Performance Analysis of Software Implementation of Reproducing Music from Musical Notes (Mozart)

In this research take a picture of Mozart of any music or instrument than the process on the captured image and all information pass to the MATLAB for image processing. The Algorithm separates the one line of Mozart and then separate another line in this way separate line by line of the whole Mozart. After separating line another step is to separate beat one by one from the separated line from the picture of Mozart. In this way, all the line and beats of Mozart are separated using the MATLAB software. When all the beats and lines are individual then find the meaning according to their symbol and combined the entire tune related to whole music or instrument. Then whole the music which is combining from the image of Mozart (musical notes) is played through the MATLAB software

A computational framework for aesthetical navigation in musical search space

Paper presented at 3rd AISB symposium on computational creativity, AISB 2016, 4-6th April, Sheffield. Abstract. This article addresses aspects of an ongoing project in the generation of artificial Persian (-like) music. Liquid Persian Music software (LPM) is a cellular automata based audio generator. In this paper LPM is discussed from the view point of future potentials of algorithmic composition and creativity. Liquid Persian Music is a creative tool, enabling exploration of emergent audio through new dimensions of music composition. Various configurations of the system produce different voices which resemble musical motives in many respects. Aesthetical measurements are determined by Zipf’s law in an evolutionary environment. Arranging these voices together for producing a musical corpus can be considered as a search problem in the LPM outputs space of musical possibilities. On this account, the issues toward defining the search space for LPM is studied throughout this paper

MelodyGLM: Multi-task Pre-training for Symbolic Melody Generation

Pre-trained language models have achieved impressive results in various music understanding and generation tasks. However, existing pre-training methods for symbolic melody generation struggle to capture multi-scale, multi-dimensional structural information in note sequences, due to the domain knowledge discrepancy between text and music. Moreover, the lack of available large-scale symbolic melody datasets limits the pre-training improvement. In this paper, we propose MelodyGLM, a multi-task pre-training framework for generating melodies with long-term structure. We design the melodic n-gram and long span sampling strategies to create local and global blank infilling tasks for modeling the local and global structures in melodies. Specifically, we incorporate pitch n-grams, rhythm n-grams, and their combined n-grams into the melodic n-gram blank infilling tasks for modeling the multi-dimensional structures in melodies. To this end, we have constructed a large-scale symbolic melody dataset, MelodyNet, containing more than 0.4 million melody pieces. MelodyNet is utilized for large-scale pre-training and domain-specific n-gram lexicon construction. Both subjective and objective evaluations demonstrate that MelodyGLM surpasses the standard and previous pre-training methods. In particular, subjective evaluations show that, on the melody continuation task, MelodyGLM gains average improvements of 0.82, 0.87, 0.78, and 0.94 in consistency, rhythmicity, structure, and overall quality, respectively. Notably, MelodyGLM nearly matches the quality of human-composed melodies on the melody inpainting task

Creating Persian-like music using computational intelligence

Dastgāh are modal systems in traditional Persian music. Each Dastgāh consists of a group of melodies called Gushé, classified in twelve groups about a century ago (Farhat, 1990). Prior to that time, musical pieces were transferred through oral tradition. The traditional music productions revolve around the existing Dastgāh, and Gushe pieces. In this thesis computational intelligence tools are employed in creating novel Dastgāh-like music.There are three types of creativity: combinational, exploratory, and transformational (Boden, 2000). In exploratory creativity, a conceptual space is navigated for discovering new forms. Sometimes the exploration results in transformational creativity. This is due to meaningful alterations happening on one or more of the governing dimensions of an item. In combinational creativity new links are established between items not previously connected. Boden stated that all these types of creativity can be implemented using artificial intelligence.Various tools, and techniques are employed, in the research reported in this thesis, for generating Dastgāh-like music. Evolutionary algorithms are responsible for navigating the space of sequences of musical motives. Aesthetical critics are employed for constraining the search space in exploratory (and hopefully transformational) type of creativity. Boltzmann machine models are applied for assimilating some of the mechanisms involved in combinational creativity. The creative processes involved are guided by aesthetical critics, some of which are derived from a traditional Persian music database.In this project, Cellular Automata (CA) are the main pattern generators employed to produce raw creative materials. Various methodologies are suggested for extracting features from CA progressions and mapping them to musical space, and input to audio synthesizers. The evaluation of the results of this thesis are assisted by publishing surveys which targeted both public and professional audiences. The generated audio samples are evaluated regarding their Dastgāh-likeness, and the level of creativity of the systems involved