Howtosignalprocessing
- Why is MFCC used in audio classification?
- Is feature scaling required for the KNN algorithm explain with proper justification?
- How can I improve my KNN algorithm?
- Can KNN be used for multi class classification?
Why is MFCC used in audio classification?
It is observed that extracting features from the audio signal and using it as input to the base model will produce much better performance than directly considering raw audio signal as input. MFCC is the widely used technique for extracting the features from the audio signal.
Is feature scaling required for the KNN algorithm explain with proper justification?
Yes, feature scaling is required to get the better performance of the KNN algorithm. For Example, Imagine a dataset having n number of instances and N number of features. There is one feature having values ranging between 0 and 1. Meanwhile, there is also a feature that varies from -999 to 999.
How can I improve my KNN algorithm?
The key to improve the algorithm is to add a preprocessing stage to make the final algorithm run with more efficient data and then improve the effect of classification. The experimental results show that the improved KNN algorithm improves the accuracy and efficiency of classification.
Can KNN be used for multi class classification?
KNN is used for both “binary” and “multi-class classification”- In the machine learning terminology, a classification problem is one where, given a list of discrete values as possible prediction outcomes (known as target classes), the aim of the model is to determine which target class a given data point might belong ...
