5 citations · 5 across the 4 of their papers we have counts for
4 papers
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…
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…