分析声事件检测系统能耗趋势，发现训练更节能但系统越来越复杂。
Energy Consumption Trends in Sound Event Detection Systems
- 连续三年在DCASE挑战中加入能耗评估，推动绿色算法发展。
- 系统复杂度和计算量持续上升，但训练阶段能耗显著降低。
- 适合关注可持续AI与音频感知的科研人员参考。
深度学习系统日益消耗大量能源和计算资源，引发对其环境影响的担忧。作为声景与声事件检测（DCASE）挑战赛的组织方，我们认识到解决这一问题的重要性。过去三年，我们在声事件检测（SED）系统的评估中引入了能耗指标。本文分析了该能耗标准对竞赛结果的影响，并探讨了系统复杂度与能耗随时间的变化趋势。研究发现，尽管系统操作数和复杂度持续增长，但训练过程中的能效显著提升，表现出向更节能方法转变的趋势。我们希望通过此分析，推动声事件检测领域采取更具环境友好性的实践。
原文摘要 · Abstract (English)
Deep learning systems have become increasingly energy- and computation-intensive, raising concerns about their environmental impact. As organizers of the Detection and Classification of Acoustic Scenes and Events (DCASE) challenge, we recognize the importance of addressing this issue. For the past three years, we have integrated energy consumption metrics into the evaluation of sound event detection (SED) systems. In this paper, we analyze the impact of this energy criterion on the challenge results and explore the evolution of system complexity and energy consumption over the years. We highlight a shift towards more energy-efficient approaches during training without compromising performance, while the number of operations and system complexity continue to grow. Through this analysis, we hope to promote more environmentally friendly practices within the SED community.
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