486 machine learning datasets
486 dataset results
$\textbf{VocSim (Vocal Similarity Benchmark)}$ 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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We introduce FortisAVQA, a dataset designed to assess the robustness of AVQA models. Its construction involves two key processes: rephrasing and splitting. Rephrasing modifies questions from the test set of MUSIC-AVQA to enhance linguistic diversity, thereby mitigating the reliance of models on spurious correlations between key question terms and answers. Splitting entails the automatic and reasonable categorization of questions into frequent (head) and rare (tail) subsets, enabling a more comprehensive evaluation of model performance in both in-distribution and out-of-distribution scenarios.
The Storytelling Video Dataset is a high-quality, human-reviewed multimodal dataset featuring over 700 full-body video recordings of native Russian speakers. Each video is 10+ minutes long and includes synchronized speech, facial expressions, gestures, and emotional variation. The dataset is ideal for research and development in:
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Dataset Summary The Deep Evaluation of Audio Representations (DEAR) dataset is a benchmark designed to assess general-purpose audio foundation models on properties critical for hearable devices. It comprises 1,158 mono audio tracks (30 s each), spatially mixing proprietary anechoic speech monologues with high-quality everyday acoustic scene recordings from the HOA‑SSR library. DEAR enables controlled evaluation of: