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Deep Archetypal Analysis Based Intermediate Matching Kernel for Bioacoustic Classification.

, and . J. Sel. Topics Signal Processing, 13 (2): 298-309 (2019)

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From single to multiple enrollment i-vectors: Practical PLDA scoring variants for speaker verification., , , and . Digit. Signal Process., (2014)Archetypal analysis based sparse convex sequence kernel for bird activity detection., , , , , and . EUSIPCO, page 1774-1778. IEEE, (2017)Multiscale CNN based Deep Metric Learning for Bioacoustic Classification: Overcoming Training Data Scarcity Using Dynamic Triplet Loss., , , and . CoRR, (2019)Deep Archetypal Analysis Based Intermediate Matching Kernel for Bioacoustic Classification., and . J. Sel. Topics Signal Processing, 13 (2): 298-309 (2019)Directional Embedding Based Semi-supervised Framework For Bird Vocalization Segmentation., and . CoRR, (2019)Convex likelihood alignments for bioacoustic Classification., , and . MLSP, page 1-6. IEEE, (2018)APE: Archetypal-Prototypal Embeddings for Audio Classification., , and . MLSP, page 1-6. IEEE, (2018)Single versus Multi-Source Discrimination in Birdcalls using Zero-Frequency Filtering., , , and . NCC, page 1-6. IEEE, (2019)Multi-layer perceptron based speech activity detection for speaker verification., , and . WASPAA, page 321-324. IEEE, (2011)Sparse Representation Frameworks for Acoustic Scene Classification., and . SPECOM (1), volume 14338 of Lecture Notes in Computer Science, page 177-188. Springer, (2023)