Melody, bass line, and harmony representations for music version identification

Abstract

In this paper we compare the use of different musical representations for the task of version identification (i.e. retrieving alternative performances of the same musical piece). We automatically compute descriptors representing the melody and bass line using a state-of-the-art melody extraction algorithm, and compare them to a harmony-based descriptor. The similarity of descriptor sequences is computed using a dynamic programming algorithm based on nonlinear time series analysis which has been successfully used for version identification with harmony descriptors. After evaluating the accuracy of individual descriptors, we assess whether performance can be improved by descriptor fusion, for which we apply a classification approach, comparing different classification algorithms. We show that both melody and bass line descriptors carry useful information for version identification, and that combining them increases version detection accuracy. Whilst harmony remains the most reliable musical representation for version identification, we demonstrate how in some cases performance can be improved by combining it with melody and bass line descriptions. Finally, we identify some of the limitations of the proposed descriptor fusion approach, and discuss directions for future research.

A Robust Cover Song Identification System with Two-Level Similarity Fusion and Post-Processing

“…Considering that the similarity between two tracks can be calculated based on different descriptors and similarity functions, the complementary properties are neglected while using a single similarity function. It has been verified [6][7][8] that different descriptors and similarity functions are complementary to each other in the CSI task. To fully take advantage of the common as well as complementary information contained in different descriptors and similarity functions in describing the similarity between tracks, some researchers began to study similarity fusion algorithms for CSI.…”

“…Then, the maximum value of the similarities obtained based on main melody, accompaniment, and mixture signal, separately, was taken as the final similarity. In [6], the standard classification-based fusion strategy [10] was adopted to fuse the similarities of three related yet different descriptors (harmony, melody, and bass line). In [11], the fusion of different similarities was achieved by projecting different similarities in a multi-dimensional space, where the dimensionality of the space was the number of similarities considered.…”

Two-layer similarity fusion model for cover song identification

“…Since only chroma descriptors were considered, the fused similarity only accounted for the same musical facet, the harmony. To solve this problem, in [25], the similarities based on three related yet different descriptors (harmony, melody, and bass line) were fused with the power of a standard classification approach similar to [24]. In [31], the fusion of different similarities was achieved by projecting all similarities in a multi-dimensional space, where the dimensionality of this space was the number of similarities considered.…”

“…To solve this problem, some researchers began to study descriptor or similarity fusion models for the CSI task [22][23][24][25][26] (see Section 2). In this paper, we propose a two-layer similarity fusion model for the CSI task aiming at enhancing the identification accuracy and classification efficiency further.…”

A heuristic for distance fusion in cover song identification

“…Based on this fact, for example, the system Hydra [7] combines features and distances extracted with different parameters which are fed to a Support Vector Machine which output, for each pair of songs, a single bit decision of the type cover/non-cover. A similar approach is used in [8], where a distance is calculated over three different audio descriptor and a classifier is trained with a subset of known cover or non-cover songs pairs. In our work instead, we do not apply any classification and no training is needed.…”