Music Genres Classification using KNN System
Music genres classification using KNN system is a project that uses machine learning algorithms to classify music files into different genres. The system can be used in music streaming services or music recommendation systems to recommend music to users based on their preferences.
Here is a high-level overview of the project:
Data Collection: Collect a large dataset of music files in different genres. This can include a wide range of music genres such as rock, pop, jazz, classical, hip-hop, etc.
Data Preprocessing: Clean the data and extract relevant features from the music files. This can include techniques such as feature extraction using Mel-frequency cepstral coefficients (MFCCs).
Feature Selection: Select the most important features that are relevant for music genre classification.
Training Set and Test Set Split: Split the dataset into training and test sets. The training set is used to train the K-Nearest Neighbor (KNN) algorithm, while the test set is used to evaluate the performance of the trained algorithm.
KNN Algorithm: Implement the KNN algorithm, which involves finding the K nearest neighbors of a test data point in the feature space and assigning a class label based on the majority class label of the K nearest neighbors.
Model Evaluation: Evaluate the performance of the KNN model using performance metrics such as accuracy, precision, recall, and F1-score.
Deployment: Deploy the system in music streaming services or music recommendation systems to recommend music to users based on their preferences.
Some possible enhancements to the system could include integrating it with other machine learning algorithms such as neural networks or decision trees to improve the classification performance. Additionally, the system could be expanded to include other music features such as tempo or rhythm to improve the accuracy of the classification.
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