Based on the real-world audio data recored by voyage data recorder (VDR), a voice activity detection (VAD) method based on constant-Q transform (CQT) and deep neural network (DNN) was proposed. In order to obtain the frequency conversion rate resolution suitable for different frequency bands, CQT was used to analyze the spectrum of VDR audio signal, and DNN was used to automatically learn the complex feature representation based on CQT amplitude spectrum to realize end-to-end voice endpoint detection of VDR audio data. The effectiveness of the proposed method was verified by real VDR audio data. Experimental results show that this method has high accuracy and robustness.