... describe music. They are commonly used to structure the increasing amounts of music available in digital form on the Web and are important for music information retrieval. Genre categorization for audio has traditionally been performed manually. A particular musical genre is characterized by statistical properties related to the instrumentation, rhythmic structure and form of its members. In this work, algorithms for the automatic genre categorization of audio signals are described. More specifically, we propose a set of features for representing texture and instrumentation. In addition a novel set of features for representing rhythmic structure and strength is proposed. The performance of those feature sets has been evaluated by training statistical pattern recognition classifiers using real world audio collections. Based on the automatic hierarchical genre classification two graphical user interfaces for browsing and interacting with large audio collections have been developed.

We describe a computer program which is able to estimate the tempo and the times of musical beats in expressively performed music. The input data may be either digital audio or a symbolic representation of music such as MIDI. The data is processed off-line to detect the salient rhythmic events and the timing of these events is analysed to generate hypotheses of the tempo at various metrical levels. Based on these tempo hypotheses, a multiple hypothesis search nds the sequence of beat times which has the best fit to the rhythmic events. We show that estimating the perceptual salience of rhythmic events significantly improves the results. No prior knowledge of the tempo, meter or musical style is assumed; all required information is derived from the data. Results are presented for a range of different musical styles, including classical, jazz, and popular works with a variety of tempi and meters. The system calculates the tempo correctly in most cases, the most common error being a doubling or halving of the tempo. The calculation of beat times is also robust. When errors are made concerning the phase of the beat, the system recovers quickly to resume correct beat tracking, despite the fact that there is no high level musical knowledge encoded in the system.

Existing audio tools handle the increasing amount of computer audio data inadequately. The typical tape-recorder paradigm for audio interfaces is inflexible and time consuming, especially for large data sets. On the other hand, completely automatic audio analysis and annotation is impossible using current techniques.

The availability of large music collections calls for ways to efficiently access and explore them. We present a new approach which combines descriptors derived from audio analysis with 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 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 meta-information-based view and two views generated from audio analysis, namely, beat periodicity as an aspect of rhythm and spectral information as an aspect of timbre.

We formulate tempo tracking in a Bayesian framework where a tempo tracker is modeled as a stochastic dynamical system. The tempo is modeled as a hidden state variable of the system and is estimated by a Kalman filter. The Kalman filter operates on a Tempogram, a wavelet-like multiscale expansion of a real performance. An important advantage of our approach is that it is possible to formulate both off-line or real-time algorithms. The simulation results on a systematically collected set of MIDI piano performances of Yesterday and Michelle by the Beatles shows accurate tracking of approximately %90 of the beats.

Content-based music genre classification is a fundamental component of music information retrieval systems and has been gaining importance and enjoying a growing amount of attention with the emergence of digital music on the Internet. Currently little work has been done on automatic music genre classification, and in addition, the reported classification accuracies are relatively low. This paper proposes a new feature extraction method for music genre classification, DWCHs 1. DWCHs capture the local and global information of music signals simultaneously by computing histograms on their Daubechies wavelet coefficients. Effectiveness of this new feature and of previously studied features are compared using various machine learning classification algorithms, including Support Vector Machines and Linear Discriminant Analysis. It is demonstrated that the use of DWCHs significantly improves the accuracy of music genre classification.

Ametho is decribed which analyzes the basic patterno beats in a pieceo music, the musical meter. The analysis isperfoVRm jofoV at three different time scales: at the atopo tatum pulse level, at the tactus pulse level which com{CfixVm8 to thetempo o a piece, and at the musicalme0LN level.Aco9@9R signalsfro arbitrary musical genres arecojRV}}m8} Fo r the initial timefrequency analysis, a new technique ispro}Rx} which measures the degreeo musical accent as a functio o time atfo@ different frequency ranges. This isfoj{ wed by a banko cok filterreso}R@}R which extracts featuresfo estimating theperioj and phaseso the three pulses. The features arepro} essed by a proC}m8jfifi@fi moo which represents primitive musicalkno wledge and uses thelo w-level om@{j atio{ to perfoC jofo estimatio o the tatum, tactus, and measure pulses. Themom} takesinto accoj thetempojR dependencies between successive estimates and enablesbob causal and nom causal analysis. Themetho is validated using a manually annollym databaseo 474 music signals fro varioC genres. Themetho wo{j ro ustlyfo different typeso music andimpro veso ver two state-o8j9}@fimooofimo9@Cm9@VmoRmo Inde x TeFFD Aco9fim8{R@@fimooofimo9@Cm9@VmoRmo EDICS: 2-MUSI ToappeC in IEEE Trans. Spe0 h and Audio ProceLCY1 . 2004 IEEE. Pe rsonaluse of thismatefifiF ispeRfifiV0(V Howe ve ,peNfi10(VY to reNYNYY0 eNYNYY0 this mate0Dfi foradve1CC0(L or promotionalpurpose or for cre0YYR ne wcolle0(LC works for reNLR or r eR1fiL0( ution toseFNN s or lists, or to refiD anycopyrighte componeh of this work inothe works mustbe obtaine fromthe IEEE. I.

We introduce the beat spectrum, a new method of automatically characterizing the rhythm and tempo of music and audio. The beat spectrum is a measure of acoustic self-similarity as a function of time lag. Highly structured or repetitive music will have strong beat spectrum peaks at the repetition times. This reveals both tempo and the relative strength of particular beats, and therefore can distinguish between different kinds of rhythms at the same tempo. We also introduce the beat spectrogram which graphically illustrates rhythm variation over time. Unlike previous approaches to tempo analysis, the beat spectrum does not depend on particular attributes such as energy or frequency, and thus will work for any music or audio in any genre. We present tempo estimation results which are accurate to within 1 % for a variety of musical genres. This approach has a variety of applications, including music retrieval by similarity and automatically generating music videos. 1.

We present a method for characterizing both the rhythm and tempo of music. We also present ways to quantitatively measure the rhythmic similarity between two or more works of music. This allows rhythmically similar works to be retrieved from a large collection. A related application is to sequence music by rhythmic similarity, thus providing an automatic “disc jockey ” function for musical libraries. Besides specific analysis and retrieval methods, we present small-scale experiments that demonstrate ranking and retrieving musical audio by rhythmic similarity. stream start start i i

Indexing and content-based retrieval are necessary to handle the large amounts of audio and multimedia data that is becoming available on the web and elsewhere. Since manual indexing using existing audio editors is extremely time consuming a number of automatic content analysis systems have been proposed. Most of these systems rely on speech recognition techniques to create text indices. On the other hand, very few systems have been proposed for automatic indexing of music and general audio. Typically these systems rely on classification and similarity-retrieval techniques and work in restricted audio domains. A somewhat