# Singing Transcription from Polyphonic Music Using Melody Contour Filtering
## 1 Introduction
Over the past decade, a series of excellent algorithm applications and proposals have brought about great improvements to melody extraction tasks, and more results have been referred to MIREX from 2005 to present. The main points is to extract the melody of the singing voice by signal processing, according to the spectrum diagram [10; 11; 12; 13], which is mixed with the separation algorithm [14] and non-negative matrix factorization (NMF) [15]. Due to the widespread use of neural networks [16], the results have reached a high level of maturity. Our observation and research found that an accompaniment affects fluctuation performance as significantly as a singing voice, specifically in a karaoke scene. In this situation, a previously present singing voice melody was extracted from a complex mixture in which the melody was embedded between two masking melodies--one with a lower accompaniment and the other with a higher fundamental frequency. Two main challenges were presented by this task: first, the estimation of the fundamental frequency corresponding to the singing voice melody [3; 17], and second, the elimination of the interference of octave errors [18]. In [17; 19], the existence of a singing voice was judged by a salience function while, in most cases [16; 20], this has been detected with a neural network. The two key components of our method are the extraction of edge features from an input audio mixture and melody contour filtering from overall contour lines. The system shows significantly improved performance compared to previous approaches. While previous studies on singing transcription have emphasized monophonic music genres [21; 22] or a single accompaniment [23], the present method focuses on a more robust performance, with an elaborate configuration according to the auditory environment. The proposed algorithm originates from an audio signal and relies on the use of information from the spectrum graph to detect all of the melodies. Therefore, it can be seen that all melody extraction algorithms are inseparable from the feedback results of information in the frequency domain generated by the Fourier transform. We observe the distribution of the singing voice through the spectrum diagram and try to find a certain distribution law from it to facilitate the implementation of our algorithm [24]. We demonstrate that a great deal of information from the signal can be discarded while still retaining its rhythmical aspects. The creation of a singing transcription system that is able to transcribe polyphonic music in a karaoke scene, without setting restrictions on the degree of polyphony and instrument types, is still an open question. The rest of the article is structured as follows. In Section 2, the phase spectral reconstruction using sinusoid extraction and the building of the salient spectrogram by harmonic superposition are presented. The melody contours are selected by a salience function based on edge searching and contour filtering by setting breakpoints and post-processing. In Section 3, the melody contour is segmented using stability region segmentation and a pitch line matching algorithm. In Section 4, we evaluate our methods by MIREX audio melody extraction metrics and the singing transcription criterion. Finally, in Section 5, we present conclusions about our system.