Music Retrieval Research Articles

Automatic music classification and summarization

Automatic music classification and summarization are very useful to music indexing, content-based music retrieval and on-line music distribution, but it is a challenge to extract the most common and salient themes from unstructured raw music data. In this paper, we propose effective algorithms to automatically classify and summarize music content. Support vector machines are applied to classify music into pure music and vocal music by learning from training data. For pure music and vocal music, a number of features are extracted to characterize the music content, respectively. Based on calculated features, a clustering algorithm is applied to structure the music content. Finally, a music summary is created based on the clustering results and domain knowledge related to pure and vocal music. Support vector machine learning shows a better performance in music classification than traditional Euclidean distance methods and hidden Markov model methods. Listening tests are conducted to evaluate the quality of summarization. The experiments on different genres of pure and vocal music illustrate the results of summarization are significant and effective.

MRTB framework: a robust content-based music retrieval and browsing

We propose new content-based music retrieval and browsing system called MRTB. New feature extraction procedures such as multi-feature clustering (MFC) and sequential forward selection (SFS) are proposed for robust and optimum performance of the music retrieval system. Using MFC and SFS, we can achieve 86% of average retrieving success rate and 0.5 /spl sim/ 0.8 sec retrieval time per each 20 s query based on top 10 ranked list rules. The user friendly graphical interface of MRTB is implemented in a thumbnail manner so the user can easily verify their retrieving results with some useful musical information.

若年層向け音楽感性検索システムに使用するための感性語選択

We aim to select several pairs of kansei words for use in a kansei-based music retrieval system. In order to evaluate kansei for music, we collected 530 kansei words from music magazines and a related work. Forty pairs of kansei words were selected out of them according to the results of 2 questionnaires. Using the those pairs, we conducted an evaluation test in which 76 subjects listened to 12 pieces of instrumental music through stereo speakers, and they rated the degree of their impression on each piece based on the 7-level semantic differential method. Factor analysis revealed that the psychological space of impressions was composed of 8 factors: There were uplift, mosso and so on. Next, we conducted simulation of retrieving. It was shown that many subjects often used 8 pair of kansei words for system input. Finally, we selected 19 pairs of kansei words, which were important to build a kansei-based music retrieval system

Catalog User Search Strategies in Finding Music Materials

Studying how catalog users search for sound recordings and scores can provide insight into cataloging procedures, OPAC design and reference services. There has been a significant number of studies on general book searching, but little research on how users find music materials. This article examines the access points catalog users choose in finding sound recordings and scores, and how difficulties in music searching influence search strategies. User studies of music-specific material are reviewed and compared with user studies of general book searching. A non-user study is discussed to shed light on the relationship between cataloging practices and user search strategies. Some of the research into new music retrieval methods is considered.

Bit-parallel [formula omitted]-matching and suffix automata

( δ , γ ) -matching is a string matching problem with applications to music retrieval. The goal is, given a pattern P 1 … m and a text T 1 … n on an alphabet of integers, find the occurrences P ′ of the pattern in the text such that (i) ∀ 1 ⩽ i ⩽ m , | P i − P i ′ | ⩽ δ , and (ii) ∑ 1 ⩽ i ⩽ m | P i − P i ′ | ⩽ γ . The problem makes sense for δ ⩽ γ ⩽ δ m . Several techniques for ( δ , γ ) -matching have been proposed, based on bit-parallelism or on skipping characters. We first present an O ( m n log ( γ ) / w ) worst-case time and O ( n ) average-case time bit-parallel algorithm (being w the number of bits in the computer word). It improves the previous O ( m n log ( δ m ) / w ) worst-case time algorithm of the same type. Second, we combine our bit-parallel algorithm with suffix automata to obtain the first algorithm that skips characters using both δ and γ. This algorithm examines less characters than any previous approach, as the others do just δ-matching and check the γ-condition on the candidates. We implemented our algorithms and drew experimental results on real music, showing that our algorithms are superior to current alternatives with high values of δ.

Tree-based versus distance-based key recognition in musical audio

A tree-based method for the recognition of the tonal center or key in a musical audio signal is presented. Time-varying key feature vectors of 264 synthesized sounds are extracted from an auditory-based pitch model and converted into character strings using PCA-analysis and classification trees. The results are compared with distance-based methods. The characteristics of the new tonality analysis tool are illustrated on various examples. The potential of this method as a building stone in a music retrieval system is discussed.

Research and developments of a multi‐modal MIR engine for commercial applications in East Asia1

AbstractThis article describes the research and development of an efficient Music Information Retrieval (MIR) engine that is embedded in a karaoke software package targeted for Asian people's need of music retrieval. The MIR engine has a multi‐modal interface that allows queries by singing, humming, tapping, speaking, and writing. In particular, we discuss the design philosophy, technical barriers, and performance evaluation of such an engine, as well as its current and potential commercial applications. Feedbacks and feature requests from users, which greatly influence our future work, are also addressed.

Popular music access: The Sony music browser

AbstractThe intentionally ambiguous expression “Popular Music Browser” reflects the two main goals of this project, which started in 1998, at Sony Computer Science Laboratories. First, we are interested in human‐centered issues related to browsing “Popular Music.” Popular here means that the music accessed is distributed widely and known to many listeners. Second, we consider “popular browsing” of music, i.e., making music accessible to nonspecialists (music lovers) and allowing sharing of musical tastes and information within communities, departing from the usual, single‐user view of digital libraries. This research project covers all areas of the music‐to‐listener chain, from music description, descriptor extraction from the music signal, or data mining techniques, similarity‐based access, and novel music retrieval methods such as automatic sequence generation, and to user interface issues. This article describes the scientific and technical issues at stake and the results obtained, and is illustrated by prototypes developed within the European IST project Cuidado.

