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Found 7 papers, 4 papers with code
Barwise Music Structure Analysis with the Correlation Block-Matching Segmentation Algorithm
Music Structure Analysis (MSA) is a Music Information Retrieval task consisting of representing a song in a simplified, organized manner by breaking it down into sections typically corresponding to ``chorus'', ``verse'', ``solo'', etc.
Convolutive Block-Matching Segmentation Algorithm with Application to Music Structure Analysis
In particular, the CBM algorithm is a dynamic programming algorithm, applying on autosimilarity matrices, a standard tool in MSA.
Semi-Supervised Convolutive NMF for Automatic Piano Transcription
Automatic Music Transcription, which consists in transforming an audio recording of a musical performance into symbolic format, remains a difficult Music Information Retrieval task.
Barwise Compression Schemes for Audio-Based Music Structure Analysis
More specifically, under the hypothesis that MSA is correlated with similarities occurring at the bar scale, this article introduces the use of linear and non-linear compression schemes on barwise audio signals.
Exploring single-song autoencoding schemes for audio-based music structure analysis
The ability of deep neural networks to learn complex data relations and representations is established nowadays, but it generally relies on large sets of training data.
Nonnegative Tucker Decomposition with Beta-divergence for Music Structure Analysis of Audio Signals
Nonnegative Tucker decomposition (NTD), a tensor decomposition model, has received increased interest in the recent years because of its ability to blindly extract meaningful patterns, in particular in Music Information Retrieval.
Uncovering audio patterns in music with Nonnegative Tucker Decomposition for structural segmentation
Recent work has proposed the use of tensor decomposition to model repetitions and to separate tracks in loop-based electronic music.
