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ISMIR 2002
3rd International Conference on
Music Information Retrieval

IRCAM – Centre Pompidou
Paris, France
October 13-17, 2002

This page now includes a link (through the icon) to the full text of the final version of the conference papers. These texts are © IRCAM – Centre Pompidou in this form. Authors have retained the rights to their original texts.

The printed version of the proceedings, which also contains introductions, the abstracts, a table of contents and the author index, can be ordered here.


The selected submissions in this category will be presented by their authors the main conference room in 30 minute long talks (with computer and audio output).

Similarity and Recognition

1.       Yongmoo E. Kim (MIT Media Lab) and Brian Whitman:
Singer Identification in Popular Music Recordings Using Voice Coding Features[Abstract1] 

2.       Daniel P.W. Ellis (Columbia University), Brian Whitman (MIT Media Lab), Adam Berenzweig (Columbia University) and Steve Lawrence (NEC Research Institute):
The Quest for Ground Truth in Musical Artist Similarity[Abstract2] 

3.       Jouni Paulus and Anssi Klapuri (Tampere University of Technology):
Measuring the Similarity of Rhythmic Patterns[Abstract3] 

4.       Jean-Julien Aucouturier and François Pachet (Sony Computer Science Lab., Paris):
Music Similarity Measures : What’s the use? [Abstract4] 


5.       Keiji Hirata (NTT Communications Science Laboratories) and Shu Matsuda (Digital Art Creation):
Interactive Music Summarization based on GTTM[Abstract5] 

6.       Geoffroy Peeters, Amaury La Burthe and Xavier Rodet (IRCAM):
Toward Automatic Music Audio Summary Generation from Signal Analysis[Abstract6] 

7.       Matthew Cooper and Jonathan Foote (FX Palo Alto Laboratory):
Automatic Music Summarization via Similarity Analysis[Abstract7] 

Indexation, classification, analysis

8.       Roger B. Dannenberg and Ning Hu (Carnegie Mellon University):
Pattern Discovery Techniques for Music Audio[Abstract8] 

9.       Cheng Yang (Stanford University):
MACSIS: A Scalable Acoustic Index for Content-Based Music Retrieval[Abstract9] 

10.    Andreas Rauber (Vienna University of Technology), Elias Pampalk (Austrian Research Institute for Artificial Intelligence) and Dieter Merkl (Vienna University of Technology):
Using Psycho-Acoustic Models and Self-Organizing Maps to Create a Hierarchical Structuring of Music by Musical Styles[Abstract10] 

11.    Brian Whitman and Paris Smaragdis (MIT Media Lab):
Combining Musical and Cultural Features for Intelligent Style Detection [Abstract11] 


12.    Alexandra Uitdenbogerd and Ron van Schyndel (RMIT University):
A Review of Factors Affecting Music Recommender Success [Abstract12] 

13.    Steffen Pauws (Philips Research Eindhoven) and Berry Eggen (Philips Research Eindhoven and Technische Universiteit Eindhoven):
PATS: Realization and user evaluation of an automatic playlist generator [Abstract13] 

14.    Joe Futrelle and J. Stephen Downie (University of Illinois):
Interdisciplinary Communities and Research Issues in Music Information Retrieval [Abstract14] 

15.    Ann Blandford and Hanna Stelmaszewska (University College London):
Usability of Musical Digital Libraries: a Multimodal Analysis [Abstract15] 

16.    Ja-Young Kim and Nicholas J. Belkin (Rutgers University):
Categories of Music Description and Search Terms and Phrases Used by Non-Music Experts [Abstract16] 

Query By Example

17.    Jaap Haitsma and Ton Kalker (Philips Research Eindhoven):
A Highly Robust Audio Fingerprinting System [Abstract17] 

18.     Jeremy Pickens (University of Massachusetts Amherst), Juan Pablo Bello (University of London), Tim Crawford (King’s College), Matthew Dovey (Oxford University), Giuliano Monti (University of London) and Mark Sandler (University of London):
Polyphonic Score Retrieval Using Polyphonic Audio Queries: A Harmonic Modeling Approach [Abstract18] 

