Music Source Separation
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Benchmarks
Most implemented
Conv-TasNet: Surpassing Ideal Time-Frequency Magnitude Masking for Speech Separation
Wave-U-Net: A Multi-Scale Neural Network for End-to-End Audio Source Separation
All for One and One for All: Improving Music Separation by Bridging Networks
Multi-scale Multi-band DenseNets for Audio Source Separation
SCNet: Sparse Compression Network for Music Source Separation
Music Source Separation with Band-split RNN
Papers
Music-Source-Separation-Training (MSST): A Unified Framework for Training and Evaluating Music Demixing Models
Music Source Separation (MSS), the task of recovering individual sound components (stems) from a polyphonic mixture, is central to applications ranging from karaoke and remixing to audio restoration and content productio…
Music Source SeparationMusic Source Restoration with Ensemble Separation and Targeted Reconstruction
The Inaugural Music Source Restoration (MSR) Challenge targets the recovery of original, unprocessed stems from fully mixed and mastered music. Unlike conventional music source separation, MSR requires reversing complex …
Music Source SeparationThe Costs of Reproducibility in Music Separation Research: a Replication of Band-Split RNN
Music source separation is the task of isolating the instrumental tracks from a music song. Despite its spectacular recent progress, the trend towards more complex architectures and training protocols exacerbates reprodu…
Music Source SeparationA Knowledge-Driven Approach to Music Segmentation, Music Source Separation and Cinematic Audio Source Separation
We propose a knowledge-driven, model-based approach to segmenting audio into single-category and mixed-category chunks with applications to source separation. "Knowledge" here denotes information associated with the data…
Music Source SeparationAudio Source SeparationA Conditioned UNet for Music Source Separation
In this paper we propose a conditioned UNet for Music Source Separation (MSS). MSS is generally performed by multi-output neural networks, typically UNets, with each output representing a particular stem from a predefine…
Music Source SeparationThe Spheres Dataset: Multitrack Orchestral Recordings for Music Source Separation and Information Retrieval
This paper introduces The Spheres dataset, multitrack orchestral recordings designed to advance machine learning research in music source separation and related MIR tasks within the classical music domain. The dataset is…
Music Source SeparationInformation Retrieval