You Only Hear Once: A YOLO-like Algorithm for Audio Segmentation and Sound Event Detection

• URL: http://arxiv.org/abs/2109.00962v1
• Date: Wed, 1 Sep 2021 12:50:16 GMT
• Title: You Only Hear Once: A YOLO-like Algorithm for Audio Segmentation and Sound Event Detection
• Authors: Satvik Venkatesh, David Moffat, Eduardo Reck Miranda
• Abstract summary: We present a novel approach called You Only Hear Once (YOHO) We convert the detection of acoustic boundaries into a regression problem instead of frame-based classification. YOHO obtained a higher F-measure and lower error rate than the state-of-the-art Convolutional Recurrent Neural Network.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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. YOHO obtained a higher F-measure and lower error rate than the state-of-the-art Convolutional Recurrent Neural Network on multiple datasets. As YOHO is purely a convolutional neural network and has no recurrent layers, it is faster during inference. In addition, as this approach is more end-to-end and predicts acoustic boundaries directly, it is significantly quicker during post-processing and smoothing.

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