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A Momentous growth in the development of large-scale database and network technology have made internet users have access to a staggering amount of multimedia data in the form of text, audio and video worldwide. Focusing on audio data alone, thousands of music profiles, lectures and speeches are transferred within fractions of a second over the internet. This is particularly true in the case of songs. Music choices vary from person to person, even within the same geographical culture. Thus it becomes imperative to search, retrieve and organize music content in a systematic manner.
From a particular choice of artist to the combination of instruments used, songs deliver a recurring pattern of characteristic. Depending on these characteristics it is possible to classify them in different ways.
The common way of classifying songs in radio stations, music stores, over the Internet and just about everywhere else, is based on Musical genres. A music genre elucidates the style of music that has recognizable features shared by its members and can be differentiated from other music types.
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Some such characteristics are related to the instrumentation used, harmony, rhythm maintained, and melody of the music. As of now, Napster and Apple’s iTunes classify genres of songs manually with the help of a listener. However, manual classification consumes time, man-power, additionally is made difficult when songs are in an unknown language to the listener. Hence with the help of machine learning, classifying songs automatically into appropriate genres as-opposed-to manual processes will save resources. Our goal is to develop an automatic genre classification technique using data mining techniques to have reliability, efficiency and accuracy in segregating genres, and can be used in radio stations, media production house, etc.
for a mass sorting of music content. Data mining is the method of sorting through colossal sets of data to establish relationships and identify patterns. The information gathered from this process can then be used to classify our samples.
In other terms, data mining is the process of realizing correlations among several fields in large databases. A branch of artificial intelligence, Machine learning works with the construction and study of systems that can learn from data. Machine Learning uses data mining tools to learn from and make predictions on the given data. Generally, the genre classification process of music has three main steps: Pre-processing, feature extraction and classification. Linear Predictive Coefficient (LPC), Mel Frequency Cepstral Coefficient (MFCC), etc are some of the commonly extracted features. These features can then be classified using supervised or unsupervised machine learning methods.
Music genre classification data mining. (2024, Feb 21). Retrieved from https://studymoose.com/music-genre-classification-data-mining-essay
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