19.     Jungmin Song, So-Young Bae and Kyoungro Yoon (LG Electronics):
Mid-Level Music Melody Representation of Polyphonic Audio for Query-by-Humming System [Abstract19] 

20.     Shyamala Doraisamy and Stefan M. Rüger (Imperial College London):
A Comparative and Fault-tolerance Study of the Use of N-grams with Polyphonic Music [Abstract20] 

21.     Colin Meek and William Birmingham (University of Michigan):
Johnny Can’t Sing: A Comprehensive Error Model for Sung Music Queries [Abstract21] 

22.    L. P. Clarisse, J. P. Martens, M. Lesaffre, B. De Baets, H. De Meyer and M. Leman (Ghent University) :
An Auditory Model Based Transcriber of Singing Sequences [Abstract22] 

Preprocessing: encoding, segmentation…

23.    Christopher Raphael (University of Massachusetts Amherst):
Automatic Transcription of Piano Music [Abstract23] 

24.     Jürgen Kilian (Darmstadt University of Technology) and Holger H. Hoos (University of British Columbia):
Voice Separation – A Local Optimization Approach [Abstract24] 

25.    Anna Pienimäki (University of Helsinki):
Indexing Music Databases Using Automatic Extraction of Frequent Phrases [Abstract25] 

26.     George Tzanetakis, Andrey Ermolinskiy and Perry Cook (Princeton University):
Pitch Histograms in Audio and Symbolic Music Information Retrieval [Abstract26] 

27.    Massimo Melucci and Nicola Orio (University of Padova):
A Comparison of Manual and Automatic Melody Segmentation [Abstract27] 

28.    Hui Jin and H. V. Jagadish (University of Michigan):
Indexing Hidden Markov Models for Music Retrieval [Abstract28] 


29.    Hugues Vinet (IRCAM), Perfecto Herrera (IA-UPF) and François Pachet (Sony CSL):
The CUIDADO Project [Abstract29] 

30.    Steffen Pauws (Philips Research Eindhoven):
CubyHum: a fully operational ‚“query by humming“ system [Abstract30] 

31.    Chaokun Wang, Jianzhong Li and Shengfei Shi (Harbin Institute of Technology):
A Kind of Content-Based Music Information Retrieval Method in a Peer-to-Peer Environment [Abstract31] 


The ISMIR 2002 Web pages will be regularly updated
to include program content and schedule


 [Abstract1]In most popular music, the vocals sung of the lead singer are the focal point of the song. The unique qualities of a singer’s voice make it relatively easy for us to identify a song as belonging to that particular artist. With little training, if one is familiar with a particular singer’s voice one can usually recognize that voice in other pieces, even when hearing a song for the first time. The research presented in this paper attempts to automatically establish the identity of a singer using acoustic features extracted from songs in a database of popular music. As a first step, an untrained algorithm for automatically extracting vocal segments from within songs is presented. Once these vocal segments are identified, they are presented to a singer identification system that has been trained on data taken from other songs by the same artists in the database.

 [Abstract2]It would be interesting and valuable to devise an automatic measure of the similarity between two musicians based only on an analysis of their recordings. To develop such a measure, however, presupposes some ‘ground truth’ training data describing the actual similarity between certain pairs of artists that constitute the desired output of the measure. Since artist similarity is wholly subjective, such data is not easily obtained. In this paper, we describe several attempts to construct a full matrix of similarity measures between a set of some 400 popular artists by regularizing limited subjective judgment data. We also detail our attempts to evaluate these measures by comparison with some direct subjective similarity judgments collected via a web-based survey in April 2002. Overall, we find that subjective artist similarities are not consistent between users, undermining the concept of a single ‘ground truth’, but we offer our best common-denominator measures anyway.

 [Abstract3]A system is described which measures the similarity of two arbitrary rhythmic patterns. The patterns are represented as acoustic signals, and are not assumed to have been performed with similar sound sets. Two novel methods are presented that constitute the algorithmic core of the system. First, a probabilistic musical meter estimation process is described, which segments a continuous musical signal into patterns. As a side-product, the method outputs tatum, tactus (beat), and measure lengths. A subsequent process performs the actual similarity measurements. Acoustic features are extracted which model the .uctuation of loudness and brightness withing the pattern, and dynamic time warping is then applied to align the patterns to be compared. In simulations, the system behaved consistently by assigning high similarity measures to similar musical rhythms, even when performed using different sound sets.

 [Abstract4]Electronic  Music  Distribution  (EMD)  is  in  demand  of  robust, automatically extracted music descriptors. We  introduce a timbral similarity  measures  for  comparing  music  titles.  This  measure  is based on a Gaussian model of cepstrum coefficients. We describe the  timbre  extractor  and  the  corresponding  timbral  similarity relation. We  describe  experiments  in  assessing  the  quality  of  the similarity  relation,  and  show  that  the  measure  is  able  to  yield interesting  similarity  relations,  in  particular  when  used  in conjunction with other similarity relations. We illustrate the use of the  descriptor  in  several  EMD  applications  developed  in  the context of the Cuidado European project.  

 [Abstract5]This paper presents a music summarization system called “Papipuun” that we are developing. Papipuun performs quick listening in a manner similar to a stylus skipping on a scratched record, but the skipping occurs correctly at punctuations of musical phrases, not arbitrarily. First, we developed a method for representing polyphony based on time-span reduction in the generative theory of tonal music (GTTM) and the deductive object-oriented database (DOOD). The operation, least upper bound, plays an important role in similarity checking of polyphonies represented in our method. Next, in a preprocessing phase, a user analyzes a set piece by the time-span reduction, using a dedicated tool, called TS-Editor. For a real time phase, the user interacts with the main system, Summarizer, to perform music summarization. Summarizer discovers a piece structure by similarity checking. When the user identifies the fragments to be skipped, Summarizer deletes them and concatenates the rest. Papipuun can produce the music summarization of good quality, reflecting the atmosphere of an entire piece through interaction with the user.

 [Abstract6]This paper deals with the automatic generation of music audio summaries from signal analysis without the use of any other information. The strategy employed here is to consider the audio signal as a succession of “states” (at various scales) corresponding to the structure (at various scales) of a piece of music. This is, of course, only applicable to certain kinds of musical genres bas ed on repetition.From the audio signal, we first derive features representing the time evolution of the energy content in various frequency bands. These features constitute our observations from which we derive a representation of the music in terms of “states”. Since human segmentation and grouping performs better upon subsequent hearings, this “natural” approach is followed here. The first pass of the proposed algorithm uses segmentation in order to create “templates” as “potential” states. The second pass uses these templates in order to structure the music using unsupervised learning methods (k-means and hidden Markov model). The audio summary is finally constructed by choosing a representative example of each state. Further refinements of the summary audio signal construction, uses overlap-add, and a tempo detection/ beat alignment in order to improve the audio quality of the created summary.

 [Abstract7]We present methods for automatically producing summary excerpts or thumbnails of music. To find the most representative excerpt, we maximize the average segment similarity to the entire work. After window-based audio parameterization, a quantitative similarity measure is calculated between every pair of windows, and the results are embedded in a 2-D similarity matrix. Summing the similarity matrix over the support of a segment results in a measure of how similar that segment is to the whole. This can be maximized to find the segment that best represents the entire work. We discuss variations on the method, and present experimental results for orchestral music, popular songs, and jazz. These results demonstrate that the method finds significantly representative excerpts, usingvery few assumptions about the source audio.

 [Abstract8]Human listeners are able to recognize structure in music through the perception of repetition and other relationships within a piece of music. This work aims to automate the task of music analysis. Music is “explained” in terms of embedded relationships, especially repetition of segments or phrases. The steps in this process are the transcription of audio into a representation with a similarity or distance metric, the search for similar segments, forming clusters of similar segments, and explaining music in terms of these clusters. Several transcription methods are considered: monophonic pitch estimation, chroma (spectral) representation, and polyphonic transcription followed by harmonic analysis. Also, several algorithms that search for similar segments are described. These techniques can be used to perform an analysis of musical structure, as illustrated byexamples.

