论文标题
使用无监督的HEBBIAN计算，通过单层Feelforward网络检测通用音乐功能
Detecting Generic Music Features with Single Layer Feedforward Network using Unsupervised Hebbian Computation
论文作者
论文摘要
通过流行的在线音乐流媒体软件和应用程序的数量不断增加的数字音乐和庞大的音乐曲目功能，使用神经网络的功能识别被用于实验，以在最近的各种实验中产生广泛的结果。通过这项工作，作者通过使用同一数据集在其单层神经网络上应用无监督的HEBBIAN学习技术来提取流行的开源音乐语料库中的此类功能的信息，并探索了新的识别技术。作者展示了详细的经验发现，以模拟这种算法如何帮助单层馈电网络在音乐功能学习作为模式的培训中。无监督的培训算法增强了他们提出的神经网络，以达到成功的音乐功能检测的准确性90.36％。为了针对类似任务进行比较分析，作者将其结果与以前的几种基准作品相似。他们进一步讨论了对工作的局限性和彻底的错误分析。作者希望发现和收集有关这种特定分类技术及其性能的新信息，并进一步了解可以改善计算音乐功能识别艺术的未来潜在方向和前景。
With the ever-increasing number of digital music and vast music track features through popular online music streaming software and apps, feature recognition using the neural network is being used for experimentation to produce a wide range of results across a variety of experiments recently. Through this work, the authors extract information on such features from a popular open-source music corpus and explored new recognition techniques, by applying unsupervised Hebbian learning techniques on their single-layer neural network using the same dataset. The authors show the detailed empirical findings to simulate how such an algorithm can help a single layer feedforward network in training for music feature learning as patterns. The unsupervised training algorithm enhances their proposed neural network to achieve an accuracy of 90.36% for successful music feature detection. For comparative analysis against similar tasks, authors put their results with the likes of several previous benchmark works. They further discuss the limitations and thorough error analysis of their work. The authors hope to discover and gather new information about this particular classification technique and its performance, and further understand future potential directions and prospects that could improve the art of computational music feature recognition.
