Understanding the structural and cognitive underpinnings of musical compositions remains a key challenge in music theory and computational musicology. While traditional methods focus on harmony and rhythm, cognitive models such as the Implication-Realization (I-R) model and Temporal Gestalt theory offer insight into how listeners perceive and anticipate musical structure. This study presents a graph-based computational approach that operationalizes these models by segmenting melodies into perceptual units and annotating them with I-R patterns. These segments are compared using Dynamic Time Warping and organized into k-nearest neighbors graphs to model intra- and inter-segment relationships. Each segment is represented as a node in the graph, and nodes are further labeled with melodic expectancy values derived from Schellenberg's two-factor I-R model-quantifying pitch proximity and pitch reversal at the segment level. This labeling enables the graphs to encode both structural and cognitive information, reflecting how listeners experience musical tension and resolution. To evaluate the expressiveness of these graphs, we apply the Weisfeiler-Lehman graph kernel to measure similarity between and within compositions. Results reveal statistically significant distinctions between intra- and inter-graph structures. Segment-level analysis via multidimensional scaling confirms that structural similarity at the graph level reflects perceptual similarity at the segment level. Graph2vec embeddings and clustering demonstrate that these representations capture stylistic and structural features that extend beyond composer identity. These findings highlight the potential of graph-based methods as a structured, cognitively informed framework for computational music analysis, enabling a more nuanced understanding of musical structure and style through the lens of listener perception.

Incorporating factual knowledge in knowledge graph is regarded as a promising approach for mitigating the hallucination of large language models (LLMs). Existing methods usually only use the user's input to query the knowledge graph, thus failing to address the factual hallucination generated by LLMs during its reasoning process. To address this problem, this paper proposes Knowledge Graph-based Retrofitting (KGR), a new framework that incorporates LLMs with KGs to mitigate factual hallucination during the reasoning process by retrofitting the initial draft responses of LLMs based on the factual knowledge stored in KGs. Specifically, KGR leverages LLMs to extract, select, validate, and retrofit factual statements within the model-generated responses, which enables an autonomous knowledge verifying and refining procedure without any additional manual efforts. Experiments show that KGR can significantly improve the performance of LLMs on factual QA benchmarks especially when involving complex reasoning processes, which demonstrates the necessity and effectiveness of KGR in mitigating hallucination and enhancing the reliability of LLMs.

The uncanny valley is the abrupt dip in human affinity to a non-human creature when we see it approaching human-like characteristics. For instance, the spooky feeling when one looks at Sofia the robot or Lil Miquela the Instagram influencer. Really though, Lil Miquela gives me the creeps when I go through her timeline. It is the eeriness of a realistic face with personalized captions with her sense of awareness that she is not a real person that is quite unsettling. There is something surreal about it that makes working with it exciting.

A few months back, I wrote a medium article on BERT, which talked about its functionality and use-case and its implementation through Transformers. In this article, we will look at how we can use BERT for answering our questions based on the given context using Transformers from Hugging Face. Suppose the question asked is: Who wrote the fictionalized "Chopin?" and you are given with the context: Possibly the first venture into fictional treatments of Chopin's life was a fanciful operatic version of some of its events. Chopin was written by Giacomo Orefice and produced in Milan in 1901. All the music is derived from that of Chopin.

Listening to classical music during lectures and throughout the night while sleeping may help us perform better in big exams, a new study suggests. US economics students who listened to Beethoven and Chopin during a lecture and again later in the night performed 18 per cent higher in exams the next day. This compared with a control group of students who were in the same lecture but slept that night with white noise on in the background. Researchers say that classical music activated a process called'targeted memory reactivation' (TMR), when the music stimulates the brain to consolidate memories. The study suggests classical music is the key to strengthening existing memories of lectures during sleep and, as a result, doing better in exams.

A computer program is presented that learns to play piano with "expression" and that even won an international computer piano performance contest. A superficial analysis of an expressive performance generated by the system seems to suggest creative musical abilities. After a critical discussion of the processes underlying this behavior, we abandon the question of whether the system is really creative and turn to the true motivation that drives this research: to use AI methods to investigate and better understand music performance as a human creative behavior. A number of recent and current results from our research are briefly presented that indicate that machines can give us interesting insights into such a complex creative behavior, even if they may not be creative themselves. A computer program is to play two piano pieces that it has never seen before in an "expressive" way (that is, by shaping tempo, timing, dynamics, and articulation in such a way that the performances sound "musical" or "human").

Generating speech from a piece of text is a common and important task undertaken by computers, but it's pretty rare that the result could be mistaken for ordinary speech. A new technique from researchers at Alphabet's DeepMind takes a completely different approach, producing speech and even music that sounds eerily like the real thing. Early systems used a large library of the parts of speech (phonemes and morphemes) and a large ruleset that described all the ways letters combined to produce those sounds. The pieces were joined, or concatenated, creating functional speech synthesis that can handle most words, albeit with unconvincing cadence and tone. WaveNet, as the system is called, takes things deeper.

The article is about AI research in the context of a complex artistic behavior: expressive music performance. A computer program is presented that learns to play piano with 'expression' and that even won an international computer piano performance contest. A superficial analysis of an expressive performance generated by the system seems to suggest creative musical abilities. After a critical discussion of the processes underlying this behavior, we abandon the question of whether the system is really creative, and turn to the true motivation that drives this research: to use AI methods to investigate and better understand music performance as a human creative behavior. A number of recent and current results from our research are briefly presented that indicate that machines can give us interesting insights into such a complex creative behavior, even if they may not be creative themselves.