# You Only Hear Once: A YOLO-like Algorithm for Audio Segmentation and Sound Event Detection
## 1 Introduction  
Audio segmentation and sound event detection have similar goals--to detect acoustic classes and their respective boundaries within an audio stream. They provide information regarding the content of audio and the temporal occurrences of audio events. It is helpful for indexing audio archives, target-based distribution of media, and as a pre-processing step for speech recognition [1]. In addition, detecting audio events in real-time is beneficial for self-driving automobiles [2], surveillance [3], bioacoustic monitoring [4], and intelligent remixing [5]. The literature has commonly adopted two approaches to audio segmentation--(1) distance-based segmentation and (2) segmentation-by-classification [6]. The first approach directly finds regions of high acoustic change through Euclidean distance or Bayesian information criterion [7]. The method divides audio into segments based on the peaks of acoustic change. Subsequently, the audio classes within each of these segments are detected. However, recent research has generally adopted the second approach, which is segmentation-by-classification. It presents sound event detection as a supervised learning task. This approach divides an audio file into frames, typically in the range of 10-25 ms, and classifies each frame individually. Effectively, we detect the onset and offset of each audio event by classification. Data to train a machine learning model for event detection require precise labels that mention the acoustic boundaries and classes. Annotating such datasets is a time-consumingand expensive process. Therefore, researchers have explored data-centric approaches such as artificial data synthesis to generate large-scale training data [8; 9]. Furthermore, researchers have explored weak label and semi-supervised learning to tackle the scarcity of labelled data [10; 11]. Datasets such as AudioSet [12] are annotated with weak labels, which indicates that a sound event is present in the audio clip, but does not specify the timing within the audio. [PERSON] et al. [13] emphasised the benefit of temporally strong labels to improve the performance of audio classifiers. The architectures for audio segmentation have evolved from traditional machine learning models such as the Gaussian mixture model to deep neural networks. Bidirectional Long short-term memory (B-LSTM) networks have been effective in segmenting temporal data [14]. [PERSON] et al. [15] showed that the non-causal temporal convolutional neural network was more effective than the B-LSTM. However, the Convolutional Recurrent Neural Network (CRNN) obtains state-of-the-art performance on many sound event detection datasets because it combines the advantage of 2D convolutions and recurrent layers [16; 17]. There has been a growing interest in the community to adopt end-to-end deep learning for information retrieval from audio. Raw audio waveforms have been explored instead of features such as mel spectrograms for the input [18; 19]. However, with regards to an end-to-end setup, there has been less attention given to the output of such networks. Traditionally, in segmentation-by-classification, the neural network classifies each audio frame. Subsequently, a post-processing step converts the neural network's output into human-readable labels. The disadvantage is that this post-processing is slow because each audio frame has to be serially processed. Therefore, in an ideal end-to-end setup, the neural network would output human-readable labels by directly predicting the boundaries of acoustic classes. In order to output human-readable labels directly, sound event detection must be transformed from a classification problem to a regression problem. [PERSON] et al. [20] proposed random regression forests for sound event detection and classification. [PERSON] et al. [21] adopted a regression approach for speech enhancement. However, most studies in the literature adopt frame-based classification, where the neural network classifies each frame separately.