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Acoustic scene classification in DCASE 2020 Challenge: generalization across devices and low complexity solutions

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arxiv 2005.14623 v2 pith:GQ6TNT5G submitted 2020-05-29 eess.AS

Toni Heittola , Annamaria Mesaros , Tuomas Virtanen This is my paper · ORCID
classification eess.AS
keywords challengeclassificationacousticdcasedevicesgeneralizationscenesolutions
verification ladder T0 review T1 audit T2 compute T3 formal
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This paper presents the details of Task 1: Acoustic Scene Classification in the DCASE 2020 Challenge. The task consists of two subtasks: classification of data from multiple devices, requiring good generalization properties, and classification using low-complexity solutions. Here we describe the datasets and baseline systems. After the challenge submission deadline, challenge results and analysis of the submissions will be added.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Auto-AEG: Scalable Data Construction for Open-Vocabulary Audio Event Grounding

    cs.SD 2026-07 conditional novelty 6.0 of 10

    Automatically constructed synthetic exact-GT data plus multi-model pseudo-labels with interval-aware GRPO rewards improve LALM open-vocabulary audio event grounding on AEGBench and DESED.

  2. Adaptive Knowledge Distillation using a Device-Aware Teacher for Low-Complexity Acoustic Scene Classification

    cs.SD 2025-09 conditional novelty 4.0 of 10

    A low-complexity scene classifier trained by two-teacher knowledge distillation and device-specific fine-tuning reaches 57.93% accuracy on the DCASE 2025 development set.

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