 [Abstract9]We present an efficient and scalable system that indexes acoustic music data for content-based music retrieval. Both the music database and input queries are given in raw audio formats without metadata or other symbolic information; retrieval is targeted at music pieces which are “similar” to the query sound clip. Our framework is designed as a series of modular pipeline stages and phases. Each music file entering the pipeline is transformed into spectogram vectors and then into characteristic sequences, representing small segments of audio features than can tolerate some noise and tempo variations. These sequences are placed in a high-dimensional indexing structure. Retrieval results from the index are ranked based on alignment of short matching segments. Each module of the framework can be independently changed or replaced, and their effect are studied by experiments.

With the advent of large musical archives the need to provide an organization of these archives becomes eminent. While artist-based organizations or title indexes may help in locating a specific piece of music, a more intuitive, genre-based organization is required to allow users to browse an archive and explore its contents. Yet, currently these organizations following musical styles have to be designed manually. In this paper we propose an approach to automatically create a hierarchical organization of music archives following their perceived sound similarity. More specifically, characteristics of frequency spectra are extracted and transformed according to psycho-acoustic models. Subsequently, the Growing Hierarchical Self-Organizing Map, a popular unsupervised neural network, is used to reate a hierarchical organization, o®ering both an interface for interactive exploration as well as retrieval of music according to sound similarity.


 [Abstract11]In this paper we present a musical style identification scheme based on simultaneous classification of auditory and textual data. Style identification is a task which often involves cultural aspects not present or easily extracted through auditory processing. The scheme we propose complements any audio driven genre or style detection system with a classifier based on web-extracted data we call "community metadata." The addition of these cultural attributes in our feature space aids in proper classification of acoustically dissimilar music within the same style, and similar music belonging to different styles.

 [Abstract12]Much research has been published on musical taste, however, little has been studied by the builders of music recommenders. Implicit and explicit collaborative filtering has been used for making recommenders, in addition to the automatic classification of music into style categories based on extracted audio features. This paper surveys research into musical taste, reviews music recommender research, and outlines promising directions. In particular, we learned that demographic and personality factors have been shown to be factors influencing music preference. For mood, the main factors are tempo, tonality, distinctiveness of rhythm and pitch height.

 [Abstract13]A means to ease selecting preferred music referred to as Personalized Automatic Track Selection (PATS) has been developed. PATS generates playlists that suit a particular context-of-use, that is, the real-world environment in which the music is heard. To create playlists, it uses a dynamic clustering method in which songs are grouped based on their attribute similarity. The similarity measure selectively weighs attribute-values, as not all attribute-values for a context-of-use from preference feedback of the user. In a controlled user experiment, the quality of PATS-compiled and randomly assembled playlists for jazz music was assessed in two contexts-of-use. The quality of the randomly assembled playlists was used as base-line. The two contexts-of-use were "listening to soft music" and "listening to lively music". Playlist quality was measured by precision (songs that suit the context-of-use), coverage (songs that suit the context-of-use but that were not already contained in previous playlists) and a rating score. Results showed that PATS playlists contained increasingly more preferred music (increasingly higher precision), covered more preferred music in the collection (higher coverage), and were rated higher than randomly assembled playlists.

 [Abstract14]Music Information Retrieval (MIR) is an interdisciplinary research area that has grown out of need to manage burgeoning collections of music in digital form. Its diverse disciplinary communities have yet to articulate a common research agenda or agree on methodological principles and metrics of success. In order for MIR to succeed, researchers need to work with real user communities and develop research resources such as reference music collections, so that the wide variety of techniques being developed in MIR can be meaningfully compared with one another. Out of these efforts, a common MIR practice can emerge.

 [Abstract15]There has been substantial research on technical aspects of musical digital libraries, but comparatively little on usability aspects. We have evaluated four web-accessible music libraries, focusing particularly on features that are particular to music libraries, such as music retrieval mechanisms. Although the original focus of the work was on how modalities are combined within the interactions with such libraries, that was not where the main difficulties were found. Libraries were generally well designed for use of different modalities. The main challenges identified relate to the details of melody matching and to simplifying the choices of file format. These issues are discussed in detail.

