# Singing Transcription from Polyphonic Music Using Melody Contour Filtering
## 1 Introduction  
The task of singing transcription from polyphonic music has always been worthy of research, and it represents one of the most difficult challenges when analyzing audio information and retrieving music content. The automatic music transcription approach described in [1] exclusively considers instrumental music and does not discuss the effects of drum instruments. The emphasis is on estimating the multiple fundamental frequencies of several concurrent sounds, such as a piano. The definition of polyphonic music is different from multiple fundamental frequency estimation and tracking [2] and automatic music transcription. In music, polyphony is the simultaneous combination of two or more tones or melodic lines. However, according to the description in Mirex2020 Singing Transcription, the task of transcribing polyphonic music containing only monophonic vocals into notes is known as singing transcription from polyphonic music [3]. Even a single interval made up of two simultaneous tones, or a chord of three simultaneous tones, is rudimentarily polyphonic. A piece of music could exhibit a rather moderate degree of polyphony, featuring a predominant singing voice and a light accompaniment. This definition is consistent with the description in [4], and the singing voice is the source of the main melody in popular music. The data collections used for the evaluation of melody extraction tasks and singing transcription tasks deal mostly with percussive instrument and rhythmic instrumental music. In this article we divide singing transcription into two sub-tasks based on note-level and frame-level factors. First, from the perspective of notation, note transcription, with notes as the basic units, is consistent with people's cognition of music. Second, according to human auditory effects, melody extraction, which uses signal frames as the basic units, conforms to the characteristic of decomposing audio into signal frames through a Fourier transform in audio analysis. The audio signal displayed on the two-dimensional plane isconsistent with human subjective cognition on the time axis, with the loudness of multiple signals superimposed on the vertical axis. The direct processing of notes-level signals seems to be inapplicable to singing transcription systems. Extracting a signal frame that represents the main melody and converting it to a symbol representation similar to MIDI makes it easier to understand [5]. The signal frame acts as an intermediary to connect audio frequencies and notes. Therefore, the melody contour extracted in our paper can be used to directly obtain the signal frame, and the basic information composed of note-level factors can also be obtained by segmenting and calculating the data. Since singing transcription has become an official project of MIREX this year, it has been given a clear definition of singing transcription and the test collections of \"Cmedia\". There are a few algorithms available for the evaluation and verification of singing transcription tasks. Therefore, we use the performance of melody extraction at the frame level to further evaluate the accuracy of our algorithms. Extracting a melody from polyphonic music is the process of extracting the fundamental frequency from the singing signal. Finding fundamental frequencies from complex audio signals with an accompaniment, in addition to the analysis of the contour characteristics, can help us achieve the appropriate reproduction mode for music research. The extracted melody can be used for music information retrieval such as humming recognition [6], a karaoke environment for a melody line to verify the accuracy of a singer [7], or to maintain originality and effectively prevent copyright issues [8; 9]. It even be used in secondary creation to achieve more interesting results, such as background music (BGM). A number of methods have been presented regarding the analysis of signal frames.