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2 results for “Audio question answering”
MusiQAl: Music Question-Answering through Audio-Video Fusion
<p></p> <p>What are the most relevant questions to ask a machine about a music performance? MusiQAl is a dataset created and annotated by a research team at RITMO for the task of Music Question Answering (MQA).</p> <p><strong>Purpose</strong> The purpose of creating MusiQal is to address significant limitations in existing MQA datasets, which often focus only on instrument performance and overlook important qualitative characteristics such as interaction, performance energy, and audience engagement. MusiQAl bridges existing gaps by catering to three performance types: instrument, singing, and dancing. It introduces a new set of questions that capture nuanced aspects of music performance, inspired by music cognition theories and advancements in machine perception of music.</p> <p><strong>Utility</strong> Audio-video and question-answer pairs are all available in this repository. Extracted features are also provided, as well as the code for constructing and evaluating the dataset in SOTA models.</p> <p> </p> <p> </p>
Textless Audio Question Answering Dataset
<p>This repository contains audio clips, related speech-based questions, and a suitable answer to asked questions to prepare T-AQA(Textless Audio Question Answering Dataset). The dataset contains a total of 1991 audio clips and 18 pairs of speech-based questions and natural language answers for each audio clip. A separate train, validation, and test files are provided for training, validation, and test deep models for the TAQA task.</p> <p>The audio clips corresponding to questioned audio can be downloaded from the following link: </p> <p>https://zenodo.org/record/6473207#.YuWKbPvhXWs</p>
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