2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDMLSP-23.5
Paper Title LOOPNET: MUSICAL LOOP SYNTHESIS CONDITIONED ON INTUITIVE MUSICAL PARAMETERS
Authors Pritish Chandna, Antonio Ramires, Xavier Serra, Emilia Gómez, Universitat Pompeu Fabra, Spain
SessionMLSP-23: Applications in Music and Audio Processing
LocationGather.Town
Session Time:Wednesday, 09 June, 16:30 - 17:15
Presentation Time:Wednesday, 09 June, 16:30 - 17:15
Presentation Poster
Topic Machine Learning for Signal Processing: [MLR-MUSAP] Applications in music and audio processing
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Loops, seamlessly repeatable musical segments, are a corner-stone of modern music production. Contemporary artists often mix and match various sampled or pre-recorded loops based on musical criteria such as rhythm, harmony and timbral texture to create com-positions. Taking such criteria into account, we present LoopNet, a feedforward generative model for creating loops conditioned on intuitive parameters. We leverage Music Information Retrieval (MIR) models as well as a large collection of public loop samples in our study and use the Wave-U-Net architecture to map control parameters to audio. We also evaluate the quality of the generated audio and propose intuitive controls for composers to map the ideas in their minds to an audio loop.