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Venkatesh, S, Moffat, D and Miranda, E 2022 You Only Hear Once: A YOLO-like Algorithm for Audio Segmentation and Sound Event Detection. Applied Sciences, 12 (7). 3293. https://doi.org/10.3390/app12073293
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applsci-12-03293-v2.pdf - Published Version Available under License Creative Commons Attribution. Download (1MB) | Preview |
Abstract/Summary
Audio segmentation and sound event detection are crucial topics in machine listening that aim to detect acoustic classes and their respective boundaries. It is useful for audio-content analysis, speech recognition, audio-indexing, and music information retrieval. In recent years, most research articles adopt segmentation-by-classification. This technique divides audio into small frames and individually performs classification on these frames. In this paper, we present a novel approach called You Only Hear Once (YOHO), which is inspired by the YOLO algorithm popularly adopted in Computer Vision. We convert the detection of acoustic boundaries into a regression problem instead of frame-based classification. This is done by having separate output neurons to detect the presence of an audio class and predict its start and end points. The relative improvement for F-measure of YOHO, compared to the state-of-the-art Convolutional Recurrent Neural Network, ranged from 1% to 6% across multiple datasets for audio segmentation and sound event detection. As the output of YOHO is more end-to-end and has fewer neurons to predict, the speed of inference is at least 6 times faster than segmentation-by-classification. In addition, as this approach predicts acoustic boundaries directly, the post-processing and smoothing is about 7 times faster.
| Item Type: | Publication - Article |
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| Additional Keywords: | audio segmentation; sound event detection; you only look once; deep learning; regression; convolutional neural network; music-speech detection; convolutional recurrent neural network; radio |
| Divisions: | Plymouth Marine Laboratory > Other (PML) |
| Depositing User: | S Hawkins |
| Date made live: | 04 Apr 2022 14:23 |
| Last Modified: | 04 Apr 2022 14:23 |
| URI: | https://plymsea.ac.uk/id/eprint/9660 |
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