AASP-L10.4
A BAYESIAN APPROACH TO SINGING SKILL EVALUATION USING SEMITONE PITCH HISTOGRAM AND MCMC-BASED GENERATED QUANTITIES
Tomoyasu Nakano, Masataka Goto, National Institute of Advanced Industrial Science and Technology (AIST), Japan
Session:
AASP-L10: Music Content Analysis Oral
Track:
Audio and Acoustic Signal Processing [AA]
Location:
Room 127+128
Presentation Time:
Fri, 8 May, 10:00 - 10:20
Presentation
Discussion
Resources
No resources available.
Session AASP-L10
AASP-L10.1: Audio-to-Score Jazz Solo Transcription with the Rhythm Perceiver
Ivan Shanin, Queen Mary University of London, United Kingdom of Great Britain and Northern Ireland; Xavier Riley, Sound Patrol Inc., United States of America; Simon Dixon, Queen Mary University of London, United Kingdom of Great Britain and Northern Ireland
AASP-L10.2: MotionBeat: Motion-Aligned Music Representation via Embodied Contrastive Learning and Bar-Equivariant Contact-Aware Encoding
Xuanchen Wang, Heng Wang, Weidong Cai, The University of Sydney, Australia
AASP-L10.3: NOISE-TO-NOTES: DIFFUSION-BASED GENERATION AND REFINEMENT FOR AUTOMATIC DRUM TRANSCRIPTION
Michael Yeung, Keisuke Toyama, Toya Teramoto, Shusuke Takahashi, Tamaki Kojima, Sony Group Corporation, Japan
AASP-L10.4: A BAYESIAN APPROACH TO SINGING SKILL EVALUATION USING SEMITONE PITCH HISTOGRAM AND MCMC-BASED GENERATED QUANTITIES
Tomoyasu Nakano, Masataka Goto, National Institute of Advanced Industrial Science and Technology (AIST), Japan
AASP-L10.5: Benchmarking Music Autotagging with MGPHot Expert Annotations vs. Generic Tag Datasets
Pedro Ramoneda, Pablo Alonso, MTG - Universitat Pompeu Fabra, Spain; Sergio Oramas, Independent Researcher, Spain; Xavier Serra, Dmitry Bogdanov, MTG - Universitat Pompeu Fabra, Spain
AASP-L10.6: SAUNA: SONG-LEVEL AUDIO & USER-LISTENING DATA NEURAL ALIGNMENT
Morgan Buisson, CNRS, France; Juan José Bosch, Daniel Stoller, Spotify, France
Contacts