essay sample on application of technologies in music

Introduction

In the music industry nowadays, there has been a broad application of technologies which have made work more manageable in the long run. In this sense, there has been a variety of musical listening platforms, including the use of online systems by the majority of listeners. They listen to various music from diverse artists across the world, facilitated by their subscriptions to use them which comes with their conditions of service in the long run. Different listeners take to different music as per their preferences, which thus brings about a semblance of variety in terms of consumer preference in musical inclinations. There, therefore, the songs which are being widely listened to by a majority of the listeners, in their respective order to the ones which are least preferred by the consumers, such as the ones listening to them from the online platforms.

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Moreover, their different groups of people prefer certain music which might have favorable features as per their choices. The challenge, as a result of variety, comes where individual listeners of music are to be recommended specific music. It is because there is a need for accuracy that the targeted individuals will prefer the recommended music at their bases.

Hopefully, there has come the technology which gathers vast amounts of information regarding music listening in the domains of consumers. It is termed as machine learning, utilizing data mining to make the necessary options about making some recommendations of music to the consumers. R studio is used in data processing (preparation and analysis). In this case, there are significant data sets, consisting of about five hundred data points for each consumer, and concerning the music listening by the consumer (Maymala, 2015). In this case, the history of music listening for a diverse group of individuals is recorded, over a wide range of time. It will enable the availability of individual data on the habit of music listening by consumers. The result is that through the processes of data mining enables the making of decisions regarding the music hearing patterns by the individual listeners. There shall, then, be the making of recommendations to the listeners o the music with the high probability that accuracy is observed as the recommended music is in line with the preference of the listener. Also, using the mined data to establish the trend of musical preferences of the listeners, there shall then be the making of predictions on their livelihood to listen to certain music in future, which can thus be used in the recommendation of songs to the users for future periods. Therefore, data mining techniques can be employed in making highly accurate recommendations of songs to the listeners as well as predicting the songs they are likely to hear in future periods.

In the paper is a report on the application of different machine learning techniques of data mining to aid in the accurate recommendation of music to the listeners, as well as predicting the songs which they are likely to hear quite accurately. Subsequently, there shall be the undertaking of the performance evaluation of the data mining machine techniques employed, followed by the comparison of their results which will aid in getting the method that has the highest probability in its predictions. There shall be the use of both the training and test data sets. The training data sets shall be fed into the algorithms during data mining, while the test data shall be employed in determining the reliability, accuracy, as well as the efficiency of the selected technique used in recommending and predicting the songs for the listeners as presented in the available data sets. Following the comparison of the methods, the one which is the most reliable, the most accurate, as well as producing the most accurate results is selected and recommended for use. In the paper, the focus is made on the application of various machine learning algorithms in the mining of a Million Song Dataset, following their comparison basing on their levels of accuracy and the selection of the most accurate and reliable technique for data mining.

Literature Review

Reasons for the Use of Data Mining

In this case, there is the significance for the employment of the data mining process in the task at hand. The data mining process from a million-song data set provided will be of great importance in the delegations and attainment of the task objective following the resolution of the problem. The data to be mined is comprised of several songs from various artists who have recorded their work. Also, such songs have been recorded in different periods. The data set also contains the records of the music listeners, where about five thousand data points from them have been registered about their association with different music, as observed in the (Inmon, & Lindstedt, 2015). The recorded information in the form of a million-song data set over a more extended period will thus facilitate the handling of data using the selected machine learning algorithms in such a way that there shall be effective applicability to the problem at hand, involving the use of the algorithm chosen in the music recommendation system quite expertly.

The million-song data set can be used to understand the trends of song listening and subscription by individual listeners across the country. It is because of the recorded past period's song subscription of the listeners, which will then be used in the establishment of a reliable trend that will be used for future prediction of the music that they are likely to subscribe to and listen. Such information is only available in the million-song data set, composed of massive amounts of information. It thus calls for the use of data mining techniques to extract only the useful information which can be customized to the music recommendation systems and be able to work quite proficiently. Not all the information in the million-song data set will be employed in the prediction process used for a music recommendation system, thus facilitating the use of data mining to extract only the required information.

