Chime-home: A dataset for sound source recognition in a domestic environment

By Peter Foster, Siddharth Sigtia, Sacha Krstulović, Jon Barker, Mark D. Plumbley. 2015

107 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Signal Processing

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Citations in the corpus

Speech Denoising with Deep Feature Losses (2018, methodology): “For the tagging task, the training set CHiMEHome-refine [38] consists of 4-second-long mono audio files sampled at 16 kHz, with 7 different tags (i.”
FSD50K: An Open Dataset of Human-Labeled Sound Events (2020, methodology): “UrbanSound8K and CHiME-home count with a signiﬁcant amount of clips per class; nonetheless, part of this abundance comes from the fact that many clips are actually time slices coming from the same original recording.”
Detection and Classification of Acoustic Scenes and Events: Outcome of the DCASE 2016 Challenge (2018, methodology): “1) Audio recordings: The CHiME-Home dataset consists of approx.”
Learning Sound Event Classifiers from Web Audio with Noisy Labels (2019, background): “Although some sound event datasets are exhaustively labeled, e.g., [1, 2, 3] their size is limited (e.g., less than 9h of audio).”
Audio tagging with noisy labels and minimal supervision (2019, background): “The most recent datasets feature larger sizes [2, 3, 4], but their labeling is less precise than that of conventional small and exhaustively labeled datasets [5, 6, 7].”
General-purpose Tagging of Freesound Audio with AudioSet Labels: Task Description, Dataset, and Baseline (2018, methodology): “In DCASE 2016 [3], the task targeted domestic audio tagging for which the CHiME-Home dataset was used, including 7 sound categories and 6.8h of recordings [4].”
Audio Retrieval with Natural Language Queries (2021, unknown): “These include TUT Acoustic scenes [14], CHIME-Home [15], ESC-50 [16], FSDKaggle [17], and AudioSet [10].”
Freesound Datasets: A Platform for the Creation of Open Audio Datasets (2017, background): “While we believe that annotator agreement is required for deﬁning ground truth based on human-sourced annotations [8], it is also true that a number of datasets do not meet this condition [24,23,39].”
Model-Agnostic Approaches To Handling Noisy Labels When Training Sound Event Classifiers (2019, background): “In the less explored field of sound event recognition, we are currently moving from small and exhaustively labeled datasets of few hours of duration [1, 2, 3], towards larger datasets in the range of tens (e.”
Metrics for Polyphonic Sound Event Detection (2016, methodology): “Possible learning approaches for this step include Gaussian mixture models [25], hidden Markov models [26], non-negative dictionaries [17], deep neural networks [18], etc.”
Addressing Missing Labels in Large-Scale Sound Event Recognition Using a Teacher-Student Framework With Loss Masking (2020, background): “A S SOUND Event Recognition (SER) has gained attention in recent years [1], research in this field has moved from small datasets encompassing few hours of audio [2]–[5], to larger datasets with much greater coverage and duration [6], [7].”
Convolutional Recurrent Neural Networks for Polyphonic Sound Event Detection (2017, unknown): “d) CHiME-Home: CHiME-Home dataset [39] consists of 4second audio chunks from home environments.”