Acoustic Feature Identification to Recognize Rag Present in Borgit

In the world of Indian classical music, raga recognition is a crucial undertaking. Due to its particular sound qualities, the traditional wind instrument known as the borgit presents special difficulties for automatic raga recognition. In this research, we investigate the use of auditory feature identification methods to create a reliable raga recognition system for Borgit performances. Each of the Borgits, the devotional song of Assam is enriched with rag and each rag has unique melodious tune. This paper has carried out few experiments on the audio samples of rags and a few Borgits sung with those rugs. In this manuscript three mostly used rags and a few Borgits  with these rags are considered for the experiment. Acoustic features considred here are FFT (Fast Fourier Transform), ZCR (Zero Crossing Rates), Mean and Standard deviation of pitch contour and RMS(Root Mean Square). After evaluation and analysis it is seen that FFT  and ZCR are two noteworthy acoustic features that helps to identify the rag present in Borgits. At last K-means clustering was applied on the FFT and ZCR values of the Borgits and were able to find correct grouping according to rags present there. This research validates FFT and ZCR as most precise acoustic parameters for rag identification in Borgit. Here researchers had observed roles of Standard deviation of pitch contour and RMS values of the audio samples in rag identification. &nbsp

Can Algorithms Promote Fair Use?

In the past few years, advances in big data, machine learning and artificial intelligence have generated many questions in the intellectual property field. One question that has attracted growing attention concerns whether algorithms can be better deployed to promote fair use in copyright law. The debate on the feasibility of developing automated fair use systems is not new; it can be traced back to more than a decade ago. Nevertheless, recent technological advances have invited policymakers and commentators to revisit this earlier debate.As part of the Symposium on Intelligent Entertainment: Algorithmic Generation and Regulation of Creative Works, this Article examines whether algorithms can be better deployed to promote fair use in copyright law. It begins by explaining why policymakers and commentators have remained skeptical about such deployment. The article then builds the case for greater algorithmic deployment to promote fair use. It concludes by identifying areas to which policymakers and commentators should pay greater attention if automated fair use systems are to be developed

Automated Composition of Picture-Synched Music Soundtracks for Movies

We describe the implementation of and early results from a system that automatically composes picture-synched musical soundtracks for videos and movies. We use the phrase "picture-synched" to mean that the structure of the automatically composed music is determined by visual events in the input movie, i.e. the final music is synchronised to visual events and features such as cut transitions or within-shot key-frame events. Our system combines automated video analysis and computer-generated music-composition techniques to create unique soundtracks in response to the video input, and can be thought of as an initial step in creating a computerised replacement for a human composer writing music to fit the picture-locked edit of a movie. Working only from the video information in the movie, key features are extracted from the input video, using video analysis techniques, which are then fed into a machine-learning-based music generation tool, to compose a piece of music from scratch. The resulting soundtrack is tied to video features, such as scene transition markers and scene-level energy values, and is unique to the input video. Although the system we describe here is only a preliminary proof-of-concept, user evaluations of the output of the system have been positive.Comment: To be presented at the 16th ACM SIGGRAPH European Conference on Visual Media Production. London, England: 17th-18th December 2019. 10 pages, 9 figure

The Challenge of Artificial Intelligence

Artificial Intelligence (AI) appears to be advancing at an ever-accelerating pace and affecting much of human life. The power of AI has already been demonstrated in various areas – from smartphone personal assistants and customer support chatbots to medical diagnoses and driverless cars. At the same time, these applications bring multiple challenges and much hyperbole. Nonetheless, of particular importance here, AI systems have also entered the classroom. However, while promising to enhance education, the design and deployment of these tools again raise particular concerns and challenges. We begin this chapter with a brief history and definition of AI outlining the evolution of AI techniques aiming to imitate or outperform human cognitive capacities. We continue by exploring what AI systems promise to deliver in educational contexts and their impact on learners, examining the interaction through the lens of three analytical categories: learning with AI, learning about AI and preparing for AI. We also explore the risks related to the introduction of AI into education and investigate transversal issues related to all three categories, noting that currently little attention has been paid to what is ethically acceptable for AI and education. Finally, we conclude by trying to answer two questions: how can we make better AI tools for education and how can education help address the challenges created by AI

Perceptions and Realities of Text-to-Image Generation

Generative artificial intelligence (AI) is a widely popular technology that will have a profound impact on society and individuals. Less than a decade ago, it was thought that creative work would be among the last to be automated - yet today, we see AI encroaching on many creative domains. In this paper, we present the findings of a survey study on people's perceptions of text-to-image generation. We touch on participants' technical understanding of the emerging technology, their fears and concerns, and thoughts about risks and dangers of text-to-image generation to the individual and society. We find that while participants were aware of the risks and dangers associated with the technology, only few participants considered the technology to be a personal risk. The risks for others were more easy to recognize for participants. Artists were particularly seen at risk. Interestingly, participants who had tried the technology rated its future importance lower than those who had not tried it. This result shows that many people are still oblivious of the potential personal risks of generative artificial intelligence and the impending societal changes associated with this technology.Comment: ACM Academic Mindtrek 202

Can Algorithms Promote Fair Use?

In the past few years, advances in big data, machine learning and artificial intelligence have generated many questions in the intellectual property field. One question that has attracted growing attention concerns whether algorithms can be better deployed to promote fair use in copyright law. The debate on the feasibility of developing automated fair use systems is not new; it can be traced back to more than a decade ago. Nevertheless, recent technological advances have invited policymakers and commentators to revisit this earlier debate.As part of the Symposium on Intelligent Entertainment: Algorithmic Generation and Regulation of Creative Works, this Article examines whether algorithms can be better deployed to promote fair use in copyright law. It begins by explaining why policymakers and commentators have remained skeptical about such deployment. The article then builds the case for greater algorithmic deployment to promote fair use. It concludes by identifying areas to which policymakers and commentators should pay greater attention if automated fair use systems are to be developed