integrating long (2.4 second) segments of sound. 1. OVERVIEW The problem of distinguishing speech signals from music signals has become increasingly important as automatic speech recognition (ASR) systems are applied to more and more "real-world" multimedia domains. If we wish to build systems

Abstract—Monaural musical sound separation has been extensively studied recently. An important problem in separation of pitched musical sounds is the estimation of time–frequency regions where harmonics overlap. In this paper, we propose a sinusoidal modeling-based separation system that can

values, and the gains and the spectra are then alternatively updated using multiplicative update rules until the values converge. Simulation experiments were carried out using generated mixtures of pitched musical instrument samples and drum sounds. The performance of the proposed method was compared

We have created software that shows a musician the pitch of the notes he or she is playing or singing, in real time and very accurately. This is useful as a teaching aid for beginners and also for studying refinements of sound production such as vibrato.

This paper describes a musical instrument identification method that takes into consideration the pitch dependency of timbres of musical instruments. The difficulty in musical instrument identification resides in the pitch dependency of musical instrument sounds, that is, acoustic features of most

for speech analysis. This makes our method capable of measuring harmonics-to-noise ratios in the lag domain with an accuracy and reliability much greater than that of any of the usual frequency-domain methods. By definition, the best candidate for the acoustic pitch period of a sound can be found from

The emergence of audio and video data types in databases will require new information retrieval methods adapted to the specific characteristics and needs of these data types. An effective and natural way of querying a musical audio database is by humming the tune of a song. In this paper, a system

Rhythm and pitch are the 2 primary dimensions of music. They are interesting psychologically because simple, well-defined units combine to form highly complex and varied patterns. This article brings together the major developments in research on how these dimensions are perceived and remembered

say that it belongs to the class of speech sounds or the class of applause sounds, where the system has previously been trained on other sounds in this class. I Acoustical/perceptual features: describing the sounds in terms of commonly understood physical characteristics such as brightness, pitch

was tested on a corpus of simple sounds as well as a corpus of musical excerpts. The system is purely data-driven and does not depend on particular audio characteristics. Given a suitable parameterization, this method maythus be applicable to image retrieval as well.