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Unifying Probabilistic Models for Time-Frequency Analysis., , , , and . CoRR, (2018)Improved multiple birdsong tracking with distribution derivative method and Markov renewal process clustering., , , and . ICASSP, page 468-472. IEEE, (2013)End-to-End Probabilistic Inference for Nonstationary Audio Analysis., , , , and . ICML, volume 97 of Proceedings of Machine Learning Research, page 6776-6785. PMLR, (2019)Data-efficient weakly supervised learning for low-resource audio event detection using deep learning., and . DCASE, page 123-127. (2018)Few-Shot Bioacoustic Event Detection at the DCASE 2022 Challenge., , , , , , , , , and 5 other author(s). DCASE, Tampere University, (2022)Rank-Based Loss For Learning Hierarchical Representations., and . ICASSP, page 3623-3627. IEEE, (2022)Sparse Gaussian Process Audio Source Separation Using Spectrum Priors in the Time-domain., , and . ICASSP, page 995-999. IEEE, (2019)Maximum a Posteriori Estimation of Piecewise Arcs in Tempo Time-Series., and . CMMR, volume 7900 of Lecture Notes in Computer Science, page 387-399. Springer, (2012)Segregating event streams and noise with a Markov renewal process model., and . J. Mach. Learn. Res., 14 (1): 2213-2238 (2013)Bird detection in audio: A survey and a challenge., , , and . MLSP, page 1-6. IEEE, (2016)