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Moisesdb: A dataset for source separation beyond 4-stems

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arxiv 2307.15913 v1 pith:OPWMFJED submitted 2023-07-29 cs.SD cs.AIeess.AS

Igor Pereira , Felipe Araújo , Filip Korzeniowski , Richard Vogl This is my paper · ORCID
classification cs.SDcs.AIeess.AS
keywords separationdatasetstemsmoisesdbsourcebeyondfacilitatefour
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In this paper, we introduce the MoisesDB dataset for musical source separation. It consists of 240 tracks from 45 artists, covering twelve musical genres. For each song, we provide its individual audio sources, organized in a two-level hierarchical taxonomy of stems. This will facilitate building and evaluating fine-grained source separation systems that go beyond the limitation of using four stems (drums, bass, other, and vocals) due to lack of data. To facilitate the adoption of this dataset, we publish an easy-to-use Python library to download, process and use MoisesDB. Alongside a thorough documentation and analysis of the dataset contents, this work provides baseline results for open-source separation models for varying separation granularities (four, five, and six stems), and discuss their results.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Rethinking Automatic Music Mixing as Sequential Stem Blending

    eess.AS 2026-08 conditional novelty 7.0 of 10

    Automatic music mixing can be reformulated as sequential stem blending using a flow matching model conditioned on the growing submix, with strong in-distribution blending scores and competitive full-mix results.

  2. StemFX: Learning Mixing Style Representations via Autoregressive FX Chain Prediction on Source-Separated Stems

    cs.SD 2026-07 conditional novelty 6.0 of 10

    StemFX predicts tokenized per-stem audio-effect chains with a jointly-trained Transformer encoder-decoder, beating contrastive and prior FX-encoding methods on effect-chain retrieval and real-mix style transfer.

  3. User-guided Generative Source Separation

    cs.SD 2025-07 conditional novelty 6.0 of 10

    GuideSep separates arbitrary target instruments from a mixture using user-provided waveform mimicry and mel-spectrogram masks, and outperforms a same-architecture mask-prediction baseline in SDR and listening tests.

  4. FasTUSS: Faster Task-Aware Unified Source Separation

    cs.SD 2025-07 conditional novelty 5.0 of 10

    FasTUSS cuts TUSS's computational cost by up to 81 percent with minor SNR drops, and introduces a causal variant compatible with KVCache.

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