ASMSP-P8.4

Contrastive Audio-MIDI Learning for Symbolic-domain Musical Instrument Classification

Shun Sawada, Nippon Institute of Technology, Japan
Session:
ASMSP-P8: Music Analysis, Synthesis, and Representation Poster
Track:
ASMSP - Acoustic, Speech and Music Signal Processing
Location:
Poster Area B
Presentation Time:
Thu, 11 Sep, 09:00 - 10:40 Italy Time (UTC +2)
Session Chair:
Alberto Bernardini, Politecnico di Milano
Presentation
Discussion
Resources
No resources available.
Session ASMSP-P8
ASMSP-P8.1: SERENADE: A SINGING STYLE CONVERSION FRAMEWORK BASED ON AUDIO INFILLING
Lester Phillip Violeta, Wen-Chin Huang, Tomoki Toda, Nagoya University, Japan
ASMSP-P8.2: EXPLORING MULTI-DESCRIPTOR DISENTANGLED REPRESENTATIONS OF ACOUSTIC INSTRUMENT NOTES
Francesco Dal Rí, Gregorio Giudici, Luca Turchet, Nicola Conci, University of Trento, Italy
ASMSP-P8.3: AN ANALYSIS OF MUSICAL INSTRUMENTS DIRECTIVITY BASED ON SPATIAL COMPLEXITY
Federico Miotello, Politecnico di Milano, Italy; Juliano Ribeiro, Ryo Matsuda, Jorge Trevino, Yamaha Corporation, Japan; Mirco Pezzoli, Politecnico di Milano, Italy
ASMSP-P8.4: Contrastive Audio-MIDI Learning for Symbolic-domain Musical Instrument Classification
Shun Sawada, Nippon Institute of Technology, Japan
ASMSP-P8.6: WINDOW FUNCTION-LESS DFT WITH REDUCED NOISE AND LATENCY FOR REAL-TIME MUSIC ANALYSIS
Cai Biesinger, Hiromitsu Awano, Masanori Hashimoto, Kyoto University, Japan
ASMSP-P8.7: GRADIENT CLIPPING IMPROVES NEURAL NETWORK OPTIMIZATION FOR PERCEPTUAL SOUND MATCHING
Han Han, Vincent Lostanlen, Mathieu Lagrange, LS2N, CNRS, CENTRALE NANTES, France
ASMSP-P8.8: ON THE JOINT MINIMIZATION OF REGULARIZATION LOSS FUNCTIONS IN DEEP VARIATIONAL BAYESIAN METHODS FOR ATTRIBUTE-CONTROLLED SYMBOLIC MUSIC GENERATION
Matteo Pettenò, Alessandro Ilic Mezza, Alberto Bernardini, Politecnico di Milano, Italy