Music is traditionally retrieved by title, composer or subject classification. It is possible, with current technology, to retrieve music from a database on the basis of a few notes sung or hummed into a microphone. This paper describes the implementation of such a system, and discusses several 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.

We have seen three important trends develop in the last five years in speech recognition. First, heterogeneous parameter sets that mix absolute spectral information with dynamic, or time-derivative, spectral information, have become common. Second, similariry transform techniques, often used to normalize and decor-relate parameters in some computationally inexpensive way, have become popular. Third, the signal parameter estimation problem has merged with the speech recognition process so that more sophisticated statistical models of the signal’s spectrum can be estimated in a closed-loop manner. In this paper, we review the signal processing components of these algorithms. These al-gorithms are presented as part of a unified view of the signal parameterization problem in which there are three major tasks: measurement, transformation, and statistical modeling. This paper is by no means a comprehensive survey of all possible techniques of signal modeling in speech recognition. There are far too many algorithms in use today to make an exhaustive survey feasible (and cohesive). Instead, this paper is meant to serve as a tutorial on signal processing in state-of-the-art speech recognition systems and to review those techniques most commonly used. In keeping with this goal, a complete mathematical description of each algorithm has been included in the paper.

A huge amount of audio data is accessible to everyone by on-line or off-line information services and it is necessary to develop techniques to automatically describe and deal with this data in a meaningful way. In the particular context of music content processing it is important to take into account the melodic aspects of the sound. The goal of this article is to review the different techniques proposed for melodic description and extraction. Some ideas around the concept of melody are first presented. Then, an overview of the different ways of describing melody is done. As a third step, an analysis of the methods proposed for melody extraction is made, including pitch detection algorithms. Finally, techniques for melodic pattern induction and matching are also studied, and some useful melodic transformations are reviewed. 1

A comparative evaluation of several pitch determination algorithms (PDAs) is presented. Fundamental frequency estimates, F0, are compared with laryngeal frequency estimates, Lx. An algorithm is presented which enables Lx contours to be generated from laryngograph data. We seek the most accurate method of F0 extraction in order to minimise errors propagating into subsequent prosodic analysis. The super resolution pitch determinator [3] performs well relative to the other PDAs studied. Modifications made to this algorithm are described, which radically reduce the number of gross F0 errors and improve the classification of voiced and unvoiced sections of speech. The raw F0 contours produced by this enhanced algorithm are processed to form schematised contours used in computer aided intonation teaching. The series of processes used in the schematisation is described. Keywords: Pitch tracking, Intonation, Language teaching 1 INTRODUCTION The fundamental frequency of speech plays an imp...

Several pitch detection algorithms are examined for use in interactive computer-music performance. We define criteria necessary for successful pitch tracking in real-time and survey

MT is a melody transcription system that accepts acoustic input, typically sung by the user, and displays it in standard music notation. It tracks the pitch of the input and segments the pitch stream into musical notes, which are labelled by their pitches relative to a reference frequency that adapts to the userÕs tuning. This paper describes the signal processing operations involved, and discusses two applications that have been prototyped: a sightsinging tutor and a scheme for acoustically indexing a melody database.

nd family deserve thanks for many things. Special thanks go to William Daly of Dumont High School, who encouraged my interest in computers and even hired me as a programmer. iii iv Contents 1 Introduction 1 1.1 Application-specic signal processors : : : : : : : : : : : : : : : : : : : : : : 1 1.2 Generating horizontal microcode : : : : : : : : : : : : : : : : : : : : : : : : 5 2 Target Architectures 11 2.1 The Kappa datapath : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 12 2.2 The boolean and control units : : : : : : : : : : : : : : : : : : : : : : : : : : 14 3 The RL Compiler 16 3.1 RL : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 16 3.2 The register-transfer notation : : : : : : : : : : : : : : : : : : : : : : : : : : 25 3.3 The machine description : : : : : : :

We have implemented a real time front end for detecting voiced speech and estimating its fundamental frequency. The front end performs the signal processing for voice-driven agents that attend to the pitch contours of human speech and provide continuous audiovisual feedback. The algorithm we use for pitch tracking has several distinguishing features: it makes no use of FFTs or autocorrelation at the pitch period; it updates the pitch incrementally on a sample-by-sample basis; it avoids peak picking and does not require interpolation in time or frequency to obtain high resolution estimates; and it works reliably over a four octave range, in real time, without the need for postprocessing to produce smooth contours.

We present a probabilistic error correction technique to be used with an average magnitude difference function (AMDF) based pitch detector. This error correction routine provides avery simple method to correct errors in pitch period estimation. Used in conjunction with the computationally efficient AMDF, the result is a fast and accurate pitch detector. In performance tests on the CSTR (Center for Speech Technology Research) database, probabilistic error correction reduced the gross error rate from 6.07% to 3.29%.

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