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Audio Melody Extraction Based on Timbral Similarity of Melodic Fragments

by Matija Marolt - in Proceedings Eurocon 2005 , 2005
"... The extended abstract presents our approach to extraction of melody from audio recordings, based on timbral similarity of melodic fragments. The algorithm was submitted to MIREX 2005 competition and scored 5 th among 9 submissions, with an average score of 59.18% correctly transcribed voiced and unv ..."
Abstract - Cited by 3 (0 self) - Add to MetaCart
The extended abstract presents our approach to extraction of melody from audio recordings, based on timbral similarity of melodic fragments. The algorithm was submitted to MIREX 2005 competition and scored 5 th among 9 submissions, with an average score of 59.18% correctly transcribed voiced

Audio Melody Extraction Based on Timbral Similarity of Melodic Fragments

by unknown authors
"... Abstract — The presented study deals with extraction of melodic line(s) from polyphonic audio recordings. Our approach is based on finding significant melodic fragments throughout the analyzed piece of music and clustering these fragments according to their timbral similarity. Fragments within clust ..."
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Abstract — The presented study deals with extraction of melodic line(s) from polyphonic audio recordings. Our approach is based on finding significant melodic fragments throughout the analyzed piece of music and clustering these fragments according to their timbral similarity. Fragments within

On Finding Melodic Lines in Audio Recordings

by Matija Marolt , 2004
"... The paper presents our approach to the problem of finding melodic line(s) in polyphonic audio recordings. The approach is composed of two different stages, partially rooted in psychoacoustic theories of music perception: the first stage is dedicated to finding regions with strong and stable pitch (m ..."
Abstract - Cited by 17 (1 self) - Add to MetaCart
(melodic fragments), while in the second stage, these fragments are grouped according to their properties (pitch, loudness...) into clusters which represent melodic lines of the piece. Expectation Maximization algorithm is used in both stages to find the dominant pitch in a region, and to train Gaussian

Learning Melodic Expectancy: Musical Predictability in an SRN

by unknown authors
"... Descriptive models of music cognition propose various principles as underlying melodic expectancy, however there is very little discussion regarding the processes involved in the acquisition of melodic expectation. To explore the potential role of learning processes, a simple recurrent network (SRN) ..."
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Descriptive models of music cognition propose various principles as underlying melodic expectancy, however there is very little discussion regarding the processes involved in the acquisition of melodic expectation. To explore the potential role of learning processes, a simple recurrent network (SRN

Learning Melodic Expectancy: Musical Predictability in an SRN

by Brandon Abbs , Prahlad Gupta
"... Abstract Descriptive models of music cognition propose various principles as underlying melodic expectancy, with some models proposing that these are innate. A simple recurrent network (SRN) was trained on a set of musical sequences to examine the degree to which the principles described by Schelle ..."
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by Schellenberg's (1997) two-factor model might be learned through musical exposure. The principle of pitch proximity, but not pitch reversal constrained the model's expectations of tones following melodic fragments. Implications for this model in the area of music perception and sequential modeling

AN EVALUATION OF METHODOLOGIES FOR MELODIC SIMILARITY IN AUDIO RECORDINGS OF INDIAN ART MUSIC

by Sankalp Gulati , Joan Serrà , Xavier Serra
"... ABSTRACT We perform a comparative evaluation of methodologies for computing similarity between short-time melodic fragments of audio recordings of Indian art music. We experiment with 560 different combinations of procedures and parameter values. These include the choices made for the sampling rate ..."
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ABSTRACT We perform a comparative evaluation of methodologies for computing similarity between short-time melodic fragments of audio recordings of Indian art music. We experiment with 560 different combinations of procedures and parameter values. These include the choices made for the sampling

Melodic String Matching via Interval Consolidation and Fragmentation

by Carl Barton, Emilios Cambouropoulos, Costas S. Iliopoulos, Zsuzsanna Lipták
"... Abstract. In this paper, we address the problem of melodic string matching that enables identification of varied (ornamented) instances of a given melodic pattern. To this aim, a new set of edit distance operations adequate for pitch interval strings is introduced. Insertion, deletion and replacemen ..."
Abstract - Cited by 1 (1 self) - Add to MetaCart
and replacement operations are abolished as irrelevant. Consolidation and fragmentation are retained, but adapted to the pitch interval domain, i.e., two or more intervals of one string may be matched to an interval from a second string through consolidation or fragmentation. The melodic interval string matching

Gaussian Mixture Models for Extraction of Melodic Lines from Audio Recordings

by Matija Marolt , 2004
"... The presented study deals with extraction of melodic line(s) from polyphonic audio recordings. We base our work on the use of expectation maximization algorithm, which is employed in a two-step procedure that finds melodic lines in audio signals. In the first step, EM is used to find regions in the ..."
Abstract - Cited by 13 (0 self) - Add to MetaCart
in the signal with strong and stable pitch (melodic fragments). In the second step, these fragments are grouped into clusters according to their properties (pitch, loudness...). The obtained clusters represent distinct melodic lines. Gaussian Mixture Models, trained with EM are used for clustering. The paper

THE MELODIC SIGNATURE INDEX FOR FAST CONTENT-BASED RETRIEVAL OF SYMBOLIC SCORES

by Camelia Constantin, Zoé Faget
"... NEUMA is an on-line library that stores collections of symbolic scores and proposes a public interface to search for melodic pieces based on several kinds of patterns: pitchesbased, with or without rhythms, transposed or not. In addition, searches can be either exact or approximate. We describe an i ..."
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and expressive way the sequences of complex features that constitute a melodic description. functions that support the analysis process. The patternmatching function takes a pattern P and carries out a search over the score collections, looking for all the melodic fragments that “match ” P. The function can

The Processing of Chords in Tonal Melodic Sequences

by Erik Jansen, Dirk-jan Povel
"... A model is proposed for the On-Line Harmonic Processing (OLHP) of tonal melodic sequences in which each incoming tone is described in terms of its features Fittingness, compliance with the previous harmony, Uncertainty, ambiguity of a new harmony, and Chord Change, goodness of the connection between ..."
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A model is proposed for the On-Line Harmonic Processing (OLHP) of tonal melodic sequences in which each incoming tone is described in terms of its features Fittingness, compliance with the previous harmony, Uncertainty, ambiguity of a new harmony, and Chord Change, goodness of the connection
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