论文标题
鸟类活动检测中可学习的声学前端
Learnable Acoustic Frontends in Bird Activity Detection
论文作者
论文摘要
自主记录单元和被动声音监测当前收集生物声学数据的最小侵入性方法。将这些数据与物种不可知论的鸟类活动检测系统相结合，可以监测鸟类种群的活性水平。不幸的是，环境噪声水平和受试者距离的变化有助于在记录中准确检测鸟类活动的困难。声学前端的选择直接影响这些问题对系统性能的影响。在本文中，我们使用DCASE2018 BAD挑战中的数据基于在广泛的鸟音频检测任务上对新一代可学习的前端进行基准的固定参数原声前端。我们观察到，每通道能量归一化是最好的总体表现者，其准确性为89.9％，并且一般可以学习的前端大大优于传统方法。我们还确定了学习鸟音频的滤网挑战。
Autonomous recording units and passive acoustic monitoring present minimally intrusive methods of collecting bioacoustics data. Combining this data with species agnostic bird activity detection systems enables the monitoring of activity levels of bird populations. Unfortunately, variability in ambient noise levels and subject distance contribute to difficulties in accurately detecting bird activity in recordings. The choice of acoustic frontend directly affects the impact these issues have on system performance. In this paper, we benchmark traditional fixed-parameter acoustic frontends against the new generation of learnable frontends on a wide-ranging bird audio detection task using data from the DCASE2018 BAD Challenge. We observe that Per-Channel Energy Normalization is the best overall performer, achieving an accuracy of 89.9%, and that in general learnable frontends significantly outperform traditional methods. We also identify challenges in learning filterbanks for bird audio.
