“马”的内部：寻找音乐内容分析系统行为背后的原因
The "Horse'' Inside: Seeking Causes Behind the Behaviours of Music Content Analysis Systems
摘要：
构建具有识别和描述音乐音频信号中的高级属性的灵敏度和智能的系统仍然是一个难以捉摸的目标，但肯定会对各种应用产生广泛而深刻的影响。到目前为止，已经有数百篇论文朝着这个目标发表，而且似乎已经取得了很大的进展。一些系统在识别高级语义概念（如音乐风格，流派和情绪）方面具有显着的准确性。然而，这些数字可能并不意味着它们看起来的那样。在本文中，我们采用了一个最先进的音乐内容分析系统，并研究了是什么原因使它在基准音乐音频数据集中实现了极高的性能。我们解剖这个系统，以了解它的运作，确定它的敏感性和局限性，并预测它可以和不可以拥有的关于音乐的知识。我们进行了一系列的实验，以阐明系统实际上已经学会了做什么，以及它在多大程度上执行预期的音乐聆听任务。我们的研究结果表明，在这种最先进的音乐智能的最初表现是如何具有欺骗性的。我们的工作为开发音乐内容分析系统提供了建设性的方向，这些系统可以解决现实世界用户的音乐信息和创作需求。
英文摘要：
Building systems that possess the sensitivity and intelligence to identify and describe high-level attributes in music audio signals continues to be an elusive goal, but one that surely has broad and deep implications for a wide variety of applications. Hundreds of papers have so far been published toward this goal, and great progress appears to have been made. Some systems produce remarkable accuracies at recognising high-level semantic concepts, such as music style, genre and mood. However, it might be that these numbers do not mean what they seem. In this paper, we take a state-of-the-art music content analysis system and investigate what causes it to achieve exceptionally high performance in a benchmark music audio dataset. We dissect the system to understand its operation, determine its sensitivities and limitations, and predict the kinds of knowledge it could and could not possess about music. We perform a series of experiments to illuminate what the system has actually learned to do, and to what extent it is performing the intended music listening task. Our results demonstrate how the initial manifestation of music intelligence in this state-of-the-art can be deceptive. Our work provides constructive directions toward developing music content analysis systems that can address the music information and creation needs of real-world users.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
