Ping-Rui Tsai

Ping-Rui Tsai, Yen-Ting Chou, Nathan-Christopher Wang et al.

Music, enchanting and poetic, permeates every corner of human civilization. Although music is not unfamiliar to people, our understanding of its essence remains limited, and there is still no universally accepted scientific description. This is primarily due to music being regarded as a product of both reason and emotion, making it difficult to define. In this article, we focus on the fundamental elements of music and construct an evolutionary network from the perspective of music as a natural language, aligning with the statistical characteristics of texts. Through this approach, we aim to comprehend the structural differences in music across different periods, enabling a more scientific exploration of music. Relying on the advantages of structuralism, we can concentrate on the relationships and order between the physical elements of music, rather than getting entangled in the blurred boundaries of science and philosophy. The scientific framework we present not only conforms to past conclusions in music, but also serves as a bridge that connects music to natural language processing and knowledge graphs.

Ping-Rui Tsai, Chi-hsiang Wang, Yu-Cheng Liao et al.

Images, as a product evolving alongside civilization, develop similarly to natural languages with the advancement of civilization. Not only are images abundant in daily life, but are also influenced by technology in shaping their forms, embodying various characteristics as they evolve in time. Language is a sequence of symbols that represents thoughts. While a written language is typically associated with the close integration of text and sound, as a combination of visual symbols and perception, the communicative power of image is no less significant. This is especially notable since 60% of the sensory input received by our central nervous system comes from vision. Given the symbolic system inherent in images, we are curious whether images can also exhibit the laws of statistical linguistics. To explore this, we begin with the relationship between human thought and visual perception to decode how images are formed by the latter mechanism. Building upon previous studies that established the high correlation between pre-trained deep convolutional neural networks and the human visual system, we use the VGG-19 to define words via each kernel and calculate the number of pixels with grayscale values greater than 90%. By (a) ranking words frequency, (b) randomizing the order of kernel appearances and performing the same word count accumulation, and (c) summing the word counts layer by layer, we are surprised to find that Zipf's, Heaps', and Benford's laws of statistical linguistics also exist in the words that comprises the text representing different images.