Speaker Separation

Continuous speech separation (CSS) is an approach to handling overlapped speech in conversational audio signals. A real recorded dataset, called LibriCSS, is derived from LibriSpeech by concatenating the corpus utterances to simulate a conversation and capturing the audio replays with far-field microphones. Source: https://github.com/chenzhuo1011/libri_css

The FSDnoisy18k dataset is an open dataset containing 42.5 hours of audio across 20 sound event classes, including a small amount of manually-labeled data and a larger quantity of real-world noisy data. The audio content is taken from Freesound, and the dataset was curated using the Freesound Annotator. The noisy set of FSDnoisy18k consists of 15,813 audio clips (38.8h), and the test set consists of 947 audio clips (1.4h) with correct labels. The dataset features two main types of label noise: in-vocabulary (IV) and out-of-vocabulary (OOV). IV applies when, given an observed label that is incorrect or incomplete, the true or missing label is part of the target class set. Analogously, OOV means that the true or missing label is not covered by those 20 classes. Source: Model-agnostic Approaches to Handling Noisy Labels When Training Sound Event Classifiers Image Source: http://www.eduardofonseca.net/FSDnoisy18k/

Here we release the dataset (Multi_Channel_Grid, abbreviated as MC_Grid) used in our paper [LIMUSE: LIGHTWEIGHT MULTI-MODAL SPEAKER EXTRACTION]([2111.04063] LiMuSE: Lightweight Multi-modal Speaker Extraction (arxiv.org)). MC_Grid, which is based on GRID dataset, includes multi-channel audio, extracted voiceprint and visual feature. The method of feature extraction will be introduced below. MC_Grid is specially prepared for speaker extraction task, and our code is available at aispeech-lab/LiMuSE. Feel free to contact us if you have any questions or suggestions.