19,997 machine learning datasets
19,997 dataset results
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$\textbf{VocSim (Vocal Similarity Benchmark)}$ is a benchmark designed to evaluate the ability of neural audio embeddings to capture acoustic and perceptual similarity in a $\textbf{zero-shot setting}$, without task-specific fine-tuning. It addresses the challenge of creating audio representations that $\textbf{generalize across diverse sound types}$, aiming to mirror the flexibility and nuanced sensitivity of biological auditory systems. The benchmark is built upon the diverse $\textbf{VocSim dataset}$, comprising $\textbf{125,382 audio clips}$ aggregated from 19 distinct sources. This includes Human Speech (phones, words, utterances, and non-verbal sounds from multiple languages, including specific blind test subsets from indigenous languages), Animal Vocalizations (songbird syllables and calls like zebra finch, Bengalese finch, canary, and giant otter calls), and Environmental Sounds (everyday environmental noises from ESC-50). The dataset is curated into these 19 subsets to stress
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UAV-Gesture
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This dataset contains electroencephalogram (EEG) signals, event-related potentials (ERP), and demographic attributes aimed at the early identification of schizophrenia. EEG signals and ERP data capture neural and cognitive markers that aid in distinguishing individuals with schizophrenia from healthy controls. Demographic factors such as age, gender, education level, and other relevant attributes are included to enhance the predictive capability of machine learning models. This comprehensive dataset enables researchers to explore and validate novel machine learning techniques for diagnostic purposes, contributing to advancements in the early detection and understanding of schizophrenia.
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