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Results 1 - 10
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73
1 A Mid-Level Representation for Melody-based Retrieval in Audio Collections
"... Abstract — Searching audio collections using high-level musical descriptors is a difficult problem, due to the lack of reliable methods for extracting melody, harmony, rhythm, and other such descriptors from unstructured audio signals. In the paper, we present a novel approach to melody-based retrie ..."
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the use of a two-dimensional shift-invariant transform to extract shift-invariant melodic fragments from the melodic representation and demonstrate how such fragments can be indexed and stored in a song database. An efficient search algorithm based on locality-sensitive hashing is used to perform
Melodic String Matching via Interval Consolidation and Fragmentation
"... 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 ..."
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Cited by 1 (1 self)
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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
On Finding Melodic Lines in Audio Recordings
, 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 ..."
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Cited by 17 (1 self)
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(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
The Processing of Chords in Tonal Melodic Sequences
"... 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
Audio Melody Extraction Based on Timbral Similarity of Melodic Fragments
- 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 ..."
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Cited by 3 (0 self)
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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
"... 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
Memory-based models of melodic analysis: Challenging the gestalt principles
- Journal of New Music Research
, 2002
"... We argue for a memory-based approach to music analysis which works with concrete musical experiences rather than with abstract rules or principles. New pieces of music are analyzed by combining fragments from structures of previously encountered pieces. The occurrence-frequencies of the fragments ar ..."
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Cited by 38 (5 self)
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We argue for a memory-based approach to music analysis which works with concrete musical experiences rather than with abstract rules or principles. New pieces of music are analyzed by combining fragments from structures of previously encountered pieces. The occurrence-frequencies of the fragments
Learning Melodic Expectancy: Musical Predictability in an SRN
"... 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
"... 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
Gaussian Mixture Models for Extraction of Melodic Lines from Audio Recordings
, 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 ..."
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Cited by 13 (0 self)
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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
Results 1 - 10
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73
