Cover song identification
7 papers with code • 4 benchmarks • 0 datasets
Cover Song Identification is the task of identifying an alternative version of a previous musical piece, even though it may differ substantially in timbre, tempo, structure, and even fundamental aspects relating to the harmony and melody of the song. The term “cover” is so wide that it ranges from acoustic renditions of a previous song, to Jimi Hendrix’ famous (and radical) reinterpretation of Bob Dylan’s “All Along the Watchtower”, to Rage Against the Machine essentially rewriting Bob Dylan’s “Maggie’s Farm”. Beyond its value for computational musicology and for enhancing music recommendation, Cover Song Identification is of interest because of its potential for benchmarking other music similarity and retrieval algorithms. Chord analysis, melody extraction and music similarity are all strongly connected to Cover Song Identification - another field of music analysis where AI has been applied.
Benchmarks
These leaderboards are used to track progress in Cover song identification
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Latest papers with no code
DisCover: Disentangled Music Representation Learning for Cover Song Identification
We analyze the CSI task in a disentanglement view with the causal graph technique, and identify the intra-version and inter-version effects biasing the invariant learning.
ByteCover3: Accurate Cover Song Identification on Short Queries
Deep learning based methods have become a paradigm for cover song identification (CSI) in recent years, where the ByteCover systems have achieved state-of-the-art results on all the mainstream datasets of CSI.
BYTECOVER2: TOWARDS DIMENSIONALITY REDUCTION OF LATENT EMBEDDING FOR EFFICIENT COVER SONG IDENTIFICATION
Convolutional neural network (CNN)-based methods have dominated the recent research of cover song identification (CSI).
Contrastive Learning with Positive-Negative Frame Mask for Music Representation
We devise a novel contrastive learning objective to accommodate both self-augmented positives/negatives sampled from the same music.
SIMILARITY LEARNING FOR COVER SONG IDENTIFICATION USING CROSS-SIMILARITY MATRICES OF MULTI-LEVEL DEEP SEQUENCES
One part is a network for learn- ing the deep sequence representation of music tracks, and the other is a similarity estimation network which takes as input the cross- similarity matrices calculated from the deep sequences of a pair of tracks.
Audio Cover Song Identification using Convolutional Neural Network
To do this, we first build the CNN using as an input a cross-similarity matrix generated from a pair of songs.
Early MFCC And HPCP Fusion for Robust Cover Song Identification
We also introduce a new medium-sized hand designed benchmark dataset called "Covers 1000," which consists of 395 cliques of cover songs for a total of 1000 songs, and we show that our algorithm achieves an MRR of 0. 9 on this dataset for the first correctly identified song in a clique.
Identifying Cover Songs Using Information-Theoretic Measures of Similarity
This paper investigates methods for quantifying similarity between audio signals, specifically for the task of of cover song detection.
