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BeatNet+: Real-time rhythm analysis for diverse music audio — Transactions of the International Society for Music Information Retrieval

Revision 8c1f614a-9813-4c07-bd08-b2625494034f · 11/1/2026, 4:41:27 PM UTC
Key
heydari2024-beatnet-plus
Authors
Heydari, Mojtaba; Duan, Zhiyao
Issued
2024
Type
article-journal
Container
Transactions of the International Society for Music Information Retrieval
Volume
7
Issue
1
Pages
274-287
Raw CSL JSON
{
  "DOI": "10.5334/tismir.198",
  "URL": "https://transactions.ismir.net/articles/198",
  "page": "274-287",
  "type": "article-journal",
  "issue": "1",
  "title": "BeatNet+: Real-time rhythm analysis for diverse music audio",
  "author": [
    {
      "given": "Mojtaba",
      "family": "Heydari"
    },
    {
      "given": "Zhiyao",
      "family": "Duan"
    }
  ],
  "issued": {
    "date-parts": [
      [
        2024
      ]
    ]
  },
  "volume": "7",
  "container-title": "Transactions of the International Society for Music Information Retrieval"
}

Claims

  1. Real-time rhythm analysis faces three challenges: accuracy without access to future input, low latency (especially on low-powered devices), and generalization to music without strong percussive components. BeatNet+ trains an auxiliary branch on versions of the same music without drums, so the main network learns a representation less dependent on percussion, and adds adaptation strategies for isolated singing voices and non-percussive music.
    "Developing real‑time music rhythm analysis systems involves addressing three key challenges: The first challenge is maintaining high accuracy while not accessing future input data, as offline models do. The second challenge is achieving low latency, especially on low‑powered devices. [...] We argue that the third challenge is generalization to various kinds of music audio. [...] The main branch is fed with full music mixtures, while the auxiliary branch is fed with the full music of the same pieces less drum tracks [...]. Another innovation is on the adaptation strategies that help develop real‑time rhythm analysis models for challenging music scenarios, including isolated singing voices and non‑percussive music."
    Locator: section: Abstract, 1 · Quote language: en
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