Following the success of the transformer architecture in the natural language domain, transformer-like architectures have been widely applied to the domain of symbolic music recently. Symbolic music and text, however, are two different modalities. Symbolic music contains multiple attributes, both absolute attributes (e.g., pitch) and relative attributes (e.g., pitch interval). These relative attributes shape human perception of musical motifs. These important relative attributes, however, are mostly ignored in existing symbolic music modeling methods with the main reason being the lack of a musically-meaningful embedding space where both the absolute and relative embeddings of the symbolic music tokens can be efficiently represented. In this paper, we propose the Fundamental Music Embedding (FME) for symbolic music based on a bias-adjusted sinusoidal encoding within which both the absolute and the relative attributes can be embedded and the fundamental musical properties (e.g., translational invariance) are explicitly preserved. Taking advantage of the proposed FME, we further propose a novel attention mechanism based on the relative index, pitch and onset embeddings (RIPO attention) such that the musical domain knowledge can be fully utilized for symbolic music modeling. Experiment results show that our proposed model: RIPO transformer which utilizes FME and RIPO attention outperforms the state-of-the-art transformers (i.e., music transformer, linear transformer) in a melody completion task. Moreover, using the RIPO transformer in a downstream music generation task, we notice that the notorious degeneration phenomenon no longer exists and the music generated by the RIPO transformer outperforms the music generated by state-of-the-art transformer models in both subjective and objective evaluations.

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Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. As we previously saw, the supervised part of machine learning is separated into two categories, and from those two categories, we have already ventured into the realm of classification and the many algorithms employed in the classification process.

The dots representing locations on this white map on this documentary I watched for a few seconds inspired me this documentary Artificial Fan (DAF) that I am writing about after writing about this yellow sticker that is about JS dependencies switching and swapping. Maybe these two topics are related because all I will be writing about in this story is to some extent also about computational journalism because it's about producing web content and fan engagement experiences and platforms as well as automated conversations in social media and all of these autonomously. The identification of the materials used in a documentary, materials like archives, interviewed people, showcased facts like documents, and the gathering of more information about these materials for the purpose of creating even more content about them and crediting the people and organizations behind these materials is the purpose of this documentary Fan bot that I am about to describe and implement. Then I could say that this documentary fan bot is about producing engaging content autonomously for the fanbase of the underlying film documentary that it covers for the goals of having more reliable sources and materials about this film documentary first streamed on video streaming platforms like Netflix, Discovery, …. This Documentary Fan bot that I am trying to describe in this story is about how one could use a computer to take any produced documentary Like … and produces autonomously rich web content and social media discourses from this film documentary with what we call artificial intelligence and its special custom field that is machine learning.

Growing the business and evolving as the market leader has always been about innovation in functionalities of employee management, customer experience, and others. Companies bring the desired changes in their business operations, services, and support by leveraging machine learning and deep learning solutions. Deep learning and machine learning have become the new face of growth and success in recent years. Companies increasingly use deep learning and machine learning solutions to innovate different aspects of their business. The corporate sector expects revenue of $59.8 billion with AI (Artificial Intelligence) and ML (Machine Learning) by 2025. Machine Learning (ML) and Deep Learning (DL) let the corporates drive informed decisions towards digital transformation without making errors and taking risks.