Résultats de recherche
7 oct. 2024 · Sound event detection aims to identify sound event classes and their time boundaries in an audio recording. This task was also present in all the ten editions, with iterative changes.
24 sept. 2024 · PROJECT: National Research Project, Public body. AIM: Evaluate the average human ability to recognize sound event. RESPONSIBLE: Prof. Luca Fredianelli, Researcher at the Italian National Council of Research. DATA AVAILABILITY: Personal results will not be published. Research project will be published.
Il y a 2 jours · Polyphonic sound event detection refers to the task of automatically identifying sound events occurring simultaneously in an auditory scene. Due to the inherent complexity and variability of real-world auditory scenes, building robust detectors for polyphonic sound event detection poses a significant challenge. The task becomes furthermore challenging without sufficient annotated data to ...
8 oct. 2024 · My research includes tasks related to the audio generative model, source separation, quality enhancement, and recognition, appeared in journals and conferences such as TPAMI, TASLP, ICML, AAAI, NeurIPS, INTERSPEECH, and ICASSP. Highlighted research performed as the first author: Text-to-audio generation model: AudioLDM and AudioLDM2.
26 sept. 2024 · Prototype based Masked Audio Model for Self-Supervised Learning of Sound Event Detection. A significant challenge in sound event detection (SED) is the effective utilization of unlabeled data, given the limited availability of labeled data due to high annotation costs.
- arXiv:2409.17656 [cs.SD]
26 sept. 2024 · Overall, Nas’ sound has evolved significantly since his early days working with Large Professor on “Illmatic.” While his signature lyrical style has remained consistent throughout his career, Nas has continued to push the boundaries of his sound and collaborate with a wide range of producers and artists.
26 sept. 2024 · Prototype based Masked Audio Model for Self-Supervised Learning of Sound Event Detection. A significant challenge in sound event detection (SED) is the effective utilization of unlabeled data, given the limited availability of labeled data due to high annotation costs.
