# Melody Extraction Using Chroma-Level Note Tracking and Pitch Mapping
## 1 Introduction  
Melody, as the essence of music, plays an important role in understanding music semantics and distinguishing different music pieces. Melody extraction is the process of automatically obtaining the pitch sequence of the lead voice or instrument from polyphonic music [1], and is widely used in music retrieval [2], genre classification [3], cover song identification [4, 5], and so on. Recently, it has become an important topic in the music information retrieval community. Existing melody extraction methods can be divided into three categories: source separation-based methods [6, 7], data-driven classification-based methods [8, 9], and salience-based methods [1, 10, 11, 12]. Source separation-based methods employ spectrum decomposition schemes to separate the lead voice from the mixed recordings, then estimate and track the pitch sequence of the previously extracted source. Since melody-accompaniment separation is an ill-conditioned problem and source separation performance directly affects subsequent pitch estimation and tracking, these methods are suitable for singing melody extraction by making use of the unstable nature of voices, such as vibrato. Data-driven classification-based methods formulate melody extraction as a classification problem, where pitches are quantitized to some specific levels (such as MIDI pitch number) [8, 9]. These methods need few priors, but they often cause quantization errors or over-fitting when the training dataset is small. Most melody extraction methods belong to the salience-based category [1, 10, 11, 12]. Multiple pitches of one music recording are estimated according to some kind of salience function, and tracking strategies are applied to obtain melody pitch sequence taking into account of both salience andsmoothness constraints. [PERSON] [10] developed the YIN algorithm [13], built a salience function in the frequency domain, and then extracted melody based on the Bayesian harmonic model, but it has heavy computational complexity. In [11], a real-time predominant vocal melody extraction framework was presented to track various sources with the help of harmonic clusters. It only tracked the higher harmonic partials, making it suitable for melody whose lower partials are distorted by accompaniment. However, higher octave errors are frequently caused by tracking of higher harmonic partials. In order to deduce the perceived pitch through co-prime harmonic partials, a modified Euclidean algorithm is proposed in [12], which generalizes the Euclidean algorithm to float numbers and utilize it to obtain the perceived pitch. Pitch shifting between different octaves within one note is commonly found due to the intrinsic non-stationary nature of music. A general architecture that underlies most salience-based methods is multiple pitch estimation, followed by pitch trajectory tracking or selection according to the smoothness and salience constraints. If fewer frame-wise pitches are considered, the true one may not lie in the search range. On the contrary, if more pitches are reserved for tracking, false-alarm rate will rise. Moreover, both salience and power distribution over different partials of the lead voice or instrument often vary with time, resulting in false estimations during some notes or frequency shifting between different octaves. One possible approach (to avoid the estimated melodic pitches shifting between different octaves or notes within one note duration) is to estimate pitch class first, and then map the accurate pitches at the note level. [PERSON] and [PERSON] attempted to extract melody and bass line using chroma features, and the salience function was constructed by harmonic pitch class profile (HPCP) [14; 15]. HPCP is, in essence, a pitch-class based salience function, and contains no octave information [14]. This method provides some new clues about measuring the salience of melody and bass line, but it outputs only melodic pitch classes, not their exact frequencies.