Abstract. This paper introduces ALYSIA: Automated LYrical SongwrIting Application. ALYSIA is based on a machine learning model using Random Forests, and we discuss its success at pitch and rhythm prediction. Next, we show how ALYSIA was used to create original pop songs that were subsequently recorded and produced. Finally, we discuss our vision for the future of Automated Songwriting for both co-creative and autonomous systems.
This paper presents the results of a novel digital signal processor (DSP) based technique for signal conditioning of Linear Variable Dgferential Transformers (L VDTs).Signal conditioning is achieved through a modified DSP-based Costas receiver. This system is tested and compared with two commercially available analog signal conditioners and a second DSP-based signal conditioner. The system developed by the authors has better dynamic response than existing solutions and better noise rejection than commercially available solutions. Static testing of the system using both 4-wire and 5-wire LVDTs verifies that the conditioner meets or exceeds the linearity performance of existing signal conditioning systems over the full-scale operating range. In addition, this system requires no phase compensation network or manual tuning.
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