According to (In Karandinou, 2019), different machine learning algorithms used in data mining are employed. It is because they have been composed in such a way that they facilitate knowledge discovery. In this case, the availability of data set on various songs and the parameters on the music listening patterns by listeners is not enough for their use in the music recommendation systems here the listeners are to recommended with particular songs based on their listening patterns. Thus, the application of data mining algorithms will enable the sifting of the data to set information as well as extracting knowledge on them, for their effective application in the music recommendations to the listeners countrywide (Maymala, 2015). In this regard, if the used machine learning algorithms well predict the song listening on the side of the listeners such that the predictions are accurate, reliable, prompt, affordable for applicability in the music recommendation systems, as well as of high quality, then they can be proficient when applied, showing the availability of the required information in a million song data set by which data mining is done.

The Source of the Mined Data

The data set to be used in the task at hand has been extracted from the Colombian million songs data set, from the Colombian million songs database. It contains all the songs, their respective artists, the number of listeners subscribing to them in a defined period, their rankings in terms of popularity and regions of being heard, volume, and categorizations. They have been extracted from the website http://labrosa.ee.columbia.edu/millionsong/pages/getting-dataset. Open Science Data Cloud has mirrored it under the following link address; https://www.opensciencedatacloud.org/publicdata/million-song-dataset/. The data set has then been subcategorized into the training and test data sets applied in the selected algorithms (Azarmi, 2016). It contains one million songs and their characteristics, such as the number of listeners for each one of them over a given period. The data set is about three hundred gigabytes in quantity and can be challenging to provide all of them in the paper at hand due to the need for ample space. The link to its location has thus been provided above.

The Objective of the Task

Based on the availed information in the introduction regarding the need for the use of data mining techniques to filter the information for application in the field of interest, the goal of the paper is to use the mined information determine the machine learning technique that can best be sued in the prediction of the music recommendation system. In this case, there is the use of the available data set, with the information on the listening habits of the music listeners, recorded over a more significant period. The information will then be filtered with the use of different machine learning techniques, where their outcomes are compared hence facilitating the selection of the best machine learning algorithm which can be applied in the music recommendation system to the listeners. Besides, the selected machine learning algorithm is selected based on its ability to best predict the accurate nature of the music that the listeners will be likely to listen, which can thus be applied in the music recommendation systems to reap greater success in the music recommendation industry. There is the use of five different machine learning algorithms, including the Logistics regression, Linear regression, classification and regression trees, and Naive Bayes Algorith (Maymala, 2015).

The selected information represented in the data set is to be mined for music recommendation system. In this regard, there shall be the use of two classified subsets of data, which will facilitate the testing of the algorithm for their accuracy and reliability in such a prediction. The data subsets to be used are the training data sets and the Test data sets. The training data sets will be applied in the algorithm functionality as it is being put to use, whereas the test algorithm will be used in checking the accuracy of the algorithm in predicting the likely music to be recommended to the listeners. In the end, there shall be a comparison of the applied machine learning algorithms, followed by pinpointing the one that can best be used in the prediction purposes for music recommendation systems to effectively function.

Summary of the Million Song Data Set Presented in the Link

From the million-song data set where the relevant information is to be mined and fed into the selected machine learning algorithms for analysis, there are some aspects of the information which can be exposed and summarized to facilitate effective analysis using the chosen machine learning techniques. One of them is that the data is multivariate. It combines different aspects surrounding the listen g of various music by different listeners across the country. They include the volume of the songs, the dates and years recorded, the artists recording them, the periodical number of listeners subscribing to them, and their ranking in terms of popularity alongside regional preference of the songs.

Additionally, the songs have been variously categorized into their groupings. The listeners, apart from their numbers being recorded, have their demographic characteristics presented, such as sex, age, and their geographical locations. However, at some points, there are some missing values in the data sets, which have been denoted as N/A. The number of instances in the data set is 729, showing the extent of information contained in the data set, which is hugely significant. For the enhancement of a better understanding of the data sets used in the task, the followi...