# Efficient Melody Extraction Based on Extreme Learning Machine
## 1 Introduction  
Melody extraction, also known as main melody extraction or predominant F0 estimation, aims to extract the predominant pitch sequence of melody (the lead voice or instrument) from polyphonic music [1]. It can be used in some applications, such as query-by-humming [2], version identification [3], music retrieval [4], and so on. Various methods have been proposed since [PERSON] first put forward the melody extraction problem [5]. Salience and temporal continuity are two principles commonly utilized in the literature. In the early studies, researchers tried various ways to formulate the melody extraction problem based on the two principles. For example, [PERSON] et al. designed a translation invariant model to track the lead melody based on probabilistic latent component analysis [6]. Expectation maximization was utilized to estimate the model parameters. [PERSON] et al. defined a set of contour characteristics, studied theirs distributions, and devised rules to distinguish melodic and non-melodic contours [7]. This approach works especially well on singing melody extraction due to the preference of singing pitch contour. Later, [PERSON] and [PERSON] combined a salience function based on a smoothed instantaneous mixture model and pitch tracking based on pitch contour characterization [8; 9]. To alleviate the low-frequency strong accompaniment influence, [PERSON] et al. generalized the Euclidean algorithm, which was designed for computing the greatest common divisor of two natural numbers to positive real numbers, andproposed the melody extraction method based on the modified Euclidean algorithm [10]. In contrast to tracking the pitch values, [PERSON] and [PERSON] proposed an online vocal melody extraction method, which traced various sources with the help of harmonic clusters and then determined the predominant vocal source by using the harmonic strength of the source. Recently, the task was formulated in the Bayesian filtering framework, and the salience, timbre similarity, and spectral smoothness were incorporated in the likelihood function, while temporal continuity was modeled in the pitch transition probability [11]. The aforementioned methods all addressed the melody extraction by human-crafted features and their performances degraded greatly when dealing with more complex music. More recently, some deep learning-based methods have been proposed. Typical methods include the method by [PERSON] et al., who separated the singing voice from polyphonic music, using a deep neural network, and tracked the melodic pitch trajectory by dynamic programming [12]. [PERSON] and [PERSON] introduced two deep neural networks for singing melody extraction: one for pitch estimation and the other for singing voice detection [13]. The aforementioned works obtain singing pitches directly from the deep learning networks, while there are still some alternative methods. For example, [PERSON] et al. got the deep salience representations for F0 estimation [14]. [PERSON] and [PERSON] addressed the melody extraction problem from the semantic segmentation on a time-frequency image perspective [15]. Afterwards, following [PERSON] and [PERSON]'s work, [PERSON] et al. added links between the pooling layers of the encoder and the un-pooling layers of the decoder to reduce convolution layers and simplify convolution modules [16]. The deep learning-based methods can automatically learn high-level features, according to the training data. They are capable of learning more sophisticated features. However, their performance strongly relies on the capacity and variability of training set, and it is often time-consuming to train their parameters. Among these methods, Melodia is commonly considered as state of the art, especially for singing melody extraction [7; 17]. It is a typical salience-based method, which is centered on the creation and characterization of pitch contours. The pitch contours are generated and grouped using heuristics based on auditory streaming cues.