The music signal comprises of different features like rhythm, timbre, melody, harmony. Its impact on the human brain has been an exciting research topic for the past several decades. Electroencephalography (EEG) signal enables non-invasive measurement of brain activity. Leveraging the recent advancements in deep learning, we proposed a novel approach for song identification using a Convolution Neural network given the electroencephalography (EEG) responses. We recorded the EEG signals from a group of 20 participants while listening to a set of 12 song clips, each of approximately 2 minutes, that were presented in random order. The repeating nature of Music is captured by a data slicing approach considering brain signals of 1 second duration as representative of each song clip. More specifically, we predict the song corresponding to one second of EEG data when given as input rather than a complete two-minute response. We have also discussed pre-processing steps to handle large dimensions of a dataset and various CNN architectures. For all the experiments, we have considered each participant's EEG response for each song in both train and test data. We have obtained 84.96\% accuracy for the same. The performance observed gives appropriate implication towards the notion that listening to a song creates specific patterns in the brain, and these patterns vary from person to person.

中文翻译：

---

GuessTheMusic：从脑电图反应中识别歌曲

音乐信号包括不同的特征，如节奏、音色、旋律、和声。在过去的几十年里，它对人类大脑的影响一直是一个令人兴奋的研究课题。脑电图 (EEG) 信号可实现对大脑活动的非侵入性测量。利用深度学习的最新进展，我们提出了一种使用卷积神经网络根据脑电图 (EEG) 响应进行歌曲识别的新方法。我们记录了一组 20 名参与者的脑电图信号，同时听了一组 12 首歌曲片段，每个片段大约 2 分钟，以随机顺序呈现。音乐的重复特性是通过数据切片方法捕获的，考虑到 1 秒持续时间的大脑信号作为每个歌曲剪辑的代表。进一步来说，当作为输入而不是完整的两分钟响应时，我们预测对应于一秒 EEG 数据的歌曲。我们还讨论了处理大尺寸数据集和各种 CNN 架构的预处理步骤。对于所有实验，我们都考虑了每个参与者对训练和测试数据中每首歌曲的 EEG 响应。我们已经获得了 84.96\% 的准确率。观察到的表现适当暗示了听一首歌会在大脑中产生特定模式的概念，而这些模式因人而异。s 训练和测试数据中每首歌曲的 EEG 响应。我们已经获得了 84.96\% 的准确率。观察到的表现适当暗示了听一首歌会在大脑中产生特定模式的概念，而这些模式因人而异。s 训练和测试数据中每首歌曲的 EEG 响应。我们已经获得了 84.96\% 的准确率。观察到的表现适当暗示了听一首歌会在大脑中产生特定模式的概念，而这些模式因人而异。