生物学和生态学中动物声音的研究在很大程度上依赖于时频(TF)可视化,最常用的是短时傅里叶变换(STFT)谱图。这种方法,然而,对时间或频谱细节具有固有的偏见,可能导致对复杂动物声音的误解。理想的TF可视化应该在频率和时间方面准确地传达声音的结构,然而,STFT通常不能满足这一要求。我们评估了四种TF可视化方法的准确性(超级小变换[SLT],连续小波变换[CWT]和两个STFT)使用合成测试信号。然后我们应用这些方法来想象查戈斯蓝鲸的声音,亚洲象,南部食典区,东方鞭鸟,马洛韦鱼和美国鳄鱼。我们表明,SLT可视化测试信号的误差比其他方法小18.48%-28.08%。我们对动物声音的可视化与文献描述之间的比较表明,STFT的偏见可能在描述侏儒蓝鲸的歌声和大象的隆隆声时引起了误解。我们建议使用SLT可视化低频动物声音可以防止这种误解。最后,我们使用SLT来开发\'BASSA\',一个开源的,提供无代码的GUI软件应用程序,用户友好的工具,用于分析Windows平台的低频动物声音的短期记录。SLT以更高的精度可视化低频动物声音,以用户友好的格式,最大限度地减少误解的风险,同时需要比STFT更少的技术专长。使用这种方法可以推动声学驱动的动物交流研究的进展,声乐制作方法,发声和物种鉴定。
The study of animal sounds in biology and ecology relies heavily upon time-frequency (TF) visualisation, most commonly using the short-time Fourier transform (STFT) spectrogram. This method, however, has inherent bias towards either temporal or spectral details that can lead to misinterpretation of complex animal sounds. An ideal TF visualisation should accurately convey the structure of the sound in terms of both frequency and time, however, the STFT often cannot meet this requirement. We evaluate the accuracy of four TF visualisation methods (superlet transform [SLT], continuous wavelet transform [CWT] and two STFTs) using a synthetic test signal. We then apply these methods to visualise sounds of the Chagos blue whale, Asian elephant, southern cassowary, eastern whipbird, mulloway fish and the American crocodile. We show that the SLT visualises the test signal with 18.48%-28.08% less error than the other methods. A comparison between our visualisations of animal sounds and their literature descriptions indicates that the STFT\'s bias may have caused misinterpretations in describing pygmy blue whale songs and elephant rumbles. We suggest that use of the SLT to visualise low-frequency animal sounds may prevent such misinterpretations. Finally, we employ the SLT to develop \'BASSA\', an open-source, GUI
software application that offers a no-code, user-friendly tool for analysing short-duration recordings of low-frequency animal sounds for the Windows platform. The SLT visualises low-frequency animal sounds with improved accuracy, in a user-friendly format, minimising the risk of misinterpretation while requiring less technical expertise than the STFT. Using this method could propel advances in acoustics-driven studies of animal communication, vocal production methods, phonation and species identification.