AUTOMATIC IDENTIFICATION OF FREQUENCIES OF MUSICAL NOTES IN POLYPHONICS WAVE FILES

This paper deal with the problems that can be found when someone wishes automatically identify frequencies of musical notes recorded in WAVE format. The same approaches the problem of the analog to digital conversion and of the sample rating of the files in this format when analyzed through Fast Fourier transform. This transform generates a significant amount of lateral lobes around the existent frequencies in these files, taking the professional to identify musical notes not existent in the original files. Like this, this paper presents optimum values of windows to minimize such problem. An appropriate window is not just an efficient and definitive solution to this project. This way a forecast method, refinement and identification is presented here to identify musical notes in files in the WAVE format . 1 Introduction The man is always looking for automatize tiresome and difficult accomplishment tasks. So, he has been looking for mathematical tools and heuristics that qualify the automatic processes to accomplish these tasks that he judges simple or that he can't do. The fact of the man examines the sky and to recognize and to differentiate the planets and stars are difficult tasks of being automated. One of the existent reasons is due to the limitations imposed by the acquisition systems and digital conversion of data and of the existent mathematical tools to manipulate such data. While the eye and the human ear process continuous signals, a computer depends of given digital data that are far away to be considered satisfactory for an efficient analysis. One of these problems is the transformation of a sound file in the WAVE format for the MIDI format. The problem is in the recognition of the frequency and duration of musical notes in a file WAVE and code in a file in the MIDI format SMF 0 or 1. 2 The great problem To discover new stars in the sky has everything in common with discovering musical notes in certain music. In both cases we have continuous signals, in other words, the light of the stars and the sound of music, where the main problem is that it's not known what it looks for. For this case the existent frequencies of the stars are not known sought in the sky as well as the frequencies of the musical notes of the sound signal. The task gets complicated when we tried to apply FFT in the signals. This tool, when applied to signals whose frequencies are not known, they produce frequencies non-existent in the original signals as a result of the analysis. Mello [1] in your paper “Estimation of periods from unequally spaced observations” AUTOMATIC IDENTIFICATION OF FREQUENCIES OF MUSICAL NOTES IN POLYPHONICS WAVE FILES Carlos Alberto Lopes da Silva Gilberto Arantes Carrijo Federal University of Uberlândia Federal University of Uberlândia Brazil Brazil carlossilva@quantica.com. br gilberto@ufu.br Luciano Vieira Lima Antônio Claudio P. Veiga Federal University of Uberlândia Federal University of Uberlândia Brazil Brazil rhodan@quantica.com. br acpveiga@ufu.br for The Astronomic Journal 1981, got to idealize the first mathematical tool to solve the problem of identification of frequencies of stars ignored in a digital map of any section of the sky. Silva et all [2] it sketches in your paper for Diderot 1999 a migration of such technology for a possible solution, also, of the problem proposed in this paper. 3 – The problem for recognition of the musical notes To be possible to analyze a sound file correctly it is initially necessary to divide it in pictures (windows) that contain 2 points in a signal period, where n is the total number of points of the picture. Such division is necessary to use Fast Fourier Transform (FFT). When run FFT with a window of fixed size in a file containing a musical note, the detection of this note is restricted to a maximum size of the window. This is characterized due to the fact that the analog to digital conversion introduces mistakes in the file (aliasing). When analyzing a file WAVE containing only a musical note (C4) we can observe the following facts: when we apply FFT with a picture size with more than 8192 samples, the musical note C4 is found and mistakes are not introduced (presence of other notes). When the size of the picture is reduced (4096–2048–1024, etc) notes that don't belong to the file starts to appear. This fact is possible of being visualized through the table 1. With this we ended that, starting from a size of window of 4096 bytes, not more it is possible to identify a musical note accurately. It can be ended that, for this case, the ideal size of window should be 8192 bytes, too big for a detailed analysis of all the notes present in the file. Found notes Original note Number of Pictures for Sample Picture Note Intensity (dB) WINDO W (Bytes) C4 01 01 C4 114,