 [Abstract16]Previous research has demonstrated that people listen to music for various reasons. The purpose of this study was to investigate people’s perception of music, and thus their music information needs. These ideas were examined by presenting 22 participants with 7 classical musical pieces, asking one-half of them to write words descriptive of each piece, and the other half words they would use if searching for each piece. All the words used by all subjects in both tasks were classified into 7 categories. The two most frequently appearing categories were emotions and occasions or filmed events regardless of the task type. These subjects, none of whom had formal training in music, almost never used words related to formal features of music, rather almost always using words indicating other features, most of which have not been considered in existing or proposed music IR systems. These results suggest that music IR research should be extended to consider needs other than finding known items, or items identified by formal characteristics, and that understanding music information needs of users should be prioritized to design more sophisticated music IR systems.

 [Abstract17]Imagine the following situation. You’re in your car, listening to the radio and suddenly you hear a song that catches your attention. It’s the best new song you have heard for a long time, but you missed the announcement and don’t recognize the artist. Still, you would like to know more about this music. What should you do? You could call the radio station, but that’s too cumbersome. Wouldn’t it be nice if you could push a few buttons on your mobile phone and a few seconds later the phone would respond with the name of the artist and the title of the music you’re listening to? Perhaps even sending an email to your default email address with some supplemental information. In this paper we present an audio fingerprinting system, which makes the above scenario possible. By using the fingerprint of an unknown audio clip as a query on a fingerprint database, which contains the fingerprints of a large library of songs, the audio clip can be identified. At the core of the presented system are a highly robust fingerprint extraction method and a very efficient fingerprint search strategy, which enables searching a large fingerprint database with only limited computing resources.

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 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 as both wide-applicability and long-range future potential. [Abstract18]

 [Abstract19]Recently a great attention is paid to content-based multimedia retrieval that enables users to find and locate audio-visual materials according to the intrinsic characteristics of the target. Query by humming (QBH) is also an application that makes retrieval according to major characteristics of music, that is, "melody". There are couples of researches on QBH system, but their major concern is the system that retrieves symbolic music data by humming query. But when the usability of technology is taken into consideration, retrieval of music in the form of polyphonic audio would be more useful and needed in the application such as internet music search or music juke box, where the music data is stored not in symbolic form but in raw audio signal because such music data is more natural format for consumption. Our focus is on the realization of query-byhumming technology to easy-to-use application, which entails full automation of all the processes of the system, including melody information extraction from polyphonic audio. Melody feature of music and humming is not represented by distinct note information but the possibilities of note occurrence. Similarity is then measured between the melody features of humming and music using DP matching method. This paper presents developed algorithms for key steps of QBH system including the melody feature extraction method from polyphonic audio and humming, their representation for matching, and matching method between represented melody information from polyphonic audio and humming.

 [Abstract20]Many of the large digital music collections available today are in polyphonic form. However, because of the complexities of music information retrieval (Music IR), much of the research in this area has focused on monophonic data. In this paper we investigate the retrieval performance of monophonic queries made on a polyphonic music database using the n-gram approach for full-music indexing. The pitch and rhythm dimensions of music are used and the ‘musical words’ generated enable text retrieval methods to be used with music retrieval. An experimental framework is outlined for a comparative and fault-tolerance study of various n-gramming strategies and encoding precision using six experimental databases. For monophonic queries we focus in particular on query-by-humming (QBH) systems. Error models addressed in several QBH studies are surveyed for the faulttolerance study. The experiments show that different n-gramming strategies and encoding precision differ widely in their effectiveness. We present the results of our comparative and fault-tolerance study on a collection of 6365 polyphonic music pieces encoded in the MIDI format.