Overview of the OMRAS project: Online music retrieval and searching

AbstractUntil recently, most research on music information retrieval concentrated on monophonic music. Online Music Retrieval and Searching (OMRAS) is a three‐year project funded under the auspices of the JISC (Joint Information Systems Committee)/NSF (National Science Foundation) International Digital Library Initiative which began in 1999 and whose remit was to investigate the issues surrounding polyphonic music information retrieval. Here we outline the work OMRAS has achieved in pattern matching, document retrieval, and audio transcription, as well as some prototype work in how to implement these techniques into library systems.

Learning to Cope With Diversity in Music Retrieval

The approach presented here makes productive use of the multidimensionality of music retrieval. It exploits heterogeneous representations of music objects into a self-adapting retrieval system. The different perspectives of users can be expressed by relevance feedback and serve as the direction for a learning process which ultimately leads to an optimal solution for a user within a certain context. The paper explores the diversity within music retrieval stemming from an abundance of approaches for representing musical objects as well as methods for searching for similarity. As a result, the system designer is usually confronted with a large number of arbitrary decisions. These challenges are discussed within the M-MIMOR framework, which provides an appropriate solution. A fusion with linear combination guarantees that every perspective is integrated. The weight and therefore the strength of one perspective is reflected by the weight of the representation scheme or matching algorithm into the fusion. These weights are adapted according to their success in previous retrieval tasks.

Polyphonic Score Retrieval Using Polyphonic Audio Queries: A Harmonic Modeling Approach

This paper extends the familiar “query by humming” music retrieval framework into the polyphonic realm. As humming in multiple voices is quite difficult, the task is more accurately described as “query by audio example,” onto a collection of scores. To our knowledge, we are the first to use polyphonic audio queries to retrieve from polyphonic symbolic collections. Furthermore, as our results will show, we will not only use an audio query to retrieve a known item symbolic piece, but we will use it to retrieve an entire set of real-world composed variations on that piece, also in the symbolic format. The harmonic modeling approach which forms the basis of this work is a new and valuable technique which has both wide applicability and future potential.

Issues in content-based music information retrieval

Representing a significant shift from methods that employ bibliographic metadata for music retrieval, modern Music Information Retrieval (MIR) research seeks to utilize characteristics of music to search databases of musical content via musically expressed queries. These investigations hold significant promise for the future development of commercial and research applications such as searchable databases of popular tunes on the web and databases of historical music. However the challenges are diverse, encompassing music cognition, storage and processing, and traditional information retrieval (IR) issues. This paper focuses on IR issues in MIR, discussing various approaches to music representation and matching techniques. Prototype MIR systems Meldex, Semex, Themefinder and Arthur are briefly described.

Issues in content-based music information retrieval

Representing a significant shift from methods that employ bibliographic metadata for music retrieval, modern Music Information Retrieval (MIR) research seeks to utilize characteristics of music to search databases of musical content via musically expressed queries. These investigations hold significant promise for the future development of commercial and research applications such as searchable databases of popular tunes on the web and databases of historical music. However the challenges are diverse, encompassing music cognition, storage and processing, and traditional information retrieval (IR) issues. This paper focuses on IR issues in MIR, discussing various approaches to music representation and matching techniques. Prototype MIR systems Meldex, Semex, Themefinder and Arthur are briefly described.

Tune Retrieval in the Multimedia Library

Musical scores are traditionally retrieved by title, composer or subject classification. Just as multimedia computer systems increase the range of opportunities available for presenting musical information, so they also offer new ways of posing musically-oriented queries. This paper shows how scores can be retrieved from a database on the basis of a few notes sung or hummed into a microphone. The design of such a facility raises several interesting issues pertaining to music retrieval. We first describe an interface that transcribes acoustic input into standard music notation. We then analyze string matching requirements for ranked retrieval of music and present the results of an experiment which tests how accurately people sing well known melodies. The performance of several string matching criteria are analyzed using two folk song databases. Finally, we describe a prototype system which has been developed for retrieval of tunes from acoustic input and evaluate its performance.

341ウェーブレット変換を用いた楽曲の特徴抽出に関する一検討

The aim of this study is creating a music retrieval system whose database employs “atmospheres” of music as its components. For the purpose, an atmosphere of music must be represented on computers. In the report symbols of atmospheres are represented by spectral patterns of music drawn on time -frequency planes. Because such information like harmonies of music, which have great influence on an atmosphere, is included only in raw sound data, not in MIDI nor other compressed data. The wave let transform is selected as a transform method because it is suitable for detecting fine differences of harmonies. The drawings of time-frequency planes indicate the possibility of showing atmospheres of music being represented by the method.

Redundant Hash Addressing of Feature Sequences Using the Self-Organizing Map

Kohonen‘s Self-Organizing Map (SOM) is combined with the Redundant Hash Addressing (RHA) principle. The SOM encodes the input feature vector sequence into the sequence of best-matching unit (BMU) indices and the RHA principle is then used to associate the BMU index sequence with the dictionary items. This provides a fast alternative for dynamic programming (DP) based methods for comparing and matching temporal sequences. Experiments include music retrieval and speech recognition. The separation of the classes can be improved by error-corrective learning. Comparisons to DP-based methods are presented.

Editorial: Tails Wagging Dogs

Editorial: Tails Wagging Dogs