# You Only Hear Once: A YOLO-like Algorithm for Audio Segmentation and Sound Event Detection
## 6 Conclusions  
In this paper, we proposed a novel paradigm called YOHO for audio segmentation and sound event detection. It obtained state-of-the-art performance for music-speech detection and surpassed the CRNN and CNN's performance for environmental audio. YOHO presents sound event detection differently from the traditional segmentation-by-classification approach. We primarily adapted the MobileNet architecture [30] to develop the YOHO paradigm. Future developments in the network architecture for YOHO would lead to improvements in performance. For instance, adding skip connections through ResNets [56] or by including Inception blocks [57]. Furthermore, there is scope to create hybrid architectures such as CNN-transformers [55] by adopting the YOHO paradigm.  
Although YOHO's output is more end-to-end by predicting acoustic boundaries directly, it is limited by the time-resolution of the input, which is the mel spectrogram. It would be interesting to explore YOHO with raw audio, which would make the sound event detection pipeline completely end-to-end. Moreover, the YOHO approach is relevant to related tasks such as singing voice detection. Furthermore, recent studies have successfully combined sound event detection with source separation and semi-supervised learning [10; 58]. Future work could explore how YOHO would perform in these scenarios.  
**Author Contributions:** Conceptualization, S.V. and D.M.; methodology, S.V. and D.M.; software, S.V.; investigation, S.V.; writing--original draft preparation, S.V.; writing--review and editing, D.M. and S.V.; supervision, D.M. and E.R.M.; project administration, E.R.M.; funding acquisition, E.R.M. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This study was supported by Engineering and Physical Sciences Research Council (EPSRC) grant EP/S026991/1.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data provided by BBC Radio Devon is copyrighted material and cannot be shared. Synthetic radio data for music-speech detection can be generated by using techniques in this paper [17]. MuSpeak [38] is an openly available annotated dataset for music-speech detection, which can be utilised by researchers for validation and testing. TUT Sound Events 2017 and Urban-SED datasets are publicly available. The code associated with this paper is openly available in this GitHub repository ([[https://github.com/satvik-venkatesh/you-only-hear-once/](https://github.com/satvik-venkatesh/you-only-hear-once/)]([https://github.com/satvik-venkatesh/you-only-hear-once/](https://github.com/satvik-venkatesh/you-only-hear-once/)), accessed on 2 March 2022).  
**Acknowledgments:** The authors would like to thank [PERSON] for helping us evaluate our model on the MIREX music-speech competition dataset. We thank [PERSON] for providing insights on the Urban-SED dataset [9] and details of the results presented in Figure 3. Research in this paper was conducted on Google Colab and we are thankful for their service.  
**Conflicts of Interest:** The authors declare no conflict of interest.