is query-to-melody matching. We have investigated and systematically compared algorithms for each of these phases. To ensure that our results are robust, we have applied methodologies that are derived from text information retrieval: we developed test collections and compared different ways of acquiring test queries and relevance judgements. In this paper we review this program of work, compare to other approaches to music information retrieval, and identify outstanding issues.

Music Retrieval: A Tutorial and Review

The increasing availability of music in digital format needs to be matched by the development of tools for music accessing, filtering, classification, and retrieval. The research area of Music Information Retrieval (MIR) covers many of these aspects. The aim of this paper is to present an overview of this vast and new field. A number of issues, which are peculiar to the music language, are described-including forms, formats, and dimensions of music-together with the typologies of users and their information needs. To fulfil these needs a number of approaches are discussed, from direct search to information filtering and clustering of music documents. An overview of the techniques for music processing, which are commonly exploited in many approaches, is also presented. Evaluation and comparisons of the approaches on a common benchmark are other important issues. To this end, a description of the initial efforts and evaluation campaigns for MIR is provided.

Editorial: Music Information Retrieval

Journal of Intelligent Information Systems

Increasing availability of music data via Internet evokes demand for efficient search through music files. Users' interests include melody tracking, harmonic structure analysis, timbre identification, and so on. We visualize, in an illustrative example, why content based search is needed for music data and what difficulties must be overcame to build an intelligent music information retrieval system.

A matlab toolbox for music information retrieval

Data analysis, machine learning and …, 2007

We present MIRToolbox, an integrated set of functions written in Matlab, dedicated to the extraction from audio files of musical features related, among others, to timbre, tonality, rhythm or form. The objective is to offer a state of the art of computational approaches in the area of Music Information Retrieval (MIR). The design is based on a modular framework: the different algorithms are decomposed into stages, formalized using a minimal set of elementary mechanisms, and integrating different variants proposed by alternative approaches -including new strategies we have developed -, that users can select and parametrize. These functions can adapt to a large area of objects as input.

A Survey of Music Information Retrieval Systems

2005

This survey paper provides an overview of content-based music information retrieval systems, both for audio and for symbolic music notation. Matching algorithms and indexing methods are briefly presented. The need for a TREC-like comparison of matching algorithms such as MIREX at ISMIR becomes clear from the high number of quite different methods which so far only have been used on different data collections. We placed the systems on a map showing the tasks and users for which they are suitable, and we find that existing content-based retrieval systems fail to cover a gap between the very general and the very specific retrieval tasks.

A Large Publicly Accassible Prototype Audio Database for Music Research

This paper introduces Codaich, a large and diverse publicly accessible database of musical recordings for use in music information retrieval (MIR) research. The issues that must be dealt with when constructing such a database are dis- cussed, as are ways of addressing these problems. It is sug- gested that copyright restrictions may be overcome by al- lowing users to make customized feature extraction queries rather than allowing direct access to recordings themselves. The jMusicMetaManager software is introduced as a tool for improving metadata associated with recordings by auto- matically detecting inconsistencies and redundancies.

Evaluation in Music Information Retrieval

Journal of Intelligent Information Systems, 2013

The field of Music Information Retrieval has always acknowledged the need for rigorous scientific evaluations, and several efforts have set out to develop and provide the infrastructure, technology and methodologies needed to carry out these evaluations. The community has enormously gained from these evaluation forums, but we have reached a point where we are stuck with evaluation frameworks that do not allow us to improve as much and as well as we want. The community recently acknowledged this problem and showed interest in addressing it, though it is not clear what to do to improve the situation. We argue that a good place to start is again the Text IR field. Based on a formalization of the evaluation process, this paper presents a survey of past evaluation work in the context of Text IR, from the point of view of validity, reliability and efficiency of the experiments. We show the problems that our community currently has in terms of evaluation, point to several lines of research to improve it and make various proposals in that line.

Tools for Music Information Retrieval and Playing

Tools and Methodologies, 2008

State-of-the-art MIR issues are presented and discussed both from the symbolic and audio points of view. As for the symbolic aspects, different approaches are presented in order to provide an overview of the different available solutions for particular MIR tasks. This section ends with an overview of MX, the IEEE standard XML language specifically designed to support interchange between musical notation, performance, analysis, and retrieval applications. As for the audio level, first we focus on blind tasks like beat and tempo tracking, pitch tracking and automatic recognition of musical instruments. Then we present algorithms that work both on compressed and uncompressed data. We analyze the relationships between MIR and feature extraction presenting examples of possible applications. Finally we focus on automatic music synchronization and we introduce a new audio player that supports the MX logic layer and allows to play both score and audio coherently.