Automatic cover detection -- the task of finding in an audio database all the covers of one or several query tracks -- has long been seen as a challenging theoretical problem in the MIR community and as an acute practical problem for authors and composers societies. Original algorithms proposed for this task have proven their accuracy on small datasets, but are unable to scale up to modern real-life audio corpora. On the other hand, faster approaches designed to process thousands of pairwise comparisons resulted in lower accuracy, making them unsuitable for practical use. In this work, we propose a neural network architecture that is trained to represent each track as a single embedding vector. The computation burden is therefore left to the embedding extraction -- that can be conducted offline and stored, while the pairwise comparison task reduces to a simple Euclidean distance computation. We further propose to extract each track's embedding out of its dominant melody representation, obtained by another neural network trained for this task. We then show that this architecture improves state-of-the-art accuracy both on small and large datasets, and is able to scale to query databases of thousands of tracks in a few seconds.
In a recent work @cite_42 , we suggested in an analogy with image processing that dominant melody extraction can be seen as a type of image segmentation, where contours of the melody have to be isolated from the surrounding background. We have thus proposed for dominant melody estimation an adaptation of U-Net @cite_40 -- a model originally designed for medical image segmentation -- which slightly improves over @cite_45 .
@cite_40 @cite_42 @cite_45
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Estimation of dominant melody in polyphonic music remains a difficult task, even though promising breakthroughs have been done recently with the introduction of the Harmonic CQT and the use of fully convolutional networks. In this paper, we build upon this idea and describe how U-Net- a neural network originally designed for medical image segmentation - can be used to estimate the dominant melody in polyphonic audio. We propose in particular the use of an original layer-by-layer sequential training method, and show that this method used along with careful training data conditioning improve the results compared to plain convolutional networks. Extraction of predominant melody from the musical performances containing various instruments is one of the most challenging task in the field of music information retrieval and computational musicology. This paper presents a novel framework which estimates predominant vocal melody in real-time by tracking various sources with the help of harmonic clusters (combs) and then determining the predominant vocal source by using the harmonic strength of the source. The novel on-line harmonic comb tracking approach complies with both structural as well as temporal constraints simultaneously. It relies upon the strong higher harmonics for robustness against distortion of the first harmonic due to low frequency accompaniments, in contrast to the existing methods which track the pitch values. The predominant vocal source identification depends upon the novel idea of source dependant filtering of recognition score, which allows the algorithm to be implemented on-line. The proposed method, although on-line, is shown to significantly outperform our implementation of a state-of-the-art offline method for vocal melody extraction. Evaluations also show the reduction in octave error and the effectiveness of novel score filtering technique in enhancing the performance. Melody extraction algorithms for single-channel polyphonic music typically rely on the salience of the lead melodic instrument, considered here to be the singing voice. However the simultaneous presence of one or more pitched instruments in the polyphony can cause such a predominant-F0 tracker to switch between tracking the pitch of the voice and that of an instrument of comparable strength, resulting in reduced voice-pitch detection accuracy. We propose a system that, in addition to biasing the salience measure in favor of singing voice characteristics, acknowledges that the voice may not dominate the polyphony at all instants and therefore tracks an additional pitch to better deal with the potential presence of locally dominant pitched accompaniment. A feature based on the temporal instability of voice harmonics is used to finally identify the voice pitch. The proposed system is evaluated on test data that is representative of polyphonic music with strong pitched accompaniment. Results show that the proposed system is indeed able to recover melodic information lost to its single-pitch tracking counterpart, and also outperforms another state-of-the-art melody extraction system designed for polyphonic music. We present a novel system for the automatic extraction of the main melody from polyphonic music recordings. Our approach is based on the creation and characterization of pitch contours, time continuous sequences of pitch candidates grouped using auditory streaming cues. We define a set of contour characteristics and show that by studying their distributions we can devise rules to distinguish between melodic and non-melodic contours. This leads to the development of new voicing detection, octave error minimization and melody selection techniques. A comparative evaluation of the proposed approach shows that it outperforms current state-of-the-art melody extraction systems in terms of overall accuracy. Further evaluation of the algorithm is provided in the form of a qualitative error analysis and the study of the effect of key parameters and algorithmic components on system performance. Finally, we conduct a glass ceiling analysis to study the current limitations of the method, and possible directions for future work are proposed.