Automatic Music Transcription Using Fourier Transform for Monophonic and Polyphonic Audio File

Abstract

Musical sheet is an important tool for musicians that enables musicians to communicate with each other and help musicians to learn a composition of a song. Sometimes, musicians face an obstacle when they cannot find the musical sheet to learn a new song or it may require payment to get the sheet. The solution for this problem is to learn the song by figuring out the composition of a song using music transcription. Music Transcription is the process of music information retrieval to produce musical notation. Music Transcription using a computational method often called Automatic Music Transcription by upload the audio file as an input and generate musical sheet. The proposed method is solved using Note Value Detection to separate windows by the detected note values and Fourier Transform to recognize the frequency from each window. This study is evaluating the system using three variables; note value, pitch accuracy, and extra notes. The study shows that note value and pitch detection gives a relatively small percentage error. Meanwhile, extra note detection gives a relatively moderate percentage error in every polyphonic file.

Thaat Classification Using Recurrent Neural Networks with Long Short-Term Memory and Support Vector Machine

“…The model extracts MFCCs, chroma features, tempo features, and beat features from the raw music signal, and uses a deep neural network classifier to predict the melody. In [20], the authors propose a method for automatic music transcription using Fourier transform for both monophonic and polyphonic audio files. The method extracts Fourier transform features from the audio signal and uses a simple rule-based classifier to predict the note values and pitches.…”