5 citations · 9 across the 8 of their papers we have counts for
8 papers
Learning-based estimation of in-situ wind speed from underwater acoustics
Matteo Zambra, Dorian Cazau, Nicolas Farrugia +4
Wind speed retrieval at sea surface is of primary importance for scientific and operational applications. Besides weather models, in-situ measurements and remote sensing technologi…
Development details and computational benchmarking of DEPAM
Paul Nguyen Hong Duc, Dorian Cazau
In the big data era of observational oceanography, passive acoustics datasets are becoming too high volume to be processed on local computers due to their processor and memory limi…
Theory-plus-code documentation of the DEPAM workflow for soundscape description
D. Cazau
In the Big Data era, the community of PAM faces strong challenges, including the need for more standardized processing tools accross its different applications in oceanography, and…
Calibration of a two-state pitch-wise HMM method for note segmentation in Automatic Music Transcription systems
Dorian Cazau, Yuancheng Wang, Olivier Adam +2
Many methods for automatic music transcription involves a multi-pitch estimation method that estimates an activity score for each pitch. A second processing step, called note segme…
Investigation on the use of Hidden-Markov Models in automatic transcription of music
D. Cazau, G. Nuel
Hidden Markov Models (HMMs) are a ubiquitous tool to model time series data, and have been widely used in two main tasks of Automatic Music Transcription (AMT): note segmentation,…
Bi-class classification of humpback whale sound units against complex background noise with Deep Convolution Neural Network
Cazau Dorian, Riwal Lefort, Julien Bonnel +2
Automatically detecting sound units of humpback whales in complex time-varying background noises is a current challenge for scientists. In this paper, we explore the applicability…
Particle Filtering for PLCA model with Application to Music Transcription
D. Cazau, G. Revillon, W. Yuancheng +1
Automatic Music Transcription (AMT) consists in automatically estimating the notes in an audio recording, through three attributes: onset time, duration and pitch. Probabilistic La…
Understanding the Probabilistic Latent Component Analysis Framework
D. Cazau, G. Nuel
Probabilistic Component Latent Analysis (PLCA) is a statistical modeling method for feature extraction from non-negative data. It has been fruitfully applied to various research fi…