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Synthetic queries fail to improve real-world melody search, study finds

Researchers have found that synthetic queries are insufficient for training real-world query-by-melody systems. While training on synthetic whistles and sung queries with incorrect lyrics improved retrieval rates for synthetic data, these gains did not transfer to real-world performance. Fine-tuning on actual user whistles showed some improvement for known songs, but further attempts with more advanced synthetic data generation methods also failed to yield significant improvements on real-world whistle queries. The study concludes that collecting real user queries is essential for developing effective query-by-melody systems. AI

IMPACT Highlights the limitations of synthetic data in training robust AI models for real-world applications, emphasizing the need for authentic user data.

RANK_REASON Academic paper detailing research findings on synthetic data for query-by-melody systems. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Synthetic queries fail to improve real-world melody search, study finds

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Academic paper detailing research findings on synthetic data for query-by-melody systems. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Luigi Rivolta ·

    Whistle and Wrong Lyrics: Synthetic Queries Are Not Enough for Real-World Query-by-Melody

    People look up a song by humming it, but also by whistling it or singing it with the wrong words. Published query-by-humming systems and benchmarks focus on hums and sung queries, and no public dataset pairs whistles or wrong-lyrics singing with song recordings. We study both que…