Computer accompaniment (think electronic pianos and soundboards) primarily uses an algorithmic approach to process and output music. This involves using signal processing techniques to transform musical pieces into a sequence of symbols, and then estimating a pitch. In practice, this approach works really well in replicating acoustic music and providing accurate accompaniment. However, it also highlights the inherent simplicity of machine perception of music. While computers excel at executing predefined algorithms and processing precise musical features, their understanding of the complexities of music remains limited. They primarily rely on mathematical representations, which can capture certain aspects of music but may fall short of grasping the full depth of human musical perception.

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          The simplicity of machine classification is demonstrated in a (somewhat outdated) study done on machine attempts at style classification. In 1999, a team of students and professors at Carnegie Mellon University attempted to use advanced machine learning algorithms to recognize music classifications, such as “frantic” or “slow” music. By decomposing musical compositions into low-level features such as note counts, pitch, duration, and more, the researchers were able to train classifiers to yield around a 98% success rate in correctly identifying music styles based on four simple styles. At first glance the project may seem to demonstrate the proficiency of machine music recognition - but we must also consider some of the limitations realized by this experiment. The four styles recognized by the machine  were very simple and consistent throughout - meaning  the computer was unable to recognize tonal shifts, something humans can do with near-perfect certainty.  Additionally, styles of music are one of the more simple concepts in music categorization - how would a computer approach classifying higher-level concepts such as music genres? Despite the historical limitations of computer classification of abstract music concepts, modern techniques have produced far better results which show how much potential the field holds. In a study  published in the International Research Journal of Engineering and Technology, researchers used signal- processing techniques to model sound patterns in a 2d spectrogram. They then used a convolutional  neural network, a model widely used for image processing, to classify these representations of sound as an image. Using this method, the model achieved a high accuracy rate of 88.54%, beating out other feature-based models such as logistical regression models and simple neural networks by over 20%.