The ability of a normal human listener to recognize objects in the environment from only the sounds they produce is extraordinarily robust with regard to characteristics of the acoustic environment and of other competing sound sources. In contrast, computer systems designed to recognize sound sources function precariously, breaking down whenever the target sound is degraded by reverberation, noise, or competing sounds. Robust listening requires extensive contextual knowledge, but the potential contribution of sound-source recognition to the process of auditory scene analysis has largely been neglected by researchers building computational models of the scene analysis process. This thesis proposes a theory of sound-source recognition, casting recognition as a process of gathering information to enable the listener to make inferences about

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.

A new method for estimating the fundamental frequencies of concurrent musical sounds is described. The method is based on an iterative approach, where the fundamental frequency of the most prominent sound is estimated, the sound is subtracted from the mixture, and the process is repeated for the residual signal. For the estimation stage, an algorithm is proposed which utilizes the frequency relationships of simultaneous spectral components, without assuming ideal harmonicity. For the subtraction stage, the spectral smoothness principle is proposed as an efficient new mechanism in estimating the spectral envelopes of detected sounds. With these techniques, multiple fundamental frequency estimation can be performed quite accurately in a single time frame, without the use of long-term temporal features. The experimental data comprised recorded samples of 30 musical instruments from four different sources. Multiple fundamental frequency estimation was performed for random sound source and pitch combinations. Error rates for mixtures ranging from one to six simultaneous sounds were 1.8%, 3.9%, 6.3%, 9.9%, 14%, and 18%, respectively. In musical interval and chord identification tasks, the algorithm outperformed the average of ten trained musicians. The method works robustly in noise, and is able to handle sounds that exhibit inharmonicities. The inharmonicity factor and spectral envelope of each sound is estimated along with the fundamental frequency.

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It is only very recently that systems have been developed that transcribe polyphonic music with more than two voices in even limited generality. Two of these systems [Kashino et al.1995, Martin 1996] have been built within a blackboard framework, integrating front ends based on sinusoidal analysis with musical knowledge. These and other systems to date rely on instrument models for detecting octaves. Recent results have shown that an autocorrelation-based front end may make bottom-up detection of octaves possible, thereby improving system performance as well as reducing the distance between transcription models and human audition. This report outlines the blackboard approach to automatic transcription and presents a new system based on the log-lag correlogram of [Ellis 1996]. Preliminary results are presented, outlining the bottom-up detection of octaves and transcription of simple polyphonic music.

Signal processing methods for the automatic transcription of music are developed in this thesis. Music transcription is here understood as the process of analyzing a music signal so as to write down the parameters of the sounds that occur in it. The applied notation can be the traditional musical notation or any symbolic representation which gives sufficient information for performing the piece using the available musical instruments. Recovering the musical notation automatically for a given acoustic signal allows musicians to reproduce and modify the original performance. Another principal application is structured audio coding: a MIDI-like representation is extremely compact yet retains the identifiability and characteristics of a piece of music to an important degree. The scope of this thesis is in the automatic transcription of the harmonic and melodic parts of real-world music signals. Detecting or labeling the sounds of percussive instruments (drums) is not attempted, although the presence of these is allowed in the target signals. Algorithms are proposed that address two distinct subproblems of music transcription. The main part of the thesis is dedicated to multiple fundamental frequency (F0) estimation, that is, estimation of the F0s of several concurrent musical sounds. The other subproblem addressed is musical meter estimation. This has to do with rhythmic aspects of music and refers to the estimation of the regular pattern of strong and weak beats in a piece of music. For multiple-F0 estimation, two different algorithms are proposed. Both methods are based on an iterative approach, where the F0 of the most prominent sound is estimated, the sound is cancelled from the mixture, and the process is repeated for the residual. The first method is derived in a prag...

Abstract not found

A system for the automatic transcription of music is described. Signal processing methods are introduced that solve different facets of the overall problem. Main emphasis is laid on finding the multiple pitches of concurrent musical sounds. Sound onset detection and musical meter estimation are described to some extent. Other topics discussed are noise robustness, estimation of the number of concurrent voices, sound separation, and musical instrument recognition. The presented system is evaluated using a database of musical sounds, synthesized MIDI-songs, and CDrecordings. Also, the performance of the system is compared to that of human listeners. 1.