| The steep rise in music downloading over CD sales has created a major shift in the music industry away from physical media formats and towards online products and services. Music is one of the most popular types of online information and there are now hundreds of music streaming and download services operating on the World-Wide Web.Some of the music collections available are approaching the scale of ten million tracks and this has posed a major challenge for searching, retrieving, and organizing music content.Research efforts in music information retrieval have involved experts from music perception, cognition, musicology, engineering, and computer science engaged in truly interdisciplinary activity that has resulted in many proposed algorithmic and methodological solutions to music search using content-based methods. This paper outlines the problems of content-based music information retrieval and explores the state-of-the-art methods using audio cues (e.g., query by humming, audio fingerprinting, content-based music retrieval) and other cues (e.g., music notation and symbolic representation), and identifies some of the major challenges for the coming years.

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This paper describes a real-time beat tracking system that recognizes a hierarchical beat structure comprising the quarter-note, half-note, and measure levels in real-world audio signals sampled from popular-music compact discs. Most previous beat-tracking systems dealt with MIDI signals and had difficulty in processing, in real time, audio signals containing sounds of various instruments and in tracking beats above the quarter-note level. The system described here can process music with drums and music without drums and can recognize the hierarchical beat structure by using three kinds of musical knowledge: of onset times, of chord changes, and of drum patterns. This paper also describes several applications of beat tracking, such as beat-driven real-time computer graphics and lighting control. IntroductionThe goal of this study is to build a real-time system that can track musical beats in real-world audio signals, such as those sampled from compact discs. I think that building such a system that even in its preliminary implementation can work in real-world environments is an important initial step in the computational modeling of music understanding. This is because, as known from the scaling-up problem (Kitano, 1993) in the domain of artificial intelligence, it is hard to scale-up a system whose preliminary implementation works only in laboratory (toy-world) environments. This real-world oriented approach also facilitates the implementation of various practical applications in which music synchronization is necessary.Most previous beat-tracking related systems had difficulty working in real-world acoustic environments. Most of them (Dannenberg & Mont-Reynaud, 1987;Desain & Honing, 1989Allen & Dannenberg, 1990;Driesse, 1991; Rosenthal,1992aRosenthal, ,1992bRowe,1993;Large,1995)usedas their input MIDI-like representations, and their applications are limited because it is not easy to obtain complete MIDI representations from real-world audio signals. Some systems (Schloss, 1985;Katayose, Kato, Imai, & Inokuchi, 1989;Vercoe, 1994;Todd, 1994;Todd & Brown, 1996;Scheirer, 1998) dealt with audio signals, but they either did not consider the higher-level beat structureabovethequarter-notelevelor did not process popular music sampled from compact discs in real time. Although I developed two beat-tracking systems for real-world audio signals, one for music with drums (Goto & Muraoka, 1994, 1995, 1998 and the other for music without drums (Goto & Muraoka, 1996, 1999, they were separate systems and the former was not able to recognize the measure level.This paper describes a beat-tracking system that can deal with the audio signals of popular-music compact discs in real time regardless of whether or not those signals contain drum sounds. The system can recognize the hierarchical beat structure comprising the quarter-note level (almost regularly spaced beat times), the half-note level, and the measure level (bar-lines).1 This structure is shown in Figure 1. It assumes that the time-signature of an input song is 4/...

This paper presents a beat tracking system that processes acoustic signals of music and recognizes temporal positions of beats in real time. Musical beat tracking is needed by various multimedia applications such as video editing, audio editing, and stage lighting control. Previous systems were not able to deal with acoustic signals that contained sounds of various instruments, especially drums. They dealt with either MIDI signals or acoustic signals played on a few instruments, and in the latter case, did not work in real time. Our system deals with popular music in which drums maintain the beat. Because our system examines multiple hypotheses in parallel, it can follow beats without losing track of them, even if some hypotheses become wrong. Our system has been implemented on a parallel computer, the Fujitsu AP1000. In our experiment, the system correctly tracked beats in 27 out of 30 commercially distributed popular songs.

Abstract-This paper describes a method for obtaining a list of repeated chorus ("hook") sections in compact-disc recordings of popular music. The detection of chorus sections is essential for the computational modeling of music understanding and is useful in various applications, such as automatic chorus-preview/search functions in music listening stations, music browsers, or music retrieval systems. Most previous methods detected as a chorus a repeated section of a given length and had difficulty identifying both ends of a chorus section and dealing with modulations (key changes). By analyzing relationships between various repeated sections, our method, called RefraiD, can detect all the chorus sections in a song and estimate both ends of each section. It can also detect modulated chorus sections by introducing a perceptually motivated acoustic feature and a similarity that enable detection of a repeated chorus section even after modulation. Experimental results with a popular music database showed that this method correctly detected the chorus sections in 80 of 100 songs. This paper also describes an application of our method, a new music-playback interface for trial listening called SmartMusicKIOSK, which enables a listener to directly jump to and listen to the chorus section while viewing a graphical overview of the entire song structure. The results of implementing this application have demonstrated its usefulness.