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

Abstract

For the task of sound source recognition, we introduce a novel data set based on 6.8 hours of domestic environment audio recordings. We describe our approach of obtaining annotations for the recordings. Further, we quantify agreement between obtained annotations. Finally, we report baseline results for sound source recognition using the obtained dataset. Our annotation approach associates each 4-second excerpt from the audio recordings with multiple labels, based on a set of 7 labels associated with sound sources in the acoustic environment. With the aid of 3 human annotators, we obtain 3 sets of multi-label annotations, for 4378 4-second audio excerpts. We evaluate agreement between annotators by computing Jaccard indices between sets of label assignments. Observing varying levels of agreement across labels, with a view to obtaining a representation of 'ground truth' in annotations, we refine our dataset to obtain a set of multi-label annotations for 1946 audio excerpts. For the set of 1946 annotated audio excerpts, we predict binary label assignments using Gaussian mixture models estimated on MFCCs. Evaluated using the area under receiver operating characteristic curves, across considered labels we observe performance scores in the range 0.76 to 0.98.Dataset URL: http://archive.org/details/chime-home

Metrics for Polyphonic Sound Event Detection

“…One recent study on inter-annotator agreement is presented in [25], for tagging of audio recorded in a home environment. Their annotation approach associated multiple labels to each 4-s segment from the audio recordings, based on a set of 7 labels associated with sound sources present.…”

“…Audio containing multiple, possibly overlapping sounds can be classified into multiple classes-performing audio tagging, illustrated in Figure 1B. Tagging of audio with sound event labels is used for example for improving the tags of Freesound audio samples [24], and has been proposed as an approach for audio surveillance of home environments [25]. Single-label classification is equivalent with tagging, when a single tag is assigned per test file.…”

“…The audio is processed in short frames of typically 20-200 ms to extract the audio features of choice. Often used in sound event detection are representations of the signal spectrum, such as mel-frequency cepstral coefficients [25,26], mel energies [17,27], or simply the amplitude or power spectrum [18,28]. The audio processing chain may include simple preprocessing of audio such as normalization and pre-emphasis before feature extraction, or more complex preprocessing such as sound source separation and acoustic stream selection for reducing the complexity of audio mixtures used in training [16].…”

“…Possible learning approaches for this step include Gaussian mixture models [25], hidden Markov models [26], non-negative dictionaries [17], deep neural networks [18], etc. For testing, an audio recording goes through the same preprocessing and feature extraction process as applied in the training stage.…”

Convolutional Recurrent Neural Networks for Polyphonic Sound Event Detection

“…d) CHiME-Home: CHiME-Home dataset [39] consists of 4-second audio chunks from home environments. The annotations are based on seven sound classes, namely child speech, adult male speech, adult female speech, video game / TV, percussive sounds, broadband noise and other identifiable sounds.…”