For clustering music with audio data, the data points are the feature vectors from the audio files. If two points are close together, it means that their audio features are similar. We want to discover which audio files belong to the same neighborhood because these clusters will probably be a good way to organize your music files:
Loading audio files with TensorFlow and Python.
Some common input types in ML algorithms are audio and image files. This shouldn't come as a surprise because sound recordings and photographs are raw, redundant, ab nd often noisy representations of semantic concepts. ML is a tool to help handle these complications. These data files have various implementations, for example, an audio file can be an MP3 or WAV.
Reading files from a disk isn't exactly a ML-specific ability. You can use a variety of Python libraries to load files onto the memory, such as Numpy or Scipy. Some developers like to treat the data preprocessing step...