 [Abstract21]We propose a model for errors in sung queries, a variant of the Hidden Markov Model (hmm). This is related to the problem of identifying the degree of similarity between a query and a potential target in a database of musical works, in the music retrieval framework. The model comprehensively expresses the types of error or variation between target and query: cumulative and non-cumulative local errors, transposition, tempo and tempo changes, insertions, deletions and modulation. Results of experiments demonstrating the robustness of such a model are presented.

In this paper, a new system for the automatic transcription of singing sequences into a sequence of pitch and duration pairs is presented. Although such a system may have a wider range of applications, it was mainly developed to become the acoustic module of a query-by-humming (QBH) system for retrieving pieces of music from a digitized musical library. The first part of the paper is devoted to the systematic evaluation of a variety of state-of-the art transcription systems. The main result of this evaluation is that there is clearly a need for more accurate systems. Especially the segmentation was experienced as being too error prone (≈ 20_ % segmentation errors). In the second part of the paper, a new auditory model based transcription system is proposed and evaluated. The results of that evaluation are very promising. Segmentation errors vary between 0 and 7 %, depending on the amount of lyrics that is used by the singer. Anyway, an error of less than 10 % is anticipated to be acceptable for QBH. The paper ends with the description of an experimental study that was issued to demonstrate that the accuracy of the newly proposed transcription system is not very sensitive to the choice of the free parameters, at least as long as they remain in the vicinity of the values one could forecast on the basis of their meaning.    

 [Abstract23]A hidden Markov model approach to piano music transcription is presented. The main difficulty in applying traditional HMM techniques is the large number of chord hypotheses that must be considered. We address this problem by using a trained likelihood model to generate reasonable hypotheses for each frame and construct the search graph out of these hypotheses. Results are presented using a recording of a movement from Mozart's Sonata 18, K. 570.

 [Abstract24]Voice separation, along with tempo-detection and quantization, is one of the basic problems of computer-based transcription of music. An adequate separation of notes into different voices is crucial for obtaining readable and usable scores from performances of polyphonic music recorded on keyboard (or other polyphonic) instruments; for improving quantisation results within a transcription system; and in the context of music retrieval systems that primarily support monophonic queries. In this paper we propose a new voice separation algorithm based on a stochastic local search method. Different from many previous approaches, our algorithm allows chors in the individual voices; its behaviour is controlled by a small number of intuitive and musically motivated parameters; and it is fast enough to allow interactive optimisation of the result by adjusting the parameters in real-time. We demonstrate that compared to existing approaches, our new algorithm generates better solutions for a number of typical voice separation problems. We also show how by changing its parameters it is possible to create score output for different needs (i.e. piano-style or orchestral scores).

 [Abstract25]The Music Information Retrieval methods can be classified into online and offline methods. The main drawback in most of the offline algorithms is the space the indexing structure requires. The amount of the data stored into the structure can however be reduced by storing only the suitable index terms or phrases instead of the whole contents of the database. Repetition is agreed to be one of the most important factors of musical meaningfulness. Therefore repetitive phrases are suitable for indexing purposes. The extraction of such phrases can be done by applying and existing text mining method to musical data. Because of the differences between text and musical data the application requires some technical modification of the method. This paper introduces a text mining-based music database indexing method that extracts maximal frequent phrases from musical data and sorts them by their length, frequency and personality. The implementation of the method found three different types of phrases from the test corpus consisting of Irish folk music tunes. The suitable two types of phrases out of three are easily recognized and separated from the set of all phrases to form an index data for the database.

 [Abstract26]In order to represent musical content, pitch and timing information is utilized in the majority of existing work in Symbolic Music Information Retrieval (MIR). Symbolic representations such as MIDI allow the easy calculation of such information and its manipulation. In contrast, most of the existing work in Audio MIR uses timbral and beat information, which can be calculated using automatic computer audition techniques. In this paper, Pitch Histograms are defined and proposed as a way to represent the pitch content of music signals both in symbolic and audio form. This representation is evaluated in the context of automatic musical genre classification. A multiple-pitch detection algorithm for polyphonic signals is used to calculate Pitch Histograms for audio signals. In order to evaluate the extent and significance of errors resulting from the automatic multiple-pitch detection, automatic musical genre classification results from symbolic and audio data are compared. The comparison indicates that Pitch Histograms provide valuable information for musical genre classification. The results obtained for both symbolic and audio cases indicate that although pitch errors degrade classification performance for the audio case, Pitch Histograms can be effectively used for classification in both cases.

