Optimizing Melodic Extraction Algorithm for Jazz Guitar Recordings Using Genetic Algorithms
نویسندگان
چکیده
Extraction of the main melody of a musical piece is a preliminary step in the process of transcribing the piece. Automatic melodic extraction is the task of computationally extracting what a human listener would perceive as the main melody of a polyphonic recording. Several melodic extraction systems have been proposed. However, such systems normally require a number of parameters to be manually tuned in order to accurately perform melody extraction in different contexts, i.e. instruments combinations. In this study, we propose a methodology for automatically optimizing some relevant parameters of a melody extraction algorithm using genetic algorithms. We simultaneously obtained both MIDI and audio recordings of jazz standards, and we collected commercial audio recordings extracted from jazz guitar CDs. Based on the MIDI recordings as ground truth, two different instrument settings are compared (Jazz trio and quartet), as well as different audio mixing of the melody with respect to the accompaniment track. We show that, compared to using the default parameters, the overall accuracy of the melody extraction with the optimized parameters is improved.
منابع مشابه
Understanding Expressive Transformations in Saxophone Jazz Performances Using Inductive Machine Learning
In this paper, we describe an approach to learning expressive performance rules from monophonic Jazz standards recordings by a skilled saxophonist. We have first developed a melodic transcription system which extracts a set of acoustic features from the recordings producing a melodic representation of the expressive performance played by the musician. We apply machine learning techniques to thi...
متن کاملJazz Ensemble Expressive Performance Modeling
Computational expressive music performance studies the analysis and characterisation of the deviations that a musician introduces when performing a musical piece. It has been studied in a classical context where timing and dynamic deviations are modeled using machine learning techniques. In jazz music, work has been done previously on the study of ornament prediction in guitar performance, as w...
متن کاملUsing and comparing metaheuristic algorithms for optimizing bidding strategy viewpoint of profit maximization of generators
With the formation of the competitive electricity markets in the world, optimization of bidding strategies has become one of the main discussions in studies related to market designing. Market design is challenged by multiple objectives that need to be satisfied. The solution of those multi-objective problems is searched often over the combined strategy space, and thus requires the simultaneous...
متن کاملChords not required: Incorporating horizontal and vertical aspects independently in a computer improvisation algorithm
Most previous computer improvisation algorithms for tonal jazz create an output based primarily on the underlying chord progression. This approach may partly ignore melodic continuity often seen in transcriptions of traditional jazz artists. Here we suggest a different approach implemented as a computer algorithm that creates material solely based on probabilities related to past note choices. ...
متن کاملGenJam: A Genetic Algorithm for Generating Jazz Solos
This paper describes GenJam, a genetic algorithm-based model of a novice jazz musician learning to improvise. GenJam maintains hierarchically related populations of melodic ideas that are mapped to specific notes through scales suggested by the chord progression being played. As GenJam plays its solos over the accompaniment of a standard rhythm section, a human mentor gives real-time feedback, ...
متن کامل