Music Pattern Discovery with Variable Markov Oracle: A Unified Approach to Symbolic and Audio Representations
نویسندگان
چکیده
This paper presents a framework for automatically discovering patterns in a polyphonic music piece. The proposed framework is capable of handling both symbolic and audio representations. Chroma features are post-processed with heuristics stemming from musical knowledge and fed into the pattern discovery framework. The pattern-finding algorithm is based on Variable Markov Oracle. The Variable Markov Oracle data structure is capable of locating repeated suffixes within a time series, thus making it an appropriate tool for the pattern discovery task. Evaluation of the proposed framework is performed on the JKU Patterns Development Dataset with state of the art performance.
منابع مشابه
Bridging the Audio-Symbolic Gap: The Discovery of Repeated Note Content Directly from Polyphonic Music Audio
Algorithms for the discovery of musical repetition have been developed in audio and symbolic domains more or less independently for over a decade. In this paper we combine algorithms for multiple F0 estimation, beat tracking, quantisation, and pattern discovery, so that for the first time, the note content of motifs, themes, and repeated sections can be discovered directly from polyphonic music...
متن کاملSymCHM—An Unsupervised Approach for Pattern Discovery in Symbolic Music with a Compositional Hierarchical Model
This paper presents a compositional hierarchical model for pattern discovery in symbolic music. The model can be regarded as a deep architecture with a transparent structure. It can learn a set of repeated patterns within individual works or larger corpora in an unsupervised manner, relying on statistics of pattern occurrences, and robustly infer the learned patterns in new, unknown works. A le...
متن کاملA Methodology for the Comparison of Melodic Generation Models Using Meta-Melo
We investigate Musical Metacreation algorithms by applying Music Information Retrieval techniques for comparing the output of three off-line, corpus-based style imitation models. The first is Variable Order Markov Chains, a statistical model; second is the Factor Oracle, a pattern matcher; and third, MusiCOG, a novel graphical model based on perceptual and cognitive processes. Our focus is on d...
متن کاملMirex 2014 Entry: Music Segmentation Techniques and Greedy Path Finder Algorithm to Discover Musical Patterns
This extended abstract describes the pattern discovery submission to MIREX 2014 of an algorithm that uses music segmentation (or music structure analysis) techniques and a refined greedy method in order to identify the repetitive musical patterns of a given music piece, either represented symbolically or with an actual audio file. We obtain a harmonic representation of the input and compute the...
متن کاملAudio Information Retrieval (AIR) Tools
The majority of work in music information retrieval (IR) has been focused on symbolic representations of music. However, most of the digitally available music is in the form of raw audio signals. Although various attempts at monophonic and polyphonic transcription have been made, none has been successful and general enough to work with real world signals. In this paper we describe some initial ...
متن کامل