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A Hybrid Approach with Multi-channel I-Vectors and Convolutional Neural Networks for Acoustic Scene Classification.

, , , and . CoRR, (2017)

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Derivation of watershed boundaries for GRDC gauging stations based on the HydroSHEDS drainage network. 41. Global Runoff Data Centre, Koblenz, (2012)On the reduction of false positives in singing voice detection., , and . ICASSP, page 7480-7484. IEEE, (2014)Zero-Mean Convolutions for Level-Invariant Singing Voice Detection., and . ISMIR, page 321-326. (2018)I-Vectors for Timbre-Based Music Similarity and Music Artist Classification., , , and . ISMIR, page 554-560. (2015)Acoustic Temperature Tomography using a UNet based Deep Learning Approach., , , , , and . EUSIPCO, page 1561-1565. IEEE, (2022)A low-latency, real-time-capable singing voice detection method with LSTM recurrent neural networks., , and . EUSIPCO, page 21-25. IEEE, (2015)Monaural Blind Source Separation in the Context of Vocal Detection., and . ISMIR, page 309-315. (2015)A Hybrid Approach with Multi-channel I-Vectors and Convolutional Neural Networks for Acoustic Scene Classification., , , and . CoRR, (2017)An Introduction to Signal Processing for Singing-Voice Analysis: High Notes in the Effort to Automate the Understanding of Vocals in Music., , , , , , , , , and 2 other author(s). IEEE Signal Process. Mag., 36 (1): 82-94 (2019)Surfing Virtual Waves to Thermal Tomography: From model- to deep learning-based reconstructions., , , , , and . IEEE Signal Process. Mag., 39 (1): 55-67 (2022)