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Workshop: Efficient Natural Language and Speech Processing (Models, Training, and Inference)

Evaluating robustness of You Only Hear Once(YOHO) Algorithm on noisy audios in the VOICe Dataset

Soham Tiwari · Manjunath Mulimani


Sound event detection (SED) in machine listening entails identifying the different sounds in an audio file and identifying the start and end time of a particular sound event in the audio file. SED finds use in various applications such as audio surveillance \cite{1audio-surveillance}, speech recognition, and context-based indexing and retrieval of data in a multimedia database \cite{2sound-event-detection}. However, in real-life scenarios, the audios from various sources are seldom devoid of any interfering noise or disturbance. In this paper, we test the performance of the new You Only Hear Once (YOHO) \cite{3yoho} algorithm on noisy audio data. The YOHO algorithm takes inspiration from the famous You Only Look Once (YOLO) \cite{7yolo} algorithms in computer vision. The YOHO algorithm can match the performance of the various state-of-the-art algorithms on datasets such as Music Speech Detection Dataset \cite{5musicspeech}, TUT Sound Event \cite{6TUT}, and Urban-SED datasets but at lower inference times. However, like many other popular SED algorithms, YOHO was mainly tested on clean audio data without any external noise. Hence, in this paper, we explore the performance of the YOHO algorithm on the VOICe dataset\cite{4voice} containing audio files with noise at different sound-to-noise ratios (SNR). YOHO can outperform or at least match the best performing SED algorithms reported in the VOICe dataset paper and make inferences in less time.

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