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arXiv · 2410.04198

DJ Mix Transcription with Multi-Pass Non-Negative Matrix Factorization

Étienne Paul André·Dominique Fourer·Diemo Schwarz
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Abstract

DJ mix transcription is a crucial step towards DJ mix reverse engineering, which estimates the set of parameters and audio effects applied to a set of existing tracks to produce a performative DJ mix. We introduce a new approach based on a multi-pass NMF algorithm where the dictionary matrix corresponds to a set of spectrogram slices of the source tracks present in the mix. The multi-pass strategy is motivated by the high computational cost resulting from the use of a large NMF dictionary. The proposed method uses inter-pass filtering to favor temporal continuity and sparseness and is evaluated on a publicly available dataset. Our comparative results considering a baseline method based on dynamic time warping (DTW) are promising and pave the way of future NMF-based applications.

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Étienne Paul André, Dominique Fourer, Diemo Schwarz. 2024-10-05. DJ Mix Transcription with Multi-Pass Non-Negative Matrix Factorization. https://arxiv.org/abs/2410.04198

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