5 citations · 5 across the 4 of their papers we have counts for
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Adapting Diffusion-Based Music Synthesis to Speech and Singing Voice Conversion
Ben Maman, Frank Zalkow, Hans-Ulrich Berendes +3
Recent diffusion-based generative models have achieved strong results in domain-specific audio generation tasks such as speech, singing, and instrumental music synthesis. However,…
Snapping Matters: Context-Aware Onset Refinement for Automatic Music Transcription
Abhirup Saha, Hans-Ulrich Berendes, Meinard Müller +1
Precise note-level annotations are critical for training automatic music transcription (AMT) systems, in particular note-onset labels, which form a core component of many recent AM…
Count The Notes: Histogram-Based Supervision for Automatic Music Transcription
Jonathan Yaffe, Ben Maman, Meinard Müller +1
Automatic Music Transcription (AMT) converts audio recordings into symbolic musical representations. Training deep neural networks (DNNs) for AMT typically requires strongly aligne…
Performance Conditioning for Diffusion-Based Multi-Instrument Music Synthesis
Ben Maman, Johannes Zeitler, Meinard Müller +1
Generating multi-instrument music from symbolic music representations is an important task in Music Information Retrieval (MIR). A central but still largely unsolved problem in thi…
Unaligned Supervision For Automatic Music Transcription in The Wild
Ben Maman, Amit H. Bermano
Multi-instrument Automatic Music Transcription (AMT), or the decoding of a musical recording into semantic musical content, is one of the holy grails of Music Information Retrieval…