The verses of Rabindranath Tagore have been sung by various artistes over generations spanning over almost 100 years. There are few songs which were popular in the early years and have been able to retain their popularity over the years while some others have faded away in the course of time. In this study we tried to find cues in the singing style of these songs, sung by different singers spanning over almost five generations, which have kept them alive for all these years. For this, we took 3 min clips of four Tagore songs which are being sung by atleast five generations of artistes over 100 years and analyzed the acoustic signals with the help of latest nonlinear technique Multifractal Detrended Fluctuation Analysis (MFDFA). Next EEG data was collected from 5 persons who listened to 30 sec clips of two Tagore songs sung over five generations of artistes in chronological order. The EEG response from the participants were analyzed with the help of the same MFDFA technique and the multifractal spectral width was considered as the parameter which can help in the identification of cognitive evolution of the Tagore songs. The multifractal spectral width is a manifestation of the inherent complexity of the signal and in future, may prove to be an important parameter to identify the singing style of a particular generation of singers and how this style varies over different generations. The EEG responses from the participants reflect how the perception and cognition of the same Tagore songs evolve over generations. The results and implications are discussed in detail.

拉宾德拉纳特·泰戈尔（Rabindranath Tagore）的诗句已经有近100年的历史了，由许多艺术家演唱。早期流行的歌曲寥寥无几，多年来一直保持流行，而其他歌曲则逐渐消失。在这项研究中，我们试图找到这些歌曲的线索，这些线索是由跨越五代人的不同歌手演唱的，这些年来它们一直活着。为此，我们用了3分钟的片段，拍摄了100年来至少五代艺人所演唱的四首泰戈尔歌曲，并借助最新的非线性技术多分形去趋势波动分析（MFDFA）分析了声信号。接下来的EEG数据是从5个人中收集的，他们按时间顺序听了五代艺术家演唱的两首泰戈尔歌曲的30秒片段。使用相同的MFDFA技术分析了参与者的脑电图响应，并将多重分形谱宽度视为可以帮助识别泰戈尔歌曲认知演变的参数。多重分形频谱宽度是信号固有复杂性的体现，在将来，它可能被证明是识别特定一代歌手的演唱风格以及该风格在不同世代之间如何变化的重要参数。参与者的脑电图反应反映出相同的泰戈尔歌曲的感知和认知如何历代发展。结果和含义进行了详细讨论。