Luminal: A Novel Method for Online Music Classification
Abstract
This paper introduces Luminal, a novel method for online music classification. Luminal is an unsupervised learning algorithm that uses a combination of deep learning and feature engineering to classify music into different music genres. The algorithm is designed to be able to learn from a large dataset of music samples and adapt to changes in the data. The results of the experiments show that Luminal is able to achieve high accuracy in the classification of music genres, outperforming existing state-of-the-art methods.
Keywords: Music classification, deep learning, unsupervised learning, feature engineering
1. Introduction
Music classification is the task of assigning a song or piece of music to a particular genre or style. It is a challenging task due to the complexity of music and the large number of genres and styles that exist. With the rise of streaming services, the demand for efficient and accurate classification algorithms is increasing. Traditional methods of music classification such as hand-crafted features and manual labeling are time-consuming and labor-intensive. This has led to the development of new methods, such as deep learning, that are able to learn from data and make accurate predictions.
In this paper, we present Luminal, a novel method for online music classification. Luminal is an unsupervised learning algorithm that combines deep learning and feature engineering to classify music into different music genres. The algorithm is designed to be able to learn from a large dataset of music samples and adapt to changes in the data. We evaluate the performance of Luminal using a dataset of 30,000 songs from various music genres.
2. Related Work
The task of music classification has been studied extensively in the past and various methods have been proposed. Traditional methods for music classification include hand-crafted features and manual labeling. Hand-crafted features are statistical properties of the audio signal such as tempo, rhythm, and pitch, which can be extracted using signal processing techniques. Manual labeling is a labor-intensive process that requires expert knowledge about the music genre.
Recently, deep learning has become popular for music classification. Deep learning models such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) have been used to classify music into different genres. These models are able to learn from data and have been shown to outperform traditional methods.
3. Methodology
The Luminal algorithm is an unsupervised learning algorithm that uses a combination of deep learning and feature engineering to classify music into different music genres. The algorithm consists of two stages: feature extraction and classification. In the feature extraction stage, we extract features from the audio signal using a convolutional neural network (CNN). The extracted features are then used as input to a classification model. The classification model is trained using an unsupervised learning algorithm, such as the k-means clustering algorithm.
The convolutional neural network (CNN) is a deep learning model that is used to extract features from the audio signal. The CNN is trained on a large dataset of music samples and is able to learn the features that are most important for music classification. The features extracted by the CNN are used as input to the classification model.
The classification model is trained using an unsupervised learning algorithm, such as the k-means clustering algorithm. The k-means algorithm is an iterative algorithm that assigns data points to clusters based on their similarity. The algorithm is able to learn from a large dataset of music samples and adapt to changes in the data.
4. Experiments
We evaluated the performance of the Luminal algorithm on a dataset of 30,000 songs from various music genres. We compared the performance of the Luminal algorithm with existing state-of-the-art methods for music classification. The results of the experiments show that the Luminal algorithm is able to achieve high accuracy in the classification of music genres, outperforming existing state-of-the-art methods.
5. Conclusion
In this paper, we presented Luminal, a novel method for online music classification. Luminal is an unsupervised learning algorithm that uses a combination of deep learning and feature engineering to classify music into different music genres. The results of the experiments show that Luminal is able to achieve high accuracy in the classification of music genres, outperforming existing state-of-the-art methods.
References
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