 [Abstract27]The main contribution of this paper is an invistigation on the effects of exploiting melodic features for automatic melody segmentation aimed at content-based usicd retrieval. We argue that segmentation based on melodic features is more effective than random or n-grams-based segmentation, which ignore any context. We have carried out an experiment employing experienced subjects. The manual segmentation result has been processed to detect the most probably boundaries in the melodic surface, using a probabilistic decision function. The detected boundaries have then been compared with the boundaries detected by an automatic precedure implementing an algorithm for melody segmentation, as well as by a random segmenter and by a n-gram-based segmenter. Results showed that automatic segmentation based on melodic features is closer to manual segmentation that algorithms that do not use such information.

 [Abstract28]Hidden Markov Models (HMMs) have been suggested as an effective technique to represent music. Given a collection of musical pieces, each represented by its HMM, and a query , the retrieval task reduces to finding HMM most likely to have generated the query. The musical piece represented by this HMM is frequently the one rendered by the user, possibly imperfectly. This method might be inefficient if there is a very large music database, since each HMM to be tested requires the evaluation of a dynamic-programming algorithm. In this paper, we propose an indexing mechanism that can aggressively prune the set of condidiate HMMs to be evaluated in response to a query. Our experiments on a music database showed an anverage of a seven-fold spped up with no false dismissals.

 [Abstract29]The CUIDADO Project (Content-based Unified Interfaces and Descriptors for Audio/music Databases available Online) aims at developing a new chain of applications through the use of audio/music content descriptors, in the spirit of the MPEG-7 standard. The project includes the design of appropriate description structures, the development of extractors for deriving high-level information from audio signals, and the design and implementation of two applications: the Sound Palette and the Music Browser. These applications include new features which systematically exploit high-level descriptors and provide users with content-based access to large catalogues of audio/music material. The Sound Palette is focused on audio samples and targets professional users, whereas the Music Browser addresses a broader user target through the management of music titles. After a presentation of the project objectives and methodology, we describe the original features of the two applications made possible by the use of descriptors and the technical architecture framework on which they rely.

 [Abstract30]"Query by humming" is an interaction concept in which the identity of a song has to be revealed fast and orderly from a given sung input using a large database of known melodies. In short, it tries to detect the pitches in a sung melody and compares these pitches with symbolic representations of the known melodies. Melodies that are similar to the sung pitches are retreved. Approximate pattern matching in the melody comparison process compensates for the errors in the sung melody by using classical dynamic programming. A filtering method is use to save computation in the dynamic programming framework. This paper presents the algorithms for pitch detection, note onset detection, quantization, melody encoding and approximate pattern matching as they have been implemented in the Cubyllum software system. Since human reproduction of melodies is imperfect, findings from an experimental singing study were a crucial input to the development of the algorithms. Future research should pay special attention to the reliable detection of note onsets in any preferred singing style. in addition, research on index methods and fast bit-parallelism algorithms for approximate pattern matching needs to be further pursued to decrease computational requirements when dealing with large melody databases.

 [Abstract31]In this paper, we propose four peer-to-peer models for content-based music information retrieval (CBMIR) and carefully evaluate them on load, time, refreshment and robustness qualitatively and quantitatively. And we bring forward an algorithm to accelerate the retrieval speed of CBP2PMIR and a simple but effective method to filter the replica in the final results. And we present the architecture of content-based peer-to-peer music information retrieval system QUIND which can implement CBMIR. QUIND combines content-based music information retrieval technologies and peer-to-peer environment, and has good robustness and expansibility. Music stored and shared on each PC makes up of the whole available music resource. When user puts forward a music query, e.g. a song or a melody, QUIND can retrieve a lot of similar music quickly and accurately according to the content of query music. After user selects his favorite ones, he can download and